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bert-finetuned-ner
amannagrawall002/bert-finetuned-ner
bert-finetuned-ner is a machine learning model from amannagrawall002. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Aug 11, 2024
- Updated
- Aug 12, 2024
- SHA
- 034dda3c
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0741
- Precision: 0.9341
- Recall: 0.9520
- F1: 0.9430
- Accuracy: 0.9867
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0775 | 1.0 | 1756 | 0.0694 | 0.8912 | 0.9273 | 0.9089 | 0.9817 |
| 0.0377 | 2.0 | 3512 | 0.0707 | 0.9245 | 0.9445 | 0.9344 | 0.9850 |
| 0.0243 | 3.0 | 5268 | 0.0671 | 0.9281 | 0.9465 | 0.9372 | 0.9855 |
| 0.0145 | 4.0 | 7024 | 0.0734 | 0.9353 | 0.9507 | 0.9429 | 0.9859 |
| 0.006 | 5.0 | 8780 | 0.0741 | 0.9341 | 0.9520 | 0.9430 | 0.9867 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
How to use and it's democase
from transformers import pipeline
model_checkpoint = "amannagrawall002/bert-finetuned-ner" token_classifier = pipeline( "token-classification", model=model_checkpoint, aggregation_strategy="simple" )
print(token_classifier("My name is Sylvain and I work at Hugging Face in Brooklyn."))
[{'entity_group': 'PER', 'score': 0.9997023, 'word': 'Sylvain', 'start': 11, 'end': 18}, {'entity_group': 'ORG', 'score': 0.995275, 'word': 'Hugging Face', 'start': 33, 'end': 45}, {'entity_group': 'LOC', 'score': 0.9987465, 'word': 'Brooklyn', 'start': 49, 'end': 57}]
- Type
- bert
- License
- apache-2.0
- Created
- Aug 11, 2024
- Updated
- Aug 12, 2024