File size: 3,045 Bytes
332d0b3 034dda3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9341149273447821
- name: Recall
type: recall
value: 0.9520363513968361
- name: F1
type: f1
value: 0.9429904984164028
- name: Accuracy
type: accuracy
value: 0.9866515570730559
---
<!-- 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](https://huggingface.co/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}]
|