metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-ner_cv
results: []
distilbert-base-uncased-ner_cv
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8548
- Precision: 0.3327
- Recall: 0.2358
- F1: 0.2760
- Accuracy: 0.7815
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: 1e-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
- lr_scheduler_warmup_steps: 20
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 5.0 | 30 | 1.0790 | 0.0 | 0.0 | 0.0 | 0.7537 |
No log | 10.0 | 60 | 0.9589 | 0.3208 | 0.1207 | 0.1754 | 0.7677 |
No log | 15.0 | 90 | 0.8975 | 0.3363 | 0.1591 | 0.2160 | 0.7773 |
No log | 20.0 | 120 | 0.8675 | 0.3354 | 0.2259 | 0.2699 | 0.7786 |
No log | 25.0 | 150 | 0.8568 | 0.3333 | 0.2443 | 0.2820 | 0.7811 |
No log | 30.0 | 180 | 0.8548 | 0.3327 | 0.2358 | 0.2760 | 0.7815 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.8.1+cu111
- Datasets 1.6.2
- Tokenizers 0.12.1