text:
\section{Introduction}

Pre-trained language models (PrLM), including ELECTRA \cite{clark2020electra}, RoBERTa\cite{liu2019roberta}, and BERT \cite{devlin:bert}, have demonstrated strong performance in downstream tasks \cite{wang2018glue}. Leveraging a self-supervised training on large text corpora, these models are able to provide contextualized representations in a more efficient way. For instance, BERT uses Masked Language Modeling and Nest Sentence Prediction as pre-training objects and is trained on a corpus of 3.3 billion words.

In order to be adaptive for a wider range of applications, PrLMs usually generate sub-token-level representations (words or subwords) as basic linguistic units. For downstream tasks such as natural language understanding (NLU), span-level representations, e.g. phrases and name entities, are also important. Previous works manifest that by changing pre-training objectives, PrLMs' ability to capture span-level information can be strengthened to some extent. For example, base on BERT, SpanBERT \cite{joshi2019spanbert} focuses on masking and predicting text spans, instead of sub-token-level information for pre-training. Entity-level masking is used as a pre-training strategy by ERNIE models \cite{sun2019ernie,zhang2019ernie}. The upper mentioned methods prove that the introduction of span-level information in pre-training to be effective for different NLU tasks.

However, the requirements for span-level information of various NLU tasks differs a lot from case to case. The methods of introducing span-level information in pre-training phase, proposed by previous works, do not fit into the requirements and cannot improve the performance for all NLU tasks. For instance, ERNIE models \cite{sun2019ernie} perform remarkably well in Relation Classification, while underperforms BERT in language inference tasks, such as MNLI \cite{nangia2017repeval}. Therefore, it is imperative to develop a strategy to incorporate span-level information into PrLMs in a more flexible and universally adaptive way. This paper proposes a novel approach, Span Fine-tuning (SF), to leverage span-level information in fine-tuning phase and therefore formulate a task-specific strategy. Compared to existing works, our approach requires less time and computing resources, and is more adaptive to various NLU tasks.

In order to maximize the value and contribution of span-level information, in additional to the sub-token-level representation generated by BERT, Span Fine-tuning also applies a computationally motivated segmentation to further improve the overall experience. Although various techniques, such as dependency parsing \cite{zhou2019limit} or semantic role labeling (SRL) \cite{zhang2019semantics}, have been used as auxiliary tools for sentence segmentation, these methods demand extra parsing procedure, which increase complexities in actual practice. Span Fine-tuning first leverages a pre-sampled $n$-gram dictionary to segment input sentences into spans. Then, the sub-token-level representations within the same span are combined to generate a span-level representation. Finally, the span-level representations are merged with sub-token-representations into a sentence-level representation. In this way, the sentence-level representation is able to contain and maximize the utilization of both sub-token-level and span-level information.

Experiments are conducted on the GLUE benchmark \cite{wang2018glue}, which includes many NLU tasks, such as text classification, semantic similarity, and natural language inference. Empirical results demonstrate that Span Fine-tuning is able to further improve the performance of different PrLMs, including BERT \cite{devlin:bert}, RoBERTa \cite{liu2019roberta} and SpanBERT \cite{joshi2019spanbert}. The result of the experiments with SpanBERT indicates that Span Fine-tuning leverages span-level information differently compared to PrLMs pre-trained with span-level information, which shows the distinguishness of our approach. It is also verified in ablation studies and analysis that Span Fine-tuning is essential for further performance improvement for PrLMs.

\section{Related Work}
\subsection{Pre-trained language models}
Learning reliable and broadly applicable word representations has been an ongoing heated focus for natural language processing community. Language modeling objectives are proved to be effective for distributed representation generation \cite{mnih2009scalable}. By generating deep contextualized word representations, ELMo \cite{Peters2018ELMO} advances state of the art for several NLU tasks. Leveraging Transformer \cite{vaswani2017attention}, BERT \cite{devlin:bert} further advances the field of transfer learning. Recent PrLMs are established based on the various extensions of BERT, including using GAN-style architecture \cite{clark2020electra}, applying a parameter sharing strategy \cite{lan2019albert}, and increasing the efficiency of parameters \cite{liu2019roberta}.
\begin{figure*}[t]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/LaCo.png}
	\caption{\label{fig:framework} Overview of the  framework of our proposed method}
\end{figure*}
\subsection{Span-level pre-training methods}
Previous works manifest that the introduction of span-level information in pre-training phase can improve PrLMs' performance. In the first place, BERT leverages the prediction of single masked tokens as one of the pre-training objectives. Due to the use of WordPiece embeddings \cite{wu2016google}, BERT is able to segment sentences into sub-word level tokens, so that the masked tokens are at sub-token-level, e.g. "\#\#ing". \cite{devlin:bert} shows that masking the whole word, rather than only single tokens, can further enhance the performance of BERT. Later, it is proved by \cite{sun2019ernie,zhang2019ernie} that the masking of entities is also helpful for PrLMs. By randomly masking adjoining spans in pre-training, SpanBERT \cite{joshi2019spanbert} can generate better representation for given texts. AMBERT \cite{zhang2020ambert} achieves better performance than its precursors in NLU tasks by incorporating both sub-token-level and span-level tokenization in pre-training. The upper mentioned studies all focus on introducing span-level information in pre-training. To the best of our knowledge, the introduction of span-level information in fine-tuning is still a white space to explore, which makes our approach a valuable attempt.

\subsection{Integration of fine-grained representation}

Different formats of downstream tasks require sentence-level representations, such as natural language inference \citep{Bowman2015A,nangia2017repeval}, semantic textual similarity \citep{cer2017semeval} and sentiment classification \citep{socher2013recursive}. Besides directly pre-training the representation of coarser granularity \citep{le2014distributed,logeswaran2018efficient}, a lot of methods have been explored to obtain a task-specific sentence-level representation by integrating  fine-grained token-level representations\citep{conneau2017supervised}. \citet{kim2014convolutional} shows that by applying a convolutional neural network (CNN) on top of pre-trained word vectors, we can get a sentence-level representation that is well adapted to classification tasks.  \citet{lin2017structured} leverage a self-attentive module over hidden states of a BiLSTM to generate sentence-level representations. \citet{zhang2019semantics} use a CNN layer to extract word-level representations form sub-word representations and combine them with word-level semantic role representations. Inspired by these methods, after a series of preliminary attempts, we choose a hierarchical CNN architecture to recombine fine-grained representations to coarse-grained ones.

\section{Methodology }

Figure \ref{fig:framework} demonstrates the overall framework of Span Fine-tuning, which is essentially uses BERT as a foundation and incorporates segmentation as an auxiliary tool. The figure does not exhaustively depict the details of BERT, given the model is relatively popular and ubiquitous. Further information on BERT is available in \cite{devlin:bert}. In Span Fine-tuning, an input sentence is divided into sub-word-level tokens and then sent to BERT to generate sub-token-level representations. At the same time, the input is segmented into spans based on $n$-gram statistics. By combining the segmentation information with sub-token-level representations generated by BERT, we divided the representation into several spans. Then, the spans are sent through a hierarchical CNN module to obtain a span-level information enhanced representation. Finally, the sub-token-level representation of \texttt{[CLS]} token generated by BERT and the span-level information enhanced representation are concatenated and form a final representation, which maximized the value of both sub-token-level and span-level information for NLU tasks.
\begin{figure*}[t]
	\centering
	\includegraphics[width=0.95\textwidth]{figures/segmentation.pdf}
	\caption{\label{fig:segmentation} Segmentation Examples }
\end{figure*}
\subsection{Sentence Segmentation}
Semantic role labeling (SRL) \cite{zhang2019semantics} and dependency parsing \cite{zhou2019limit} have been used as auxiliary tools for segmentation by previous works. Nonetheless, these techniques demand additional parsing procedures, and therefore increase complexities for real application. In order to obtain a simpler and more convenient segmentation, base on frequency, we select meaningful $n$-grams appeared in wikitext-103 dataset\footnote{PMI method has also been tried to adjust our dictionary, but the result is not competitive.} to form a pre-sampled dictionary.

We use the dictionary to match $n$-grams from the head of each input sentence. $n$-grams with greater lengths are prioritized, while unmatched tokens remain the same. In this way, we are able to obtain a specific segmentation of the input sentence. Figure \ref{fig:segmentation} demonstrates some examples of sentence segmentation from the GLUE dataset.

