File size: 2,032 Bytes
d8505ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7669
- Precision: 0.2852
- Recall: 0.2420
- F1: 0.2618
- Accuracy: 0.8806
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0387 | 0.4292 | 100 | 3.2626 | 0.2781 | 0.2316 | 0.2527 | 0.8801 |
| 0.0432 | 0.8584 | 200 | 4.3510 | 0.3575 | 0.1485 | 0.2098 | 0.9021 |
| 0.0305 | 1.2876 | 300 | 4.4340 | 0.3663 | 0.1578 | 0.2206 | 0.9024 |
| 0.0303 | 1.7167 | 400 | 4.2810 | 0.3418 | 0.1537 | 0.2120 | 0.9000 |
| 0.0347 | 2.1459 | 500 | 4.3217 | 0.3607 | 0.1828 | 0.2426 | 0.9001 |
| 0.0235 | 2.5751 | 600 | 4.3738 | 0.3302 | 0.1817 | 0.2344 | 0.8961 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|