File size: 2,891 Bytes
a38dba2 9f09891 826f2fa 34d5be3 a38dba2 9f09891 a38dba2 826f2fa a38dba2 34d5be3 a38dba2 | 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 | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- 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-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2481
- Model Preparation Time: 0.0021
```ipython
precision recall f1-score support
B-COMMENT 0.88 0.90 0.89 767
B-NAME 0.91 0.91 0.91 1050
B-QTY 0.99 0.99 0.99 836
B-RANGE_END 0.93 1.00 0.96 13
B-UNIT 0.99 0.99 0.99 706
I-COMMENT 0.93 0.96 0.95 1499
I-NAME 0.92 0.84 0.88 572
OTHER 0.88 0.82 0.85 439
accuracy 0.93 5882
macro avg 0.93 0.93 0.93 5882
weighted avg 0.93 0.93 0.93 5882
```
## Usage
```ipython
In [40]: from transformers import pipeline
In [41]:
In [41]: classifier = pipeline("token-classification", "AXKuhta/bert-finetuned-ner")
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 199/199 [00:00<00:00, 9724.08it/s]
In [42]: classifier.tokenizer.tokenize("mozzarella")
Out[42]: ['mozzarella']
In [43]: classifier("1 pound broccoli", aggregation_strategy="simple")
Out[43]:
[{'entity_group': 'QTY',
'score': np.float32(0.9997178),
'word': '1',
'start': 0,
'end': 1},
{'entity_group': 'UNIT',
'score': np.float32(0.99991345),
'word': 'pound',
'start': 2,
'end': 7},
{'entity_group': 'NAME',
'score': np.float32(0.999385),
'word': 'broccoli',
'start': 8,
'end': 16}]
```
## Training procedure
See bert_finetune_fin.ipynb
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Framework versions
- Transformers 5.6.2
- Pytorch 2.11.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.2
|