\subsection{Sentence Encoder Architecture}
An input sentence $X=\{x_1, \dots, x_n\}$ is given with a length $n$. The sentence is firstly divided into sub-word tokens (with a special token \texttt{[CLS]} at the beginning) and converted to sub-token-level representations $E=\{e_1, \dots, e_m\}$ (usually $m$ is larger than $n$) according to embeddings proposed by \cite{wu2016google}. Then, the transformer encoder (BERT) captures the contextual information for each token by self-attention and generates a sequence of sub-token-level contextual embeddings $T=\{t_1, \dots, t_m\}$, in which $t_1$ is the contextual representation of special token \texttt{[CLS]}. Based on the segmentation generated by the $n$-gram statistics, the sub-token-level contextual representations are combined into several spans $\{C_1, \dots, C_r\}$, with $r$ as a hyperparameter indicating the max number of spans for all processed sentences. Each $C_i$ contains several contextual sub-token-level representations extracted from $T$ dedoted as $\{t^i_1, t^i_2, ..., t^i_l\}$. $l$ is another hyperparameter representing the max number of tokens for all the spans. A CNN-Maxpooling module is applied to each $C_i$ to get a span-level representation $c_i$:
\begin{equation}
\begin{split}
c{^i_j} &= ReLU(W_1\left[t^i_j, t^i_{j+1}, \dots,t^i_{j+k-1}\right] + b_1),\\
c_i &= MaxPooling({c^i_1, \dots,c^i_r}),
\end{split}
\end{equation}
where $W_1$ and $b_1$ are trainable parameters and $k$ is the kernel size. Based on the span-level representations $\{c_1, \dots, c_r\}$, another CNN-Maxpooling module is applied to obtain a sentence-level representation $s$ with enhanced span-level information:
\begin{equation}
\begin{split}
s{'_i} &= ReLU(W_2\left[c_i, c_{i+1}, \dots,c_{i+k-1}\right] + b_2),\\
s &= MaxPooling({s{'_1}, \dots,s{'_r}}),
\end{split}
\end{equation}
Finally, we concatenate $s$ with the contextual sub-token-level representation $t_1$ of special token \texttt{[CLS]} provided by BERT, and generate a sentence-level representation $s^*$ that maximizes the value of both sub-token-level and span-level information for NLU tasks:
$s^* = s \diamond t_1$.

\begin{table*}
	\centering
	\setlength{\tabcolsep}{2pt}
	{
		\begin{tabular}{lcccccccccc}
			\hline

			\hline

			Method& CoLA & SST-2 & MNLI & QNLI & RTE  & MRPC  & QQP & STS-B & Avg & Gain\\
			&  (mc) & (acc)	& m/mm(acc) & (acc) & (acc)  & (F1) & (F1) & (pc) & - & -\\
			\hline
			\multicolumn{10}{c}{\emph{In literature}} \\
			BERT$_\text{BASE}$  & 52.1 & 93.5  & 84.6/83.4 &  - & 66.4 & 88.9 & 71.2  & 87.1 & 78.3\\
			BERT$_\text{LARGE}$ &60.5 & \textbf{94.9} &  86.7/85.9 & 92.7 & 70.1  & 89.3 & 72.1 & 87.6 & 80.5\\
			\hdashline
			BERT-1seq\footnotemark[3]& 63.5 & 94.8 & 88.0/87.4 & 93.0 & 72.1 & 91.2 & 72.1 & 89.0 &  83.5 &\multirow{2}{*}{1.0}\\
		    SpanBERT & \textbf{64.3} & 94.8 & \textbf{88.1}/\textbf{87.7} & \textbf{94.3} & 79.0 & 90.9 & 71.9 & \textbf{89.9} &  \textbf{84.5}\\
			\hline
			\multicolumn{10}{c}{\emph{Our implementation}} \\
			BERT$_\text{BASE}$ & 51.4 & 92.1 & 84.4/83.5 & 90.3 & 67.1 & 88.3 & 71.3 & 85.1 &  79.3 & \multirow{2}{*}{\textbf{1.1}}\\
			BERT$_\text{BASE}$ + SF & 55.1 & 93.6 & 84.8/84.3 & 90.6 & 69.6 & 88.7 & 71.9 & 86.5 &  80.4\\
			\hdashline
			BERT$_\text{WWM}$   & 61.1 &93.6 &87.1/86.5 & 93.9 & 77.3  & 90.0 & 71.9 & 88.1 &  83.3 & \multirow{2}{*}{\textbf{1.1}}\\
			BERT$_\text{WWM}$ + SF   & 62.9 &94.1 &87.6/87.0 & \textbf{94.3} & \textbf{81.4}  & \textbf{91.1} & \textbf{72.4} & 89.1 &  84.4\\

			\hline

			\hline
		\end{tabular}
	}

	\caption{\label{tab:glue} Test sets performance on GLUE benchmark. All the results are obtained from \cite{liu2019multi}, \cite{radford2018improving}. For a simple demonstration, problematic WNLI set are excluded, and we do not show the accuracy of the datasets have F1 scores. \emph{mc} and \emph{pc} denote the Matthews correlation and Pearson correlation respectively.
	}

\end{table*}

\subsection{Tasks and Datasets}
  To evaluate Span Fine-tuning, experiments are conducted on nine NLU benchmark datasets, including text classification, natural language inference, semantic similarity. Eight of which are available from the GLUE benchmark \cite{wang2018glue}. And the rest one is SNLI \cite{Bowman2015A}, a widely accepted natural language inference dataset.

\subsection{Pre-trained Language Model}
We leverage the PyTorch implementation of BERT \cite{devlin:bert}, RoBERTa \cite{liu2019roberta} and SpanBERT \cite{joshi2019spanbert} based on HuggingFace's codebase\footnote{\url{https://github.com/huggingface}} \cite{Wolf2019HuggingFacesTS}  as our PrLMs and baselines.

\section{Experiments}
\subsection{Set Up}
We select all the $n$-grams with $n \le 5$, which occurs more than ten times in the wikitext-103 dataset, to form a dictionary. The pre-sampled dictionary, containing more than 400 thousand $n$-grams, is used to segment input sentences. During segmentation, two hyperparameters are involved: $r$ representing the largest number of spans in a sentence, and $l$ indicating the largest number of tokens included in a span. In order to maintain different dimensions of features for each input sentence, padding and tail are employed. We set $r$ equals to 16, and based on NLU tasks, choose $l$ in \{64,128\} .

The fine-tuning procedure is as the same as BERT's. Adam is used as the optimizer. The initial learning rate is in \{1e-5,2e-5, 3e-5\}, the warm-up rate is 0.1, and the L2 weight decay is 0.01. The batch size is set in \{16, 32, 48\}. The maximum number of epochs is set in \{2,3,4,5\} based on NLU tasks. Input sentences are divided into subtokens and converted to WordPiece embeddings, with a maximum length in \{128, 256\}. The output size of the CNN layer is the same as the hidden size of PrLM, and the kernel size is set to 3.

\subsection{Results with BERT as PrLM}
Two released BERT \cite{devlin:bert}, BERT Large Whole Word Masking and BERT Base, are first used as pre-trained encoder and baselines for Span Fine-tuning. Compared with BERT Large, BERT Large Whole Word Masking reach a better performance, since it uses whole-word masking in pre-training phase. Therefore, we select BERT Large Whole Word Masking as a stronger baseline. The results indicate that Span Fine-tuning can maximize the contribution of span-level information, even when compared to a stronger baseline.

Table \ref{tab:glue} exhibits the results on the GLUE datasets, showing that Span Fine-tuning can significantly improve the performance of PrLMs. Since our approach leverages BERT as a foundation, and undergoes the the same evaluation procedure, it is evident that the performance gain is fully contributed by the newly introduced Span Fine-tuning.

In order to test the statistical significance of the results, we follow the procedure of  \cite{zhang2020retrospective}. We use the McNemars test, this test is designed for paired nominal observations, and it is appropriate for binary classification tasks.The p-value is defined as the probability of obtaining a result equal to or more extreme than what was observed under the null hypothesis. The smaller the p-value, the higher the significance. A commonly used level of reliability of the result is 95\%, written as p = 0.05.  As shown in table \ref{tab:pvalue}, compared with the baseline, for all the binary classification tasks of GLUE benchmark, our method pass the significance test.

\begin{table}[htb!]
	\centering\small
	\setlength{\tabcolsep}{3pt}
	{
		\begin{tabular}{lcccccc}
			\toprule
			& CoLA & SST-2 & QNLI& RTE& MRPC&QQP\\
			\midrule
			p-value & 0.005 & 0.012 & 0.023 & 0.009& 0.008&0.031\\
			\bottomrule
		\end{tabular}
	}

	\caption{\label{tab:pvalue} Results of McNemars tests for binary classification tasks of GLUE benchmark, tests are conducted based on the results of best run of BERT$_\text{BASE}$ and BERT$_\text{BASE}$ + SF.
	}

\end{table}

\footnotetext[3]{The baseline of SpanBert, a BERT pre-trained without next sentence prediction object.}

Span Fine-tuning can reach the same performance improvement as previous methods. As illustrated in Table \ref{tab:glue}, on average, SpanBERT can improve the result by one percentage point over the baseline (BERT-1seq), while Span Fine-tuning is able to achieve an improvement of 1.1 percentage points over our baseline. However, as showed in Table \ref{tab:compare}, Span Fine-tuning requires considerably less time and computing resources compared to the large-scale pre-training for span-level information incorporation. When the Span Fine-tuning is adopted, the extra parameters are only 3 percent of the total parameters of the adopted PrLMs for every downstream task, and introduce little extra overhead.

\begin{table}[htb!]
\centering
\begin{tabular}{lcc}
\toprule
Method  & Time & Resource\\
\midrule
Pre-train  & 32 days& 32 Volta V100  \\
Span Fine-tune & 12 hours max & 2 Titan RTX\\

\bottomrule
\end{tabular}
\caption{\label{tab:ablation} The comparison between incorporation of span-level information in pre-training and Span Fine-tuning .}
\label{tab:compare}
\end{table}

\begin{table*}
	\centering
	\setlength{\tabcolsep}{5pt}
	{
		\begin{tabular}{lccccccccc}
			\toprule

			Method& CoLA & SST-2 & MNLI & QNLI & RTE  & MRPC  & QQP & STS-B & Avg.\\
			&  (mc) & (acc)	& m/mm(acc) & (acc) & (acc)  & (F1) & (acc) & (pc) & -\\
			\midrule
			SpanBERT$_\text{LARGE}$ & 64.3 & 94.8 & 88.1/87.7 & 94.3 & 79.0 & 90.9 & 89.5 & 89.9 & 86.5\\
			SpanBERT$_\text{LARGE}$ + SF & 65.9 & 95.1 & 88.4/88.1 & 94.3 & 83.3 & 92.1 & 90.9 & 90.1 & 87.6\\
			RoBERTa$_\text{LARGE}$ & 68.0 & \textbf{96.4} & 90.2/\textbf{90.2} & \textbf{94.7} & 86.6 & 90.9 & \textbf{92.2} & \textbf{92.4} & 89.0\\
			RoBERTa$_\text{LARGE}$ + SF  & \textbf{68.9} &96.1 &\textbf{90.3}/\textbf{90.2} & 94.3 & \textbf{90.6}  & \textbf{92.8} & \textbf{92.2} & \textbf{92.4} &  \textbf{89.8}\\
			\bottomrule
		\end{tabular}
	}

