File size: 2,032 Bytes
de6462f |
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: 1.1545
- Precision: 0.2718
- Recall: 0.2523
- F1: 0.2617
- Accuracy: 0.8754
## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1562 | 0.4292 | 100 | 1.0438 | 0.3433 | 0.1900 | 0.2447 | 0.8973 |
| 0.1346 | 0.8584 | 200 | 1.0574 | 0.3029 | 0.2305 | 0.2618 | 0.8862 |
| 0.1116 | 1.2876 | 300 | 1.4601 | 0.4197 | 0.1194 | 0.1859 | 0.9085 |
| 0.1141 | 1.7167 | 400 | 1.0446 | 0.2705 | 0.2565 | 0.2633 | 0.8744 |
| 0.1047 | 2.1459 | 500 | 1.1404 | 0.2783 | 0.2710 | 0.2746 | 0.8747 |
| 0.103 | 2.5751 | 600 | 1.3562 | 0.3015 | 0.1869 | 0.2308 | 0.8909 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|