Duplicate from dslim/bert-base-NER

Co-authored-by: David S. Lim <dslim@users.noreply.huggingface.co>

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+language: en
+datasets:
+- conll2003
+license: mit
+model-index:
+- name: dslim/bert-base-NER
+  results:
+  - task:
+      type: token-classification
+      name: Token Classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: test
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9118041001560013
+      verified: true
+    - name: Precision
+      type: precision
+      value: 0.9211550382257732
+      verified: true
+    - name: Recall
+      type: recall
+      value: 0.9306415698281261
+      verified: true
+    - name: F1
+      type: f1
+      value: 0.9258740048459675
+      verified: true
+    - name: loss
+      type: loss
+      value: 0.48325642943382263
+      verified: true
+---
+# bert-base-NER
+
+If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks!
+
+<a href="https://www.buymeacoffee.com/dslim" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/arial-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
+
+## Model description
+
+**bert-base-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** and achieves **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). 
+
+Specifically, this model is a *bert-base-cased* model that was fine-tuned on the English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
+
+If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a [**bert-large-NER**](https://huggingface.co/dslim/bert-large-NER/) version is also available. 
+
+### Available NER models 
+| Model Name | Description | Parameters |
+|-------------------|-------------|------------------|
+| [distilbert-NER](https://huggingface.co/dslim/distilbert-NER) **(NEW!)** | Fine-tuned DistilBERT - a smaller, faster, lighter version of BERT | 66M |
+| [bert-large-NER](https://huggingface.co/dslim/bert-large-NER/) | Fine-tuned bert-large-cased - larger model with slightly better performance | 340M |
+| [bert-base-NER](https://huggingface.co/dslim/bert-base-NER)-([uncased](https://huggingface.co/dslim/bert-base-NER-uncased)) | Fine-tuned bert-base, available in both cased and uncased versions | 110M |
+
+
+## Intended uses & limitations
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Wolfgang and I live in Berlin"
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+#### Limitations and bias
+
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 
+
+## Training data
+
+This model was fine-tuned on English version of the standard [CoNLL-2003 Named Entity Recognition](https://www.aclweb.org/anthology/W03-0419.pdf) dataset. 
+
+The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
+
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
+I-MISC | Miscellaneous entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organization right after another organization
+I-ORG |organization
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+
+
+### CoNLL-2003 English Dataset Statistics
+This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper. 
+#### # of training examples per entity type
+Dataset|LOC|MISC|ORG|PER
+-|-|-|-|-
+Train|7140|3438|6321|6600
+Dev|1837|922|1341|1842
+Test|1668|702|1661|1617
+#### # of articles/sentences/tokens per dataset
+Dataset |Articles |Sentences |Tokens
+-|-|-|-
+Train |946 |14,987 |203,621
+Dev |216 |3,466 |51,362
+Test |231 |3,684 |46,435
+
+## Training procedure
+
+This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original BERT paper](https://arxiv.org/pdf/1810.04805) which trained & evaluated the model on CoNLL-2003 NER task. 
+
+## Eval results
+metric|dev|test
+-|-|-
+f1 |95.1 |91.3
+precision |95.0 |90.7
+recall |95.3 |91.9
+
+The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF. More on replicating the original results [here](https://github.com/google-research/bert/issues/223).
+
+### BibTeX entry and citation info
+
+```
+@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}
+}
+```
+```
+@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
+    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.  and
+      De Meulder, Fien",
+    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
+    year = "2003",
+    url = "https://www.aclweb.org/anthology/W03-0419",
+    pages = "142--147",
+}
+```

added_tokens.json

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config.json

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