Token Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("autoevaluate/entity-extraction")
model = AutoModelForTokenClassification.from_pretrained("autoevaluate/entity-extraction")Quick Links
entity-extraction
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0808
- Precision: 0.8863
- Recall: 0.9085
- F1: 0.8972
- Accuracy: 0.9775
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2552 | 1.0 | 878 | 0.0808 | 0.8863 | 0.9085 | 0.8972 | 0.9775 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Downloads last month
- 19
Datasets used to train autoevaluate/entity-extraction
Evaluation results
- Precision on conll2003self-reported0.886
- Recall on conll2003self-reported0.908
- F1 on conll2003self-reported0.897
- Accuracy on conll2003self-reported0.977
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="autoevaluate/entity-extraction")