metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_all_JGTt
results: []
my_awesome_wnut_all_JGTt
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0375
- Precision: 0.4267
- Recall: 0.3478
- F1: 0.3832
- Accuracy: 0.9916
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 251 | 0.0280 | 0.4259 | 0.25 | 0.3151 | 0.9918 |
| 0.0294 | 2.0 | 502 | 0.0291 | 0.4324 | 0.3478 | 0.3855 | 0.9916 |
| 0.0294 | 3.0 | 753 | 0.0355 | 0.4267 | 0.3478 | 0.3832 | 0.9915 |
| 0.0084 | 4.0 | 1004 | 0.0375 | 0.4267 | 0.3478 | 0.3832 | 0.9916 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2