	\caption{\label{tab:strong} Results on test sets of GLUE benchmark with stronger baseline, we average results from three different random seeds.
	}

\end{table*}

Besides, Span Fine-tuning is more flexible and adaptive compared to previous methods. Table \ref{tab:glue} shows that Span Fine-tuning is able to achieve stronger results on all NLU tasks compared to the baseline, whereas the results of SpanBERT in certain tasks, such as Quora Question Pairs and Microsoft Research Paraphrase Corpus, are worse than its baseline. Since for spanBERT, the utilization of span-level information is fixed for every downstream task. Whereas in our method, an extra module designed to incorporate span-level information is trained during the fine-tuning, which can be more dynamically adapted to different downstream tasks.

\begin{table}[htb!]
\centering
\begin{tabular}{lrr}
\toprule
Method  & Dev & Test \\
\midrule
BERT$_\text{WWM}$   & 92.0& 91.4      \\
BERT$_\text{WWM}$ + SF&92.3& 91.7\\
SemBERT$_\text{WWM}$&92.2& 91.9\\
\bottomrule
\end{tabular}
\caption{\label{tab:snli}  Accuracy on dev and test sets of SNLI. SemBERT$_\text{WWM}$ \cite{zhang2019semantics} is the published SoTA on SNLI.}
\label{tab:booktabs}
\end{table}

Table \ref{tab:snli} indicates that Span Fine-tuning also enhances the result of PrMLs on the SNLI benchmark.
The improvement achieved by Span Fine-tuning is similar to published state-of-the-art accomplished by SemBERT. However, compared to SemBERT, Span Fine-tuning saves a lot more time and computing resources. Span Fine-tuning merely leverages a pre-sampled dictionary to facilitate segmentation, whereas SemBERT leverages a pre-trained semantic role labeller, which brings extra complexities to the whole segmentation process.

Furthermore, Span Fine-tuning is different from SemBERT in terms of motivation, method and contribution factors. The motivation of SemBERT is to enhance PrLMs by incorporating explicit contextual semantics, whereas the motivation of our work is to let PrLMs leverage span-level information in fine-tuning. When it comes to method,  SemBERT concatenate the original representations given by BERT with representations of semantic role labels, in comparison, our work directly leverages a segmentation given by a pre-sampled dictionary to generate span-enhanced representation and requires no pre-trained semantic role labeler.  The gain of SemBERT comes from semantic role labels while the gain of our work comes from the specific segmentation, which is very different.

It's worth noticing that semantic role labeler can also generate segmentation. However, semantic role labeler will generate multiple segmentation for sentence which has various predicate-argument structures. Furthermore, such segmentation is sometimes coarse-grained (with span more than ten words), which is unpractical for our work.

\subsection{Results with Stronger PrLMs}

In addition to BERT, we also apply Span Fine-tuning to stronger PrLMs, such as RoBERTa \cite{liu2019roberta} and SpanBERT \cite{joshi2019spanbert}, which optimize BERT by enhancing pre-training procedure and predicting text spans rather than single tokens respectively.

Table \ref{tab:strong} shows that Span Fine-tuning can strengthen both RoBERTa and SpanBERT. RoBERTa is a already very strong baseline, we remarkably improve the performance of RoBERTa on RTE by four percentage points. SpanBERT already incorporated span-level information during the pre-training, but the results still support that Span Fine-tuning utilizes the span-level formation and improves the performance of PrLMs in a different dimension.

\section{ Analysis}
\subsection{Ablation Study}

In order to determine the key factors in Span Fine-tuning, a series of studies are conducted on the dev sets of eight NLU tasks. BERT$_\text{BASE}$ is chosen as the PrLM for the ablation studies. As shown in Table \ref{tab:ablation}, three sets of ablation studies are performed. For experiment  BERT$_{BASE}$ + CNN, only a hierarchical CNN structure is applied in  to evaluate whether the improvement comes from the extra parameters. To illustrate, we firstly apply two layers of CNN over the token-level representations given by BERT. Then, a max pooling operation is applied to get the sentence-level representation. Finally, the sentence-level representation and the 'CLS' representation of BERT is concatenated and sent to the classifier. In this way, the parameters of  BERT$_{BASE}$ + CNN are the same as in our method.  For experiment BERT$_{BASE}$ + CNN + Random SF, random sentence segmentation is applied to the experiment to test if the proposed segmentation method of Span Fine-tuning really functions in span-level information incorporation.  For experiment BERT$_{BASE}$ + CNN + NLTK SF, we conduct the experiments using a pre-trained chunker from Natural Language Toolkit to see whether the proposed segmentation method of Span Fine-tuning can achieve further improvements.

\begin{table}[h!]
\centering
\begin{tabular}{lc}
\toprule
method  & Avg Score \\
\midrule
BERT$_\text{BASE}$   & 82.6      \\
BERT$_\text{BASE}$ + CNN&82.5\\
BERT$_\text{BASE}$ + Random SF\footnotemark[4] &    83.0\\
BERT$_\text{BASE}$ + NLTK SF\footnotemark[5] &   83.7\\
BERT$_\text{BASE}$ + SF &   \textbf{84.2}\\
\bottomrule
\end{tabular}
\caption{\label{tab:ablation}  Ablation studied on dev sets of GLUE benchmark, we average results from three different random seeds.}
\end{table}
\footnotetext[4]{Random SF represents Span Fine-tuning with randomly segmented sentences.}
\footnotetext[5]{NLTK SF represents Span Fine-tuning with segmentation generated by an NLTK pre-trained chunker.}

The results of the experiment BERT$_{BASE}$ + CNN suggest that the improvement is unlikely to come from the extra parameters, since it reduce the overall performance by 0.1 percent. The experiment BERT$_{BASE}$ + Random SF and BERT$_{BASE}$ + NLTK SF indicate that the segmentation generated by a pre-train chunker or even random segmentation can also achieve enhancement under the Span Fine-tuning structure. However, a pre-trained chunker demands additional part-of-speech parsing process, while our segmentation method only relies on a pre-sampled dictionary and saves a lot more time, and at the same time, achieves greater improvement. Our Span Fine-tuning is able to remarkably enhance the result on all NLU tasks, raising average score by 1.6 percentage points. Overall, the result of experiments indicate that the performance improvement is primarily a result of our unique segmentation method.

\subsection{Encoder Architecture}

\cite{conneau2017supervised} mentions that the influence of sentence encoder architectures on PrLM performance varies a lot from case to case. \cite{toshniwal-etal-2020-cross} also suggests that different span representations can affect NLPs tasks greatly.

\begin{table}[htb!]
\centering
\begin{tabular}{lrr}
\toprule
Method  & Dev & Test \\
\midrule
CNN-Max   & 90.9& 90.9      \\
CNN-CNN&\textbf{91.3}& \textbf{91.1}\\
 Attention\footnotemark[6]-Max &90.7& 90.5\\
 Attention-Attention&90.8& 90.8\\
\bottomrule
\end{tabular}
\caption{\label{tab:structure}  Accuracy on dev and test sets of SNLI. SemBERT$_\text{WWM}$ \cite{zhang2019semantics} is the published SOTA on SNLI.}
\label{tab:booktabs}
\end{table}
\footnotetext[6]{Attention indicate the Self-attentive module \cite{lin2017structured}.}

To evaluate the effectiveness of our encoder architecture, we replace the component of the encoding layer and the overall structure respectively. For the component of the encoding layer, CNN \cite{kim2014convolutional} and the Self-attentive module \cite{lin2017structured} are compared. For the overall structure, two structures are considered: a single layer structure with the max-pooling operation and a hierarchical structure.

By matching every component of the encoding layer with the overall structure, four different encoder architectures are generated: CNN-Maxpooling, CNN-CNN, Attention-Maxpooling, Attention-Attention.
Experiments are conducted on SNLI dev and test sets. Table \ref{tab:structure} suggests that the hierarchical CNN (CNN-CNN) is most suitable encoder architecture for us.

\subsection{Size of $n$-gram Dictionary}
Since our segmentation method is based on a pre-sampled dictionary, the size of dictionaries will have a large impact on segmentation results. Figure \ref{fig:span_number} depicts how the average number of spans in the sentences changed along with dictionary size in CoLA and MRPC datasets. At the origin, where no segmentation is applied, every token is considered as a span. The number of spans drops significantly, as the dictionary size grows and more $n$-grams are matched and grouped together.
\begin{figure}[htb!]
	\centering
	\includegraphics[width=0.48\textwidth]{figures/span_number.pdf}
	\caption{\label{fig:span_number} Influence of dictionary size on the average number of spans in the sentences}
\end{figure}

To evaluate the influence of dictionary size on PrLM performance, experiments on the dev sets of two NLU tasks are implemented: CoLA and MRPC. To concentrate on the impact of segmentation and reduce the impacts from sub-token-level representations provided by PrLM, the concatenation process is not applied to this experiment. Rather, the span-level information enhanced representations are directly sent to a dense layer to generate prediction. As demonstrated in figure \ref{fig:case_study}, the incorporation of pre-sampled $n$-gram dictionary generates a stronger performance compared to random segmentation. Moreover, dictionaries of medium sizes (20$k$ to 200$k$) commonly result in better performance. Such trend can be explained by intuition, give dictionaries of small sizes are likely to omit meaningful $n$-grams, whereas the ones of large sizes tend to over-combine meaningless $n$-grams.

