Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("aimarsg/bert-finetuned-ner-1")
model = AutoModelForTokenClassification.from_pretrained("aimarsg/bert-finetuned-ner-1")Quick Links
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the xglue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2202
- Precision: 0.6038
- Recall: 0.6720
- F1: 0.6361
- Accuracy: 0.9489
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 191 | 0.2359 | 0.5659 | 0.6309 | 0.5967 | 0.9397 |
| No log | 2.0 | 382 | 0.2136 | 0.5754 | 0.6681 | 0.6183 | 0.9464 |
| 0.1605 | 3.0 | 573 | 0.2202 | 0.6038 | 0.6720 | 0.6361 | 0.9489 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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Dataset used to train aimarsg/bert-finetuned-ner-1
Evaluation results
- Precision on xglueself-reported0.604
- Recall on xglueself-reported0.672
- F1 on xglueself-reported0.636
- Accuracy on xglueself-reported0.949
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aimarsg/bert-finetuned-ner-1")