Update README.md

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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/dslim/bert-base-NER/README.md

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+*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,120 @@
+---
+language: en
+datasets:
+- conll2003
+license: mit
+---
+# bert-base-NER
+
+## 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. 
+
+
+## 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-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
+I-MIS | 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",
+}
+```

config.json

@@ -1,10 +1,9 @@
 {
+  "_num_labels": 9,
   "architectures": [
     "BertForTokenClassification"
   ],
   "attention_probs_dropout_prob": 0.1,
-  "do_sample": false,
-  "finetuning_task": null,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
@@ -21,7 +20,6 @@
   },
   "initializer_range": 0.02,
   "intermediate_size": 3072,
-  "is_decoder": false,
   "label2id": {
     "B-LOC": 7,
     "B-MISC": 1,
@@ -34,26 +32,12 @@
     "O": 0
   },
   "layer_norm_eps": 1e-12,
-  "length_penalty": 1.0,
-  "max_length": 20,
   "max_position_embeddings": 512,
   "model_type": "bert",
   "num_attention_heads": 12,
-  "num_beams": 1,
   "num_hidden_layers": 12,
-  "num_labels": 9,
-  "num_return_sequences": 1,
-  "output_attentions": false,
-  "output_hidden_states": false,
   "output_past": true,
   "pad_token_id": 0,
-  "pruned_heads": {},
-  "repetition_penalty": 1.0,
-  "temperature": 1.0,
-  "top_k": 50,
-  "top_p": 1.0,
-  "torchscript": false,
   "type_vocab_size": 2,
-  "use_bfloat16": false,
   "vocab_size": 28996
-}
+}