\begin{figure}[htb!]
	\centering
	\includegraphics[width=0.48\textwidth]{figures/ablation.jpg}
	\caption{\label{fig:case_study} The influence of the size of $n$-gram dictionary}
\end{figure}

\subsection{Span Fine-tuning for Token-Level Tasks}
The upper mentioned experiments are conducted on the GLUE benchmark, whose tasks are all at the sentence level. Nevertheless, token-level representations are needed in many other NLU task, such as name-entity recognition (NER). Our approach can be applied to token-level tasks with simple modification of encoder architecture (e.g. removing the pooling layer of CNN module). Table \ref{tab:ner} shows the results of our approach on the CoNLL-2003 Named Entity Recognition (NER) task \cite{tjong-kim-sang-de-meulder-2003-introduction} with BERT as our PrLM.

\begin{table}[htb!]
	\centering\small
	\setlength{\tabcolsep}{1pt}
	{
		\begin{tabular}{lccccc}
			\toprule
			& BERT$_\text{BASE}$ & BERT$_\text{BASE}$+SF& BERT$_\text{LARGE}$ & BERT$_\text{LARGE}$+SF \\
			\midrule
			Dev & 91.7 & 92.1 & 92.3 & 92.5 \\
		    Test & 95.7 & 96.2 & 96.5 & 96.8 \\
			\bottomrule
		\end{tabular}
	}

	\caption{\label{tab:ner}  F1 on dev and test sets of named entity recognition from
    CoNLL-2003, we average results from three different random seeds.
	}

\end{table}

\section{Conclusion}
This paper proposes Span Fine-tuning that maximize the advantages of flexible span-level information in fine-tuning with sub-token-level representations generated by PrLMs. Leveraging a reasonable segmentation provided by a pre-sampled $n$-gram dictionary, Span Fine-tuning can further enhance the performance of PrLMs on various downstream tasks. Compared with previous span pre-training methods, our Span Fine-tuning remains competitive for the following reasons:

\paragraph{Task-adaptive}
For methods that incorporate span-level information in pre-training, the utilization of span-level information is unlikely easily adjusted for every downstream task as span pre-training has been fixed after tremendous computational cost. In our method, the extra module designed to incorporate span-level information is trained during the fine-tuning, resulting in a more dynamically adaptation to different downstream tasks.
\paragraph{Flexible to PrLMs}
Our approach can be generally applied to various PrLMs including RoBERTa and SpanBERT.
\paragraph{Novelty}
Our approach can further improve the performance of PrLMs pre-trained with span-level information (e.g. SpanBERT). Such result implies that we our method utilizes the span-level information in a different manner comparing with PrLMs pre-trained with span-level information, which makes our method distinguished comparing with previous works.

redpajama_set_name: RedPajamaArXiv


Flying from Fairbanks to Coldfoot in Alaska then driving to the Arctic Circle and Yukon River in one day.
A snow covered April hike on Savage Alpine Trail during a clear day at Denali National Park.
Hiking to the top of Camelback Mountain (2,706 ft) in Phoenix, Arizona in October.


text:
Halsreep is een term uit de zeilvaart die betrekking heeft op langsgetuigde schepen. De halsreep dient om bij het reven van het grootzeil, het voorlijk en het onderlijk opnieuw strak te trekken op de giek.

Zeilterminologie

redpajama_set_name: RedPajamaWikipedia


text:
Q: Send mail with attachment using Python I am new to python. Below is the code we are using for sending mail.
import smtplib
import shutil
import os
from email.mime.multipart import MIMEMultipart
from email.mime.base import MIMEBase
from email.mime.text import MIMEText
from email import encoders

To="maniram.220@gmailcom"
Subject="mail through code"
Text="Hai!"
mail_sender="pull@gmail.com"
mail_passwd="ZSDCXFF"

server = smtplib.SMTP('smtp.gmail.com', 587)
server.ehlo()
server.starttls()
server.ehlo
server.login(mail_sender,mail_passwd)
BODY='\r\n' .join(['To: %s' % To,'From: %s' % mail_sender,'Subject: %s' % Subject,'',Text])
try:
    server.sendmail(mail_sender,[To],BODY)
    print("email sent")
except:
    print("error sending email")
    server.quit()

I need to zip the files in folder(path given by user) and then mail it as an attachment.
How can I attain this?

A: The following shows that it may not be necessary to zip up an entire directory to attach it to an email. Instead, just adjust the FILE_PATHS variable as needed to take all the files into account. Alternately, you are free to use create_attachment which automatically identifies directories, zips them up, and creates a well-named attachment for inclusion in the email.
#! /usr/bin/env python3
import collections
import contextlib
import email.encoders
import email.mime.application
import email.mime.audio
import email.mime.base
import email.mime.image
# import email.mime.message
import email.mime.multipart
import email.mime.text
import email.utils
import getpass
import mimetypes
import pathlib
import smtplib
import string
import tempfile
import uuid
import zipfile

FROM = 'John Doe', 'john.doe@gmail.com'
SUBJECT = 'Email Demonstration'
TO = [
    ('Jane Doe', 'jane.doe@gmail.com'),
    ('Mary Doe', 'mary.doe@gmail.com')
]
FILE_PATHS = [
    pathlib.Path(r'C:\path\to\my\file'),
    pathlib.Path(r'C:\path\to\another\file'),
    pathlib.Path(r'C:\path to\yet\another\file'),
    pathlib.Path(r'C:\why\are\there\so\many\paths')
]
PLAIN_MESSAGE = '''\
Hello, Jane and Mary!
How are you doing?
Hope you like the email.'''
HTML_TEMPLATE = r'''<!DOCTYPE html>

    <meta http-equiv="Content-Type" content="${content_type}">
    <title>${title}</title>

    ${header}
${message}

    File Manifest
${manifest}

'''
HTML_MESSAGE = [
    'When will the event start, and how much time do ushers need to show up '
    'before the event?',
    'Who will be admitted to the event, will they require tickets, and what '
    'is to be done if we run out of room?',
    'Where will the event be taking place, and will ushers need to enter '
    'through a different way than everyone else?',
    'How do you want your ushers to act, and will you have a rehearsal or '
    'practice session explaining your expectations?',
    'What clothing do you want ushers to where; and do you want the standard '
    'black suit, white shirt, and red tie ushers usually where around here?'
]

def main():
    message = create_multipart()
    for path in FILE_PATHS:
        mime, handle = create_attachment(path)
        message.attach(mime)
    message.attach(email.mime.text.MIMEText(PLAIN_MESSAGE, 'plain'))
    attach_html_text(message)
    with smtplib.SMTP('smtp.gmail.com', 587) as server:
        server.starttls()
        server.login(input('Username: '), getpass.getpass())
        server.send_message(message)

def create_multipart():
    message = email.mime.multipart.MIMEMultipart('alternative')
    message['Message-ID'] = make_message_id()
    message['From'] = email.utils.formataddr(FROM)
    message['Date'] = email.utils.formatdate(localtime=True)
    message['Subject'] = SUBJECT
    message['To'] = email.utils.COMMASPACE.join(map(
        email.utils.formataddr, TO
    ))
    return message

def make_message_id():
    return email.utils.make_msgid(str(uuid.uuid4()))

@contextlib.contextmanager
def create_temp_zip_file(directory):
    paths_to_zip = collections.deque([directory])
    with tempfile.TemporaryDirectory() as root:
        handle = pathlib.Path(root) / f'{directory.name}.zip'
        with zipfile.ZipFile(handle, 'x') as file:
            while paths_to_zip:
                path = paths_to_zip.popleft()
                if path.is_dir():
                    paths_to_zip.extend(path.iterdir())
                elif path.is_file():
                    file.write(path, path.relative_to(directory))
        yield handle

def create_attachment(path):
    if path.is_dir():
        with create_temp_zip_file(path) as handle:
            return create_attachment(handle)
    kind, encoding = mimetypes.guess_type(str(path))
    if kind is None:
        kind = 'application/octet-stream'
    main_type, sub_type = kind.split('/')
    if main_type == 'application':
        with path.open('rb') as file:
            mime = email.mime.application.MIMEApplication(
                file.read(),
                sub_type
            )
    elif main_type == 'audio':
        with path.open('rb') as file:
            audio_data = file.read()
        try:
            mime = email.mime.audio.MIMEAudio(audio_data)
        except TypeError:
            mime = email.mime.audio.MIMEAudio(audio_data, sub_type)
    elif main_type == 'image':
        with path.open('rb') as file:
            image_data = file.read()
        try:
            mime = email.mime.image.MIMEImage(image_data)
        except TypeError:
            mime = email.mime.image.MIMEImage(image_data, sub_type)
    # elif main_type == 'message':
    #     attachment = email.mime.message.MIMEMessage()
    elif main_type == 'text':
        with path.open('rt') as file:
            mime = email.mime.text.MIMEText(file.read(), sub_type)
    else:
        mime = email.mime.base.MIMEBase(main_type, sub_type)
        with path.open('rb') as file:
            mime.set_payload(file.read())
        email.encoders.encode_base64(mime)
    mime.add_header('Content-Disposition', 'attachment', filename=path.name)
    content_id = make_message_id()
    mime.add_header('Content-ID', content_id)
    handle = f'"cid:{content_id.strip("<>")}"'
    return mime, handle

def attach_html_text(message):
    mapping = dict(
        content_type='',
        title=message['Subject'],
        header=message['Subject'],
        message=make_html_list(HTML_MESSAGE, 'o', 1),
        manifest=make_html_list(FILE_PATHS, 'u', 1)
    )
    template = string.Template(HTML_TEMPLATE)
    mime = email.mime.text.MIMEText(template.substitute(mapping), 'html')
    mapping['content_type'] = mime['Content-Type'].replace('"', '')
    mime = email.mime.text.MIMEText(template.substitute(mapping), 'html')
    message.attach(mime)

