.gitattributes

@@ -6,4 +6,3 @@
 *.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
-*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,120 +0,0 @@
----
-language: en
-datasets:
-- conll2003
-license: mit
----
-# bert-base-NER
-
-## Model description
-
-**bert-large-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-large-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 smaller BERT model fine-tuned on the same dataset, a [**bert-base-NER**](https://huggingface.co/dslim/bert-base-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 |95.7 |91.7
-precision |95.3 |91.2
-recall |96.1 |92.3
-
-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

@@ -1,67 +0,0 @@
-{
-  "architectures": [
-    "BertForTokenClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": null,
-  "directionality": "bidi",
-  "do_sample": false,
-  "eos_token_ids": null,
-  "finetuning_task": null,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "id2label": {
-    "0": "O",
-    "1": "B-MISC",
-    "2": "I-MISC",
-    "3": "B-PER",
-    "4": "I-PER",
-    "5": "B-ORG",
-    "6": "I-ORG",
-    "7": "B-LOC",
-    "8": "I-LOC"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "is_decoder": false,
-  "label2id": {
-    "B-LOC": 7,
-    "B-MISC": 1,
-    "B-ORG": 5,
-    "B-PER": 3,
-    "I-LOC": 8,
-    "I-MISC": 2,
-    "I-ORG": 6,
-    "I-PER": 4,
-    "O": 0
-  },
-  "layer_norm_eps": 1e-12,
-  "length_penalty": 1.0,
-  "max_length": 20,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 16,
-  "num_beams": 1,
-  "num_hidden_layers": 24,
-  "num_labels": 9,
-  "num_return_sequences": 1,
-  "output_attentions": false,
-  "output_hidden_states": false,
-  "output_past": true,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "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
-}

special_tokens_map.json

@@ -1 +0,0 @@
-{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}