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roberta-large-finetuned-chunking
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4192
- Precision: 0.3222
- Recall: 0.3161
- F1: 0.3191
- Accuracy: 0.8632
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: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0373 | 1.0 | 2498 | 0.9545 | 0.3166 | 0.2545 | 0.2822 | 0.8656 |
0.0045 | 2.0 | 4996 | 1.1324 | 0.2667 | 0.3142 | 0.2885 | 0.8525 |
0.0022 | 3.0 | 7494 | 1.3138 | 0.3349 | 0.3097 | 0.3218 | 0.8672 |
0.0015 | 4.0 | 9992 | 1.3454 | 0.3261 | 0.3260 | 0.3260 | 0.8647 |
0.0014 | 5.0 | 12490 | 1.3640 | 0.3064 | 0.3126 | 0.3095 | 0.8603 |
0.0008 | 6.0 | 14988 | 1.4192 | 0.3222 | 0.3161 | 0.3191 | 0.8632 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1