def make_html_list(iterable, kind, indent=0, space=' ' * 4):
    prefix = space * indent
    elements = [f'{prefix}<{kind}l>']
    elements.extend(f'{prefix}{space}
{item}
' for item in iterable)
    elements.append(f'{prefix}</{kind}l>')
    return '\n'.join(elements)

if __name__ == '__main__':
    main()

redpajama_set_name: RedPajamaStackExchange


text:
Britney Spears Celebrates Turning 40
Filed Under:40, birthday, Britney Spears, music
LAS VEGAS, NV - FEBRUARY 26: (EXCLUSIVE COVERAGE) Britney Spears performs Britney Spears: Piece of Me Remixed. Reimagined. Still iconic. At Planet Hollywood Resort & Casino on February 26, 2016 in Las Vegas, Nevada. (Photo by Denise Truscello/BSLV/Getty Images for Brandcasting, Inc)
(CNN) — Britney Spears turns 40 on Dec. 2. The singer, who rose to enormous popularity as a teen with her hit "…Baby One More Time," posted to social media to remind fans that she still feels young.
Spears modeled bikinis in the post and quoted the film "This is 40," starring Leslie Mann, who says, "I don't want to shop at old lady stores. I don't want to go to J. Jill and Chico's and Ann Taylor."
READ MORE: Marvel Star Evangeline Lilly Attended D.C. Rally Against COVID Vaccines And Mandates
She tried on multiple clothes in a reel, appearing in front of a huge Christmas tree while she tried on festive dresses.
"Fashion and festivities … guess what ???? My b-day is coming soon," she captioned the video, and included a birthday cake emoji.
READ MORE: Dolly Parton Released A Line Of Southern-Style Cake Mixes, But You May Have To Wait To Try Them
Spears has a lot to rejoice about this year after winning her fight to end her 13-year conservatorship and regaining control of her finances.
She has since been celebrating her new life on Instagram with dances and inspirational quotes.
MORE NEWS: Special Grand Jury Granted In Georgia's Trump Election Interference Probe

redpajama_set_name: RedPajamaCommonCrawl


On February 23, the Senate Health, Education, Labor and Pensions (HELP) Committee will hold a hearing on the implementation of the Every Student Succeeds Act (ESSA). This law, which was enacted at the end of 2015, reauthorizes the Elementary and Secondary Education Act which governs general education in our nation's public schools. ESSA devolves much authority back to the states. Disability advocates are urged to actively participate in its implementation in their states as most students with disabilities spend the majority of their instructional time in the general education settings. Visit the Committee web site to learn more and to watch a live webcast the day of the hearing.


text:
Home / Theosophical Classics / Real H. P. Blavatsky, The: A Study in Theosophy, and a Memoir of a Great Soul
Real H. P. Blavatsky, The: A Study in Theosophy, and a Memoir of a Great Soul
$19.99 (as of April 19, 2020, 10:32 am)	& FREE Shipping. Details
Real H. P. Blavatsky, The: A Study in Theosophy, and a Memoir of a Great Soul quantity
Categories: Blavatsky Collection, Theosophical Classics Tags: biographies, theosophical movement, theosophy
The present biography was written by Wm. Kingsland, a student and close associate of H.P. Blavatsky—founder of the Theosophical Society. Blavatsky's theosophical teachings, based on what she herself was taught by Eastern adepts, paint a grand picture of who and what we are as human beings, where we come from and what is our shared destiny. These ideas are so inseparable from the way in which she viewed the world and herself, that to attempt any biography of her one must also understand, to some degree, those teachings. In this regard, Kingsland's biography is second to none. || From the Preface: "In speaking of the Real H. P. Blavatsky I use the term first of all as correcting the false representations and misconceptions which have been so commonly and so lightly accepted by the world at large; and, secondly, as signifying—what in fact each of us possesses—an inner Self, a real Self as distinguished from the fluctuating, changing personality; a Self which, in the majority of us, is only very feebly active in or through the temporary personality. This distinction between the higher and the lower Self is a fundamental one, not merely in Theosophy but also in all Mysticism, both philosophical and devotional. . . . Yet, even when we have done this, the fact remains that it is not the personality of H. P. Blavatsky that matters at all, either in its outer or its inner aspects. What really matters is the message which she gave to the world. And perhaps the giver of that message can only be appreciated in proportion as the message itself is received. Where it is not received: where it runs counter to hard and fast conventions, prejudices, beliefs or dogmas, one can hardly expect in the present state of society, or the present characteristics of human nature, that the moral law "Judge not, that ye be not judged" will be respected any more in the case of H. P. Blavatsky than it is in other cases. Nevertheless, I may possibly hope in the following pages to do something towards correcting many of the misrepresentations and slanders to which the detractors of Theosophy have so freely lent themselves. This work is, therefore, as much a study of Theosophy as a Memoir of H. P. Blavatsky."—William Kingsland
Damodar and the Pioneers of the Theosophical Movement
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Esoteric World of Madame Blavatsky, The: Insights into the Life of a Modern Sphinx
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Secret Doctrine, The: The Synthesis of Science, Religion, and Philosophy (2-volume set)
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Secret Doctrine, The: The Synthesis of Science, Religion, and Philosophy (2-volume set) Secret Doctrine Dialogues, The: H. P. Blavatsky Talks with Students
Secret Doctrine, The: Volumes I and II : A Facsimile of the Original Edition of 1888 $25.00 (as of April 19, 2020, 9:30 am)
Vedas: The Samhitas of the Rig, Yajur, Sama, and Atharva [single volume, unabridged] $19.99 (as of April 19, 2020, 9:26 am)	& FREE Shipping. Details
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Real H. P. Blavatsky, The: A Study in Theosophy, and a Memoir of a Great Soul $19.99 (as of April 19, 2020, 10:32 am)	& FREE Shipping. Details


Mising is one of the indigenous tribal communities of Assam with a rich tradition of weaving. A Mising woman, despite being engaged in myriad household activities through the day, would spend some time on her loom everyday. The women make garments, mainly for everyday use, through handloom weaving. They also weave classy products for special occasions. Mising women would weave in advance, five to 10 sets of mekhela chador — a traditional women's attire — to gift their daughters at the time of marriage.
In Assam, women feel proud to wear handloom products, especially mekhela chador, during special occasions like weddings and festivals like Bihu and Saraswati puja. Hence, the products are in great demand among people residing in Assam as well as outside the state. However, handloom weaving has not taken off as a thriving source of livelihood for various reasons. Using simple technology and adapting to market needs, the Mising women have now found the prospects of handloom weaving looking bright.
The traditional throw shuttle loom is built under the stilt house, such houses being a traditional practice among the Misings to adapt to the floods. Using a tedious process that needs patience, the weavers produce wraps like mekhela chador and gero, stole like gamosa, besides some utilitarian items. Traditionally, weaving in Mising community was for their own use. But these days Mising handloom products are much in demand.
The demand of Mising handloom was beyond expectation in the handloom fair in 2014 when the total sale was for Rs 14 million over a period of five days. But due to low efficiency (weavers produce just two mekhela chador in a month) of traditional looms and limited product diversity, the weavers' remuneration is low. As a result, handloom weaving is not seen as a viable livelihood.
However, dovetailing modern technology with the traditional skill of the weavers, handloom can provide a lucrative livelihood opportunity for the families of the area. With the objective of bridging the demand and supply gap, and making handloom weaving a sustainable livelihood, Mising Autonomous Council (MAC) and the Center for Microfinance and Livelihood (CML) with the Tata Trusts planned an intervention to be executed over three years.
The team decided to introduce technology to overcome the low production efficiency. As a first step, a warping drum was introduced for getting a defect-free warp. Unlike the traditional method, warping drum helped reduce the area required to make a warp.
Replacing the throw shuttle loom with advanced fly shuttle loom brought down the cost of production. A complete set of fly shuttle loom costs about Rs 30,000. But the cost was reduced to Rs 5,000 through a local innovation of replacing the steel frame of the loom with bamboo frame. Bamboo is not only locally available, it is also cheaper, durable and easily repairable.
In batches of 25, the weavers underwent a 60-day skill training, overcoming resistance from the men at home, giving up their contribution to the livelihood and running of the family. During the training, they learnt to take the warp in a warping drum, practice in the fly shuttle loom, develop designs, diversify products and also maintain records. In 18 months, 375 members have completed their training.
On completion of training, each weaver received a fly shuttle loom. With the prospects appearing good, the weavers provided bamboo and labor for construction of a 100 sq. ft weaving shed in their homes where floods would not damage them. "Earlier I couldn't take warp during rains as it required a large open ground but today with the warping drum I can take warp in a single room," Runa Doley of Matmora village told VillageSquare.in.
The training and practice in the new loom has increased the production efficiency up to a maximum of 250%. "Now I produce four mekhela chador in a month, when earlier I could make only one or two," Anima Taye of Sisitangoni village told VillageSquare.in.
The introduction of technology has not only improved production speed but also reduced the drudgery for the weavers. "Earlier I had to bend after every weft to throw the shuttle and I couldn't sleep at night because of the back pain. But now I weave at triple the usual speed without bending and without pain," Purnima Kaman told VillageSquare.in.
The women took a while to consider that handloom weaving could be taken up commercially and to deliver orders on time. Exposure through handloom exhibitions and community-led commercial enterprises has instilled in the weavers the need to complete orders on time and to move beyond the traditional use of yarn, color combination and design to suit market requirement. "Till two months back I wove only what my mother taught. Now I know warp measurement for different products and can weave any design developed in a graph paper," said Doley.
Besides the traditional wraps, now the women weave shawl, stole, table cloth, curtain, fabric for shirt, kurta etc. in diverse design and combination of colors. As a result, the range of buyers for their product has increased in comparison to earlier days, with the sale almost doubling in a month. "It was painful to weave a mekhela chador for a month and get just Rs 1,200 in return. Now I will earn more," Doley told VillageSquare.in.
The women came together as informal producer groups (PG). With the PG periodically purchasing yarn, the women avail of their working capital in the form of yarn at a minimal rate of interest. The weavers are free to sell the products on their own or through a company promoted by CML.
Out of the women who got trained, 200 weavers have already constructed their shed and started production in their new loom, with the working capital provided for about 3kg of yarn. The weavers have formed three PGs with 150 members in each. The PGs undertake collective direct selling to a few customers.
Timely marketing of the products and regular cash flow to the weavers is now a challenge. So the marketing channel will have to be strengthened and more organized. The nascent initiative shows that handloom weaving could change the lives of the Mising population in the flood-prone riverine area for better. There is a need for the state also to step in and to scale up the initiative.
Abdul Hamid works for Centre for Microfinance and Livelihood as Senior Program Manager. He worked with Professional Assistance for Development Action (PRADAN) for many years after graduating from Assam Agricultural University. Views are personal.
Abdul Hamid works for Centre for Microfinance and Livelihood as Senior Program Manager. He worked with Professional Assistance for Development Action (PRADAN) for many years after graduating from Assam Agricultural University.
I appreciate the gesture of the Tata Trust to make the flood affected people to sustain their livelihood and save the real culture of the gramins of beautiful Assam. Salute to the Trust!


