update README

README.md

@@ -6,8 +6,18 @@ metrics:
 pipeline_tag: fill-mask
 tags:
 - not-for-all-audiences
+- abusive language
+- hate speech
+- offensive language
+widget:
+- text: She is a [MASK].
+  example_title: Misogyny
+- text: He is a [MASK].
+  example_title: Misandry
 ---
 
+**WARNING: Some language produced by this model and README may offend. The model intent is to facilitate bias in AI research**
+
 # sexistBERT base model (uncased)
 
 Re-pretrained model on English language using a Masked Language Modeling (MLM)
@@ -98,8 +108,14 @@ Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('clincolnoz/sexistBERT_temp')
-model = BertModel.from_pretrained("clincolnoz/sexistBERT_temp")
+tokenizer = BertTokenizer.from_pretrained(
+  'clincolnoz/sexistBERT_temp',
+  revision='state at epcoh 20' # tag name, or branch name, or commit hash
+)
+model = BertModel.from_pretrained(
+  'clincolnoz/sexistBERT_temp',
+  revision='state at epcoh 20' # tag name, or branch name, or commit hash
+)
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -109,8 +125,15 @@ and in TensorFlow:
 
 ```python
 from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('clincolnoz/sexistBERT_temp')
-model = TFBertModel.from_pretrained("clincolnoz/sexistBERT_temp", from_pt=True)
+tokenizer = BertTokenizer.from_pretrained(
+  'clincolnoz/sexistBERT_temp',
+  revision='state at epcoh 20' # tag name, or branch name, or commit hash
+)
+model = TFBertModel.from_pretrained(
+  'clincolnoz/sexistBERT_temp',
+  from_pt=True,
+  revision='state at epcoh 20' # tag name, or branch name, or commit hash
+)
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)
@@ -187,7 +210,7 @@ headers). -->
 For the NSP task the data were preprocessed by splitting documents into sentences to create first a bag of sentences and then to create pairs of sentences, where Sentence B either corresponded to a consecutive sentence in the text or randomly select from the bag. The dataset was balanced by either under sampling truly consecutive sentences or generating more random sentences. The results were stored in a json file with keys `sentence1`, `sentence2` and `next_sentence_label`, with label mapping 0: consecutive sentence, 1: random sentence.
 
 The texts are lowercased and tokenized using WordPiece and a vocabulary size of
-30,256. The inputs of the model are then of the form:
+30,778. The inputs of the model are then of the form:
 
 ```
 [CLS] Sentence A [SEP] Sentence B [SEP]
@@ -225,25 +248,17 @@ Glue test results:
 |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
 |      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    | -->
 
+### Framework versions
+
+- Transformers 4.27.0.dev0
+- Pytorch 1.13.1+cu117
+- Datasets 2.9.0
+- Tokenizers 0.13.2
 
 <!-- ### BibTeX entry and citation info -->
 
 <!-- ```bibtex
-@article{DBLP:journals/corr/abs-1810-04805,
-  author    = {Jacob Devlin and
-               Ming{-}Wei Chang and
-               Kenton Lee and
-               Kristina Toutanova},
-  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
-               Understanding},
-  journal   = {CoRR},
-  volume    = {abs/1810.04805},
-  year      = {2018},
-  url       = {http://arxiv.org/abs/1810.04805},
-  archivePrefix = {arXiv},
-  eprint    = {1810.04805},
-  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
+@article{
+  
 }
 ``` -->