transformers pytorch roberta token-classification dataset:tner/tweetner7 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
pipeline_tag: token-classification
widget:
- text: 'Get the all-analog Classic Vinyl Edition of `Takin'' Off` Album from {@herbiehancock@}
    via {@bluenoterecords@} link below: {{URL}}'
  example_title: NER Example 1
model-index:
- name: tner/roberta-large-tweetner7-random
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: tner/tweetner7
      type: tner/tweetner7
      args: tner/tweetner7
    metrics:
    - type: f1
      value: 0.6632769652650823
      name: F1 (test_2021)
    - type: precision
      value: 0.6554878048780488
      name: Precision (test_2021)
    - type: recall
      value: 0.6712534690101758
      name: Recall (test_2021)
    - type: f1_macro
      value: 0.6096477771855761
      name: Macro F1 (test_2021)
    - type: precision_macro
      value: 0.6042443991246051
      name: Macro Precision (test_2021)
    - type: recall_macro
      value: 0.6191008735553379
      name: Macro Recall (test_2021)
    - type: f1_entity_span
      value: 0.7900359938296291
      name: Entity Span F1 (test_2021)
    - type: precision_entity_span
      value: 0.780713640469738
      name: Entity Span Precision (test_2020)
    - type: recall_entity_span
      value: 0.7995836706372152
      name: Entity Span Recall (test_2021)
    - type: f1
      value: 0.6439847577572129
      name: F1 (test_2020)
    - type: precision
      value: 0.6771608471665712
      name: Precision (test_2020)
    - type: recall
      value: 0.6139076284379865
      name: Recall (test_2020)
    - type: f1_macro
      value: 0.6008744778169367
      name: Macro F1 (test_2020)
    - type: precision_macro
      value: 0.6358142893696356
      name: Macro Precision (test_2020)
    - type: recall_macro
      value: 0.5742193301311931
      name: Macro Recall (test_2020)
    - type: f1_entity_span
      value: 0.7552409474543968
      name: Entity Span F1 (test_2020)
    - type: precision_entity_span
      value: 0.7943871706758304
      name: Entity Span Precision (test_2020)
    - type: recall_entity_span
      value: 0.7197716658017644
      name: Entity Span Recall (test_2020)
---
# tner/roberta-large-tweetner7-random

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_random` split).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set of 2021:
- F1 (micro): 0.6632769652650823
- Precision (micro): 0.6554878048780488
- Recall (micro): 0.6712534690101758
- F1 (macro): 0.6096477771855761
- Precision (macro): 0.6042443991246051
- Recall (macro): 0.6191008735553379



The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5224148236700539
- creative_work: 0.45186640471512773
- event: 0.4894837476099427
- group: 0.6327722432153899
- location: 0.6692258477287268
- person: 0.838405036726128
- product: 0.6633663366336633 

For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro): 
    - 90%: [0.6546824558783396, 0.6722355436189195]
    - 95%: [0.6527609558375069, 0.6741666937877734] 
- F1 (macro): 
    - 90%: [0.6546824558783396, 0.6722355436189195]
    - 95%: [0.6527609558375069, 0.6741666937877734] 

Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-random/raw/main/eval/metric.json) 
and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-random/raw/main/eval/metric_span.json).

### Usage
This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip.   
```shell
pip install tner
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are 
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.  

```python
import re
from urlextract import URLExtract
from tner import TransformersNER

extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet


text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/roberta-large-tweetner7-random")
model.predict([text_format])
```
It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.

### Training hyperparameters

The following hyperparameters were used during training:
 - dataset: ['tner/tweetner7']
 - dataset_split: train_random
 - dataset_name: None
 - local_dataset: None
 - model: roberta-large
 - crf: True
 - max_length: 128
 - epoch: 30
 - batch_size: 32
 - lr: 1e-05
 - random_seed: 0
 - gradient_accumulation_steps: 1
 - weight_decay: 1e-07
 - lr_warmup_step_ratio: 0.15
 - max_grad_norm: 1

The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-tweetner7-random/raw/main/trainer_config.json).

### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```

@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.7",
    doi = "10.18653/v1/2021.eacl-demos.7",
    pages = "53--62",
    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}

```
- TweetNER7
```

@inproceedings{ushio-etal-2022-tweet,
    title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
    author = "Ushio, Asahi  and
        Neves, Leonardo  and
        Silva, Vitor  and
        Barbieri, Francesco. and
        Camacho-Collados, Jose",
    booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}

```