text:
CPSL has been established by the Australian Catholic Bishops Conference and Catholic Religious Australia.
Whistleblowing at CPSL
The Members of Catholic Professional Standards Ltd are the Australian Catholic Bishops Conference (ACBC) and Catholic Religious Australia (CRA).
Catholic Professional Standards Ltd is governed in accordance with the company's Constitution. The current Constitution can be read here. The Constitution was last amended in November 2018.
In accordance with the Constitution, the Members are represented by two Member Representatives. The Member Representatives are:
Australian Catholic Bishops Conference:
Archbishop Mark Coleridge, Archbishop of Brisbane
Bishop William Wright, Bishop of Maitland-Newcastle
Catholic Religious Australia:
Sr Clare Nolan rsc, Congregational Leader, Sisters of Charity
Fr Tom McDonough cp, Provincial, Passionist Fathers
The Board meet with the Member Representatives twice per year (May and November).
Catholic Professional Standards Ltd (CPSL) operates as an independent entity. Board directors are lay men and women with professional expertise in governance, law, education, child protection, human services, safeguarding and regulation.
There are no bishops, priests or religious brothers or sisters on the Board.
The Honourable Geoff Giudice AO (November 2016)
LLB, BA
Geoff Giudice has a background in industrial relations and labour law having worked in the union movement and the retail industry before commencing legal practice, first as a solicitor, and subsequently a member of the Victorian Bar. He was appointed to the Federal Court and was president of the Australian Industrial Relations Commission in 1997 and became the inaugural president of Fair Work Australia (now the Fair Work Commission) in 2009, retiring in 2012.
Geoff has degrees in Law and Arts from the University of Melbourne and is an Honorary Professorial Fellow at the University of Melbourne Law School (Centre for Employment and Labour Relations Law).
Since 2012 Geoff has been involved in a range of activities including chair of the AFL Players Disputes Board, a member of the AFL Anti-Doping Appeal Board and the establishment of industrial relations systems in developing countries.
Geoff is currently a consultant in the employment law practice of Ashurst Australia and Chair of the Independent Review Panel under the Gambling Regulation Act 2003 (Vic).
Current Appointments:
Honorary Professorial Fellow, University of Melbourne Law School (Centre for Employment and Labour Relations Law)
Consultant, Ashurst Australia (Employment Law)
Chair, Independent Review Panel, Gambling Regulation Act 2003 (Vic)
Deputy Chair
Patricia Faulkner AO (November 2016)
MBA, BA (Econ), Dip Ed
Patricia Faulkner is a former secretary of the Department of Human Services in Victoria. In this role, Patricia led portfolios including child protection, youth justice, public hospitals, public housing, disability accommodation and community health services.
In 2015-16, Patricia was deputy commissioner of the Victorian Royal Commission into Family Violence.
For 25 years Patricia has led large complex organisations and held a variety of chairman and director roles with government entities and not for profit boards across a range of sectors.
Patricia is currently Chair of Jesuit Social Services, Chair of the Melbourne Racing Club Foundation, Chair of St Vincent's Health Australia's response to Royal Commission into Aged Care Quality and Safety Committee and Chair of the CBA Advisory Panel.
Chair, Jesuit Social Services
Chair, Melbourne Racing Club Foundation
Trustee, Vic Super
Chair, St Vincent's Health Australia response to Royal Commission into Aged Care Quality and Safety Committee
Committee Member, Melbourne Racing Club
Board Member, Melbourne Theatre Company
Chair, Advisory Panel CBA
Chair, Melbourne Academic Centre for Health Council (MACH)
Professor The Honourable Michael Lavarch AO (August 2017)
Professor Michael Lavarch has had extensive involvement in Australian public life, serving in local government before being elected to Federal Parliament in 1987, serving as attorney general in the Keating Government from 1993-1996.
Since leaving Parliament in 1996, Michael has been the secretary general of the Law Council of Australia, dean of Law at QUT and, since 2012, Deputy Chief Commissioner and Commissioner, Risk, Intelligence and Regulatory Support at the Australian Skills Quality Authority (ASQA).
Michael has worked with organisations supporting child welfare programs including the Forde Foundation which was established by the Queensland Government in response to the findings into the abuse of children in Queensland institutions.
Michael is currently Chair of the Telecommunications Industry Ombudsman Ltd and Chief Adjudicator with the Alcohol Beverages Advertising Code Adjudication Panel.
Chair, Telecommunications Industry Ombudsman Ltd
Chief Adjudicator, Alcohol Beverages Advertising Code Adjudication Panel
Chair, Way Forward Debt Solutions Ltd
Deputy Chief Commissioner, Risk, Intelligence & Regulatory Support, Australian Skills Authority (ASQA)
Co-Chair, Path to Treaty Panel
Review of NSW Branch of Labor Party (fixed short term Oct - Nov 2019)
Dr Robyn Miller (August 2017)
PhD, MFT, GradDipFT, BSocSc (Social Work)
Dr Robyn Miller brings over 30 years' experience in community services, local government and child protection sectors, and has practised in both the public and private sectors as a therapist, clinical supervisor, consultant and lecturer.
From 2006-15 Robyn provided professional leadership as the chief practitioner within the Department of Human Services in Victoria, was a member of the Victorian Child Death Review Committee for 10 years and worked as a consultant with the Royal Commission into Institutional Responses to Child Sexual Abuse.
Since 2016, Robyn has been the CEO of MacKillop Family Services, which provides specialist services to vulnerable and disadvantaged children, young people and their families in Victoria, New South Wales and Western Australia.
Chief Executive Officer, MacKillop Family Services
Board member, Catholic Social Services Australia
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Dr Ruth Shean (February 2018)
PhD, MEd
Dr Ruth Shean is a former senior public servant in the Western Australian government with more than 30 years of experience in governance, corporate management and culture change.
Ruth is the former Western Australian Commissioner for Public Sector Standards with responsibility for oversight of legislative and regulatory standards in the WA public sector.
Ruth is the former Director General of the Department of Training and Workforce Development in WA. Previous government CEO roles include Director General of the Disability Services Commission and Director General of the Department for Community Development.
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text:
Wendtorf est une commune allemande de l'arrondissement de Plön, dans le Land de Schleswig-Holstein.

Géographie
La commune se situe à l'est de la fœrde de Kiel. La vieille ville se situe à quelques centaines de mètres à l'intérieur. Le front sur la mer Baltique est divisé en deux, entre la marina et la plage.

Au nord du territoire, il y a la réserve naturelle maritime de Bottsand.

Histoire
Un premier village est mentionné au même endroit en 1240. En 1440, il est appelé « Wendtorf ». En 1796, les premières maisons sont construites près de la plage. L'ancien village de pêcheurs vit maintenant du tourisme.

Marina de Wentdorf
Au port de plaisance, autrefois un port de pêche, à l'occasion des Jeux olympiques d'été de 1972 de Munich dont les épreuves de voile se déroulent à Kiel, un plattenbau d'immeubles de cinq étages d'appartements, soit 500, est construit. Le site présente des similitudes avec le Centre Olympique. Il comprend aussi une galerie commerciale et des installations de loisirs, comme un mini-golf, une salle de badminton et une mini-piscine extérieure, aujourd'hui fermées. À l'époque, une école maternelle est aussi bâtie et reçoit toujours les enfants de la commune et de Stein, Lutterbek, Barsbek et Wisch.

En 2007, la société en commandite dépose le bilan et est liquidée l'année suivante. Après la faillite, Sven Hollesen présente pour son entreprise Planet-Gruppe, spécialisée dans le tourisme, un projet de modernisation et d'extension de la marina à hauteur de 50 millions d'euros.

