Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/dslim/bert-base-NER/README.md
README.md
ADDED
@@ -0,0 +1,114 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
language: en
|
3 |
+
datasets:
|
4 |
+
- conll2003
|
5 |
+
---
|
6 |
+
# bert-base-NER
|
7 |
+
|
8 |
+
## Model description
|
9 |
+
|
10 |
+
**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).
|
11 |
+
|
12 |
+
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.
|
13 |
+
## Intended uses & limitations
|
14 |
+
|
15 |
+
#### How to use
|
16 |
+
|
17 |
+
You can use this model with Transformers *pipeline* for NER.
|
18 |
+
|
19 |
+
```python
|
20 |
+
from transformers import AutoTokenizer, AutoModelForTokenClassification
|
21 |
+
from transformers import pipeline
|
22 |
+
|
23 |
+
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
|
24 |
+
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
|
25 |
+
|
26 |
+
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
|
27 |
+
example = "My name is Wolfgang and I live in Berlin"
|
28 |
+
|
29 |
+
ner_results = nlp(example)
|
30 |
+
print(ner_results)
|
31 |
+
```
|
32 |
+
|
33 |
+
#### Limitations and bias
|
34 |
+
|
35 |
+
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.
|
36 |
+
|
37 |
+
## Training data
|
38 |
+
|
39 |
+
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.
|
40 |
+
|
41 |
+
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:
|
42 |
+
Abbreviation|Description
|
43 |
+
-|-
|
44 |
+
O|Outside of a named entity
|
45 |
+
B-MIS |Beginning of a miscellaneous entity right after another miscellaneous entity
|
46 |
+
I-MIS |Miscellaneous entity
|
47 |
+
B-PER |Beginning of a person’s name right after another person’s name
|
48 |
+
I-PER |Person’s name
|
49 |
+
B-ORG |Beginning of an organisation right after another organisation
|
50 |
+
I-ORG |Organisation
|
51 |
+
B-LOC |Beginning of a location right after another location
|
52 |
+
I-LOC |Location
|
53 |
+
|
54 |
+
|
55 |
+
### CoNLL-2003 English Dataset Statistics
|
56 |
+
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.
|
57 |
+
#### # of training examples per entity type
|
58 |
+
Dataset|LOC|MISC|ORG|PER
|
59 |
+
-|-|-|-|-
|
60 |
+
Train|7140|3438|6321|6600
|
61 |
+
Dev|1837|922|1341|1842
|
62 |
+
Test|1668|702|1661|1617
|
63 |
+
#### # of articles/sentences/tokens per dataset
|
64 |
+
Dataset |Articles |Sentences |Tokens
|
65 |
+
-|-|-|-
|
66 |
+
Train |946 |14,987 |203,621
|
67 |
+
Dev |216 |3,466 |51,362
|
68 |
+
Test |231 |3,684 |46,435
|
69 |
+
|
70 |
+
## Training procedure
|
71 |
+
|
72 |
+
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.
|
73 |
+
|
74 |
+
## Eval results
|
75 |
+
metric|dev|test
|
76 |
+
-|-|-
|
77 |
+
f1 |95.1 |91.3
|
78 |
+
precision |95.0 |90.7
|
79 |
+
recall |95.3 |91.9
|
80 |
+
|
81 |
+
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).
|
82 |
+
|
83 |
+
### BibTeX entry and citation info
|
84 |
+
|
85 |
+
```
|
86 |
+
@article{DBLP:journals/corr/abs-1810-04805,
|
87 |
+
author = {Jacob Devlin and
|
88 |
+
Ming{-}Wei Chang and
|
89 |
+
Kenton Lee and
|
90 |
+
Kristina Toutanova},
|
91 |
+
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
|
92 |
+
Understanding},
|
93 |
+
journal = {CoRR},
|
94 |
+
volume = {abs/1810.04805},
|
95 |
+
year = {2018},
|
96 |
+
url = {http://arxiv.org/abs/1810.04805},
|
97 |
+
archivePrefix = {arXiv},
|
98 |
+
eprint = {1810.04805},
|
99 |
+
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
|
100 |
+
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
|
101 |
+
bibsource = {dblp computer science bibliography, https://dblp.org}
|
102 |
+
}
|
103 |
+
```
|
104 |
+
```
|
105 |
+
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
|
106 |
+
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
|
107 |
+
author = "Tjong Kim Sang, Erik F. and
|
108 |
+
De Meulder, Fien",
|
109 |
+
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
|
110 |
+
year = "2003",
|
111 |
+
url = "https://www.aclweb.org/anthology/W03-0419",
|
112 |
+
pages = "142--147",
|
113 |
+
}
|
114 |
+
```
|