metadata
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert_v5
results: []
Bert_v5
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9191
- Precision: 0.7612
- Recall: 0.8007
- F1: 0.5106
- Accuracy: 0.7357
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.0663 | 1.0 | 934 | 0.8636 | 0.6973 | 0.8467 | 0.4082 | 0.7023 |
| 0.8354 | 2.0 | 1868 | 0.8261 | 0.7367 | 0.8086 | 0.4733 | 0.7221 |
| 0.7164 | 3.0 | 2802 | 0.7737 | 0.7572 | 0.7988 | 0.5055 | 0.7347 |
| 0.6149 | 4.0 | 3736 | 0.7542 | 0.7488 | 0.8402 | 0.5176 | 0.7438 |
| 0.5153 | 5.0 | 4670 | 0.8185 | 0.7614 | 0.8123 | 0.5017 | 0.7389 |
| 0.4314 | 6.0 | 5604 | 0.8599 | 0.7543 | 0.8259 | 0.5085 | 0.7395 |
| 0.3689 | 7.0 | 6538 | 0.9191 | 0.7612 | 0.8007 | 0.5106 | 0.7357 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1