Source, notes et références

Commune au Schleswig-Holstein


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OI! YES, I'M BARKIN' AT YOU, YOU NINNY!
Re: Who has a fursuit?
There is a picture of the fursuit head on my Fur Affinity, maybe even Deviant Art.
Bastsheba, I'll check it out!
Well for one, I do not own one- So I do not wear one.
And, I don't think I'd ever get one. They're cool and all, but I think they are just strange. Too hot, bulky, no visibility. I'm just not really a fan of them. Like, I see pictures of people wearing them in public and my reaction is, "That's weird." Kind of strange that I think that.
I made a couple heads, paws, and alot of tails. I love wearing tails.
I love fursuits they look like so much fun to wear, except in the summer though.
For my first suit im getting Cassandra done hopefully by MixedCandy. I love their suits and I think they'll capture Cassandra's personality perfectly.
Full suit or partial suit, it really depends on what their offering at the time, i prefer a partial but i dont mind getting a full suit either it'll just cost more.
I dont think I'd every really get a suit. I'd like to try it one day though to see what it's like. If a friend ever got one though I would be their handler? Is that what there called? They guy who stops them walking into lamp posts and watches out for them.
I've got Phoenix Wolf Suits in mind. I like their style, and there prices should be affordable for me.
I just got back from a local con and its put my mind to the idea of starting on a suit. Sadly I don't think I'll be able to until I move out because I'll have no where to put it since my room is full of all my junk and old cosplays.
Going to Furnal Equinox on Saturday. I'll be sure to get some photos done.
That the circus themed one this year?
Cool! If you see me I'll be fursuiting in my brown dhole fursuit. I'll probably be hanging around a purple snow leopard haha. If you want, pm me your cell if you wanna meet up. I have horrible memory and I'll only be there from 12 - 6.
Can't wait 'till I can get one... Wonder how hard to make/find an afordable partial... Thinking of going as a furry Edgeworth to Nebcon in October.


Very happy to have recently received my start date too.
Got my New Starter form by email today, with contract to follow by snail-mail next week.
Hey all .. so today my status has changed to .. Your application is currently going through pre-employment checks and has been referred for a decision.
Will I hear some thing soon or is there still a long wait??
That means you are now in vetting. Roughly 8 weeks but known to be a lot longer at times.
Mercedes, once Vetting started, it had the "pre-employment checks are being being carried out" message, then it changed to "we will contact you shortly to advise you of the next step" - I got the call a few days after that.
I've recieved my contract and everything. The pay on the contract is much less than was originally stated. Has anyone else had this?
It will be as you wont be at your establishment for 3 months. Your pay will be the same as everyone elses at the poelt course.


text:
What Literacy Skills Do Students Really Need for Work?
By Catherine Gewertz — September 25, 2018 | Corrected: October 04, 2018 5 min read
Corrected: An earlier version of this story included the wrong affiliation for Steve Graham. He is a professor at Arizona State University.
Schools are under growing pressure to make sure that students are ready for work or job training, as well as college, when they graduate from high school. But employers say their young hires haven't learned the reading, writing, and verbal-communication skills that are most important to a successful working life.
That gap between reality and expectations begs a boxful of questions about whether there's a preparation problem and, if so, how to solve it.
Should K-12 schools add workplace-oriented literacy skills to their already-heavy lineup of classics like the five-paragraph essay? Who should teach young people how to write an environmental-impact report or explain quarterly business results to investors: High schools? Colleges? Or are such skills better learned at work or in job-training programs?
Surveys of employers paint a picture of discontent. Executives and hiring managers report that they have trouble finding candidates who communicate well. Good oral-communication skills, in particular, rank especially high on employers' wish lists, alongside critical thinking and working in teams.
But do companies' hiring struggles mean that K-12 schools, colleges, and job-training programs are doing a poor job of preparing students for work?
Some labor economists argue that the much-ballyhooed "skills gap" is caused not by inadequate career preparation but by companies' refusal to provide the pay and training necessary to get the workers they need. And many educators argue that the primary purpose of schooling isn't to create a jobs pipeline but to prepare young people to be informed, active citizens.
Education Week's new special report on literacy and the workplace won't be able to resolve these arguments for you. But it can give you a glimpse of how some schools and employers are grappling with the workplace-literacy demands that young people face. Relatively few K-12 schools, it seems, are seriously exploring this kind of work.
From Mechanics to Managers
Perhaps unsurprisingly, career and technical education programs are more likely than regular K-12 schools to teach literacy skills in ways that link directly and explicitly to work. Helping aspiring engineers decode complicated technical reports, for instance, can help students make real-world connections and fire up their motivation. But they also need strong general-literacy skills so they don't bump into glass ceilings.
"Are we creating students who can be lifelong learners, not just the oil changers and mechanics, but the managers, the owners of the business? If you're in it for that game, then reading is vitally important for their long-term success," said Travis Park, who studies the integration of core academics into CTE programs as an associate professor of agricultural education at North Carolina State University in Raleigh.
By most accounts, students are bringing weak writing skills into college and entry-level jobs. Eight years after nearly all states' adoption of the Common Core State Standards, which call for more writing, studies show that high school students do very little substantive writing at all, let alone writing in specific genres such as business or science that would give them a taste of the working world.
The kinds of writing employers prize most are often not the most highly technical ones. They want workers who can explain things concisely, support their requests with evidence, and understand the appropriate shifts in tone necessary for different audiences. One teacher surveyed local employers and found that what they really wanted was for her to teach cursive writing, so workers could sign their paychecks.
Some kinds of workplace writing, however, should be taught by employers, because schools can't cover them all, said Steve Graham, a professor at Mary Lou Fulton Teachers College at Arizona State University, who specializes in writing instruction.
"If we make an assumption that writing differs by context, some of the responsibility [for teaching students] just realistically is going to have to fall on businesses, and I think businesses should accept that," Graham said.
"Schools are not going to be able to be responsible for all … the different kinds of skills that [businesses] want. And even if they were more responsive, there would still need to be some on-the-job training."
The Newest Literacy
Digital literacy is also claiming its place, alongside reading, writing, speaking, and listening, as a crucial skill set in the workplace. Education Week spent time with four employees in very different jobs at a big Delaware health-care provider and found that those new-age skills are essential at every stage of the employment ladder.
On the lower rungs, for jobs such as environmental service assistants (who clean and disinfect rooms) and patient-care technicians (who take vital signs), the ability to go beyond basic familiarity with apps and digital communications tools to manipulate and make sense of information using different software packages is a big key to advancement.
At the top rungs, people like health information management executives need deep clinical knowledge but also a rich understanding of where and how information is generated, stored, analyzed, presented, and used across different systems, and the ability to synthesize that information and use it to make strategic decisions.
"Schools absolutely need to be teaching these skills. If students don't get the technological know-how and mindset to adapt to and learn these technologies, they can't be competitive," said Hiller Spires, a professor and senior research fellow in literacy education at North Carolina State University.
Matthew T. Hora, who studies workplace communication as a cultural anthropologist at the University of Wisconsin-Madison, urges schools and employers to embrace a blended concept of responsibility for workplace-literacy skills.
"I've talked to a lot of educators who think it's not their job to prepare students for careers, that their job is to perpetuate disciplinary knowledge and build engaged citizens," Hora said.
"But it's problematic to completely ignore what is going to happen to these graduates when they go out into the world. We need to think about the labor market and careers and make sure that a broad range of things are on the table when we talk about the purposes of education."
Career Readiness High Schools Student Engagement
A version of this article appeared in the September 26, 2018 edition of Education Week as What Skills Do Students Really Need for Work?
From the Special Report
Literacy for the Workplace
Jobs Have Evolved. Will English Class?
College & Workforce Readiness Speaking Skills Top Employer Wish Lists. But Schools Don't Teach Them
Catherine Gewertz, September 25, 2018
College & Workforce Readiness Jobs at All Levels Now Require Digital Literacy. Here's Proof.
Benjamin Herold, September 25, 2018
Reading & Literacy Is Professional Writing the Missing Link in High School English Classes?
Sarah D. Sparks, September 25, 2018
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When COVID-19 closed schools for millions of students, Education Week documented two seniors as they faced an uncertain future.
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College & Workforce Readiness COVID-19's Disproportionate Toll on Class of 2020 Graduates
The pandemic hit college-bound members of the class of 2020 from low-income homes much harder than it did their better-off peers, our survey found.
Alex Harwin
Magdalena Estiverne graduated from high school this past spring during the COVID-19 pandemic. She is currently taking online community college classes.
Eve Edelheit for Education Week
College & Workforce Readiness Conflicting Messages Exacerbate Student Detours on the Road to College
Amid the many disruptions of the COVID-19 era, it's more important than ever for educators to be consistent about the admissions requirements—and the costs—of college.
Sarah D. Sparks
Liz Ogolo, 18, who is attending Harvard University this fall, said the transition to college was difficult without guidance from her high school, which switched to remote learning in the spring.
Angela Rowlings for Education Week
College & Workforce Readiness Coping With Disruption at School and at Home
A 2020 high school graduate struggles to continue her education despite a disrupted senior year, a move to a new home, and spotty internet access.
Magdalena Estiverne graduated from Evans High School in Orlando, Fla., this past spring during the COVID-19 pandemic.


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Artist Profile Video: Richard Lewer on Eden Menta
— October 5, 2019
Contemporary artist Richard Lewer talks through his and Eden Menta's collaborative relationship, Menta's practice and why he thinks Menta is one the best young contemporary artists working today. Menta's practice spans multiple mediums and themes ranging from the macabre to insect life to the possibilities of self-portraiture.
About Eden Menta
Eden Menta works predominantly in the mediums of ink on paper and digital art. Her work is influenced by pop-culture references from television, magazines and her own life experiences. Edgy and instilled with a wry humour, Menta's work is often motivated by social and political beliefs, resulting in a street-art, sub-culture feel. Eden Menta is an emerging artist and has been attending the studio program at Arts Project Australia since 2013. Since this time she has exhibited annually at the Arts Project Australia gallery. She has also exhibited in group exhibitions at Brunswick Street Gallery, Melbourne (2015 and 2014), No Vacancy Gallery, Melbourne (2014), Gertrude Glasshouse, Melbourne (2016) and Arts Project Australia (2016).
This video has been made possible by the generous support of The Jack Brockhoff Foundation, and was created in partnership with MediaLink Productions.
Eden Menta – Photo Kate Longley
Artist Profile Video: Charlotte Day on Mark Smith
The Natural Way Interview #1 and #2 – Robin Warren & Danny Lyons
Read more from Arts Project Australia.
Explore our exciting suite of exhibitions, articles, videos, publications and more.
Have you seen us?
Win a $100 Arts Project Australia gift certificate to spend in our online stockroom! In partnership with Civic Outdoor, artworks by Arts Project Australia artists have been featured on billboards rotating around the city and metropolitan areas of Melbourne over the past few months. Constantly on the move, catching this exhibition requires a keen eye. For this reason, it's time to band together and collectively document the artworks in their public, large-scale form before the exhibition closes at the end of 2020! If you spot an Arts Project Australia billboard, tag or send us a picture via social media or email [email protected] to go into the draw to win. Note the billboards are digital and might feature an artwork between other advertisements, so it is worthwhile getting your camera ready and waiting to see if you are lucky when you pass a Civic Outdoor billboard. Civic Outdoors have generously provided the space on their suite of digital billboards free of charge. The billboards feature Julian Martin, Mark Smith, Eden Menta, Monica Lazzari, Anthony Romagnano, Samantha Ashdown, John Bates, Robin Warren, Warren O'Brien, Lachlan Turk, Boris Cipusev, Miles Howard Wilks and Jordan Dymke. Entries close 31 December 2020 and winner drawn at random in the new year (after January 18 2021) with a selection of entrant images featured on Arts Project Australia's Instagram and Facebook pages. Voucher redeemable via online stockroom for 3 years.
Thank you for Supporting the 2020 Virtual Gala
Arts Project Australia extends thanks to supporters of the 2020 Virtual Gala. The Virtual Gala launched on Friday 20 November with over 1000 visits to the virtual exhibition hosted on Arts Project's website and many also crossing over to long-time supporter Leonard Joel for the silent auction. On Saturday November 28, after a week of browsing and early bidding on the 201 artworks, over 600 supporters Arts Project Australia tuned in across YouTube, Facebook and Instagram for the Virtual Gala live stream. From home, audiences enjoyed a delectable picnic hamper from the Recreation and watched the event to ponder, celebrate and laugh about the year of 2020 with entertainment from an enviable line-up of Melbourne-based performers including Paul Kelly, Claire Hooper and Moira Finnucine. It wasn't over yet - the enthusiasm continued online at the silent auction, which closed several days later on Tuesday 2 December with 86 lots sold (not including post-auction sales) and 100% of the $20,000 made in sales paid directly to successful artists. The Virtual Gala was a heartfelt celebration of the artists and their incredible work over 2020. The success of the first iteration of the Gala- a long-lasting cornerstone of the Arts Project Australia program - in virtual format shows testament to the committed support and love for Arts Project artists and their work from a community of creative supporters far and wide. With special thanks to Arts Project Australia artists, as well as their families, carers and support people; entertainers Paul Kelly, Claire Hooper, Moira Finnucine, Trent Walter, Mama Alto, Maude Davey, Paul Cordeiro, Piera Dennerstein, Jazida and Rachel Lewindon; producer Sian Darling and designer Liz Cox; John Albrecht and Leonard Joel team; Arts Project Australia executive director Sue Roff and staff Sim Luttin, Jo Salt, Sarah Wood, Danielle Hakim, Tahney Fosdike, Margaret McIntosh, Sandy Fernee, as well as studio, admin staff and volunteers; as well as the board and Exhibition Committee, with special note to Virginia Lovett, Mandy Hall, Denise Damianos, Damien Hodgkinson, Marie McCarthy and Melinda Martin. Missed the Virtual Gala? You can still watch recorded livestream, visit the virtual gallery or see auction results.
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closer look and sure enough the machine in question was NOT a "Centennial" model.
How did I know? You'll find out in a moment. But first, a brief history lesson.
Isaac Singer began manufacturing sewing machines in 1851. Over the next hundred years Singer Manufacturing Comany (aka Simanco) grew into a gigantic company with factories in half a dozen countries.
To commemorate 100 years of manufacturing, Singer struck a special edition trademark badge which graces the special edition models. The Centennial badge has a distinctive blue band around the edge and the words "A Century of Sewing Service 1851-1951"
Many of the machines of this limited edition were produced by the thousands or hundreds of thousands in previous and subsquent years, but only a limited number were issued with the blue badge. Which makes this special edition stand out as unique, and therefore more valuable in the eyes of collectors.
Which is why some sellers will tout a machine as a Centennial model to make their machine stand out in a crowd. While certainly misleading, some will argue that it is not technically fraudulent because the machine in question was manufactured in 1951, the year Singer celebrated their centennial anniversary.
The issue is with serial numbers and manufacture dates.
The serial numbers of the blue-badged machines reveal that many of these machines were manufactured prior to 1951 and were labeled with the special edition badge for sale during 1951. For example, the blue badge pictured above belongs to a machine with a serial number allocated in 1949.
Which means that many machines with serial numbers or manufacture dates from 1951 do not carry a Centennial badge. But some sellers contend that if a machine was manufactured in 1951 then that makes it a Centennial model.
I do not agree. The term "Centennial" should be reserved for those machines marked by Singer as belonging to the commemorative special edition. That is what "special edition" means.
So next time you see "Centennial" in the headline, look for the distinctive blue band on the badge. Then take a moment to reflect on the heritage and legacy of these remarkable sewing machines.
Thank you for sharing a great link!
The Singer serial number 'dates' are not manufacture dates, they are batch commission dates. You are absolutely right that they were preparing for the Centennial with machines commissioned as early as 1949, ready for sale in 1951. By about October 1951, the batches commissioned were not released with Centennial badges.
Where can I get further info?
I phoned 1-800-4-singer yesterday, gave the serial number and asked what model my machine was. They were not able to answer my question because they only had information up to serial numbers starting with E.
According to the list I have your machine was manufactured in 1962 in "K"
Karachi, Pakistan but I will continue to be sure.
I have the Anniversary series, it was my grandmother's and I use it all the time. It has the badge with the blue band, etc. It's a great little machine!
My mom has this machine. How much are they worth, we may be having to sell.
Thanks for asking. It isn't possible to specifically appraise a machine without seeing, touching, and testing it and we don't have the resources to do that cross country.
Generally speaking, though, the simple answer is: your machine is worth whatever someone is willing to pay for it.
The value of any machine depends on several factors, including availability, condition, and presentation. Do some research before selling (or buying) any sewing machine. For example, check eBay or craigslist to see how many identical machines are currently for sale. Use the "completed listings" feature on eBay to see the selling price, not the asking price. Make an honest assessment of your machine's condition compared to others like it. If yours is dirty or scratched or in need of repair then you can't expect to sell it for top dollar.
Hope this information helps you and thank you for visiting!
Thanks for asking. We get many inquiries like yours and the simple answer is: your machine is worth whatever someone is willing to pay for it.
I know you were looking for a dollar amount, but it isn't possible to appraise a machine without seeing, touching, and testing it. We just don't have the manpower to provide specific appraisal services.
I have the Centennial edition, just as you described it. It is still in the original cabinet, with matching stool. I also have the original instruction booklet, and have the green plastic case of attachments. It belonged to my Mother and I will never part with it!
Somewhere I have a 15-91, which is the original Little Tailor, manufactured for cottage industry, i.e. piecework. This is the first home domestic made with the differential gear. If I cannot find my 15-91, I am buying another one, as I found it to be that good to sew with. Thank you for this article.
Virginia: Can you tell me the difference between a 301 and a 15-91. I just received a 301 and though it is the perfect machine for piecing. The stitch is fabulous.
Hi. I have a question about a Singer folding knife in brass commemorating the 100 year anniversery of the company. Any idea in what context this was given out? The initials J.B.S is written on the bottom of the blade. I can find nothing AT ALL, and I've really tried. Would greatly appreciate your help.
I recently bought a singer treadle with the centennial badge on it but I can't match the serial number to any thing . Can someone please tell me what I bought JG346142 is the serial number and it has the centennial badge?
Thank you for the information. I bought a Singer M.R. with the blue-ringed Centennial badge and JC..... serial number. Thank you for all the information. I just tried to use it and it is not working right. I am not sure if I have everything set up right as this is the first time I am using a treadle machine. What I would really like to know is where I can get a copy of the original instruction booklet. I did phone Singer, with the number you listed in someone's reply, but they referred me to a repair person in a nearby town. They are on vacation and so I have left a message. Do you have anymore information? Thank you.
I believe I have one of the blue centennial machines. I got it last week. I had a friend google the S/N and said it was produced in 1950. I totally love the machine as it sews like a demon still .... it might be priceless money wise... but to me its priceless little gem ... she is so pretty... I will look up the information to see if my gals the true thing or not... thanks for the info.
Today I received a 301 with a centennial badge. Serial # NA081750. Is it manufactured prior to 1951 or earlier? Thank you for the artical and feedback.
My machine has a round silver disk with just a plain red "S" inside the disk and the same thing on the foot pedal just a silver disk with the plain red "S"it has the centennial badge but the serial # is AJ689547 which makes in made in 6/13/50 in N.J.is this considered a centennial?
Hi Barbara, I have a treadle Singer with the blue ringed centennial badge. The serial no. is MA897674 (not sure about the last number tho as it is faded). I was hoping you might be able to tell me its age, and if it was made after 1950, why were they still making treadle machines?
I have a Centennial 15-91. My serial number is JC843869 - which I think means it was made in St. Johns. How can I find out when if was commissioned or made? Do we assume it was between 948-1951? Thanks!


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This Pendant Necklace (SJHP0010TIGER-SS) is made with Solid .925 Sterling Silver (925/1000) and Gemstone embellished in this Pendant Necklace is 100% Natural Tigers Eye. Gemstone Size is 5x7mm Pear and total weight of Gemstone is approximately 4.68 carats. 18 Inches Sterling Silver Chain is included with every Pendant.