Downloads · 30 days
6
2% of all-time downloads
absa-train-service-roberta-large
nguyenanh2803/absa-train-service-roberta-large
absa-train-service-roberta-large is a text classification model from nguyenanh2803. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
244
Public
Parameters
278M
68.9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.5 GB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jul 26, 2024
- Updated
- Jul 26, 2024
- SHA
- a574e1c5
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/> <img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>
absa-train-service-roberta-large
This model is a fine-tuned version of FacebookAI/roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8683
- Accuracy: 0.7424
- Precision: 0.7345
- Recall: 0.7367
- F1: 0.7302
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: 0.0002
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 2.2255 | 1.0 | 469 | 2.0677 | 0.3296 | 0.1937 | 0.3250 | 0.2297 |
| 1.8236 | 2.0 | 938 | 1.7061 | 0.504 | 0.5413 | 0.4914 | 0.4567 |
| 1.5384 | 3.0 | 1407 | 1.4381 | 0.552 | 0.5944 | 0.5549 | 0.5196 |
| 1.4301 | 4.0 | 1876 | 1.3316 | 0.5984 | 0.6000 | 0.5990 | 0.5618 |
| 1.3776 | 5.0 | 2345 | 1.1645 | 0.6576 | 0.6817 | 0.6491 | 0.6332 |
| 1.2078 | 6.0 | 2814 | 1.0967 | 0.6448 | 0.7035 | 0.6348 | 0.6110 |
| 1.2535 | 7.0 | 3283 | 1.0565 | 0.7008 | 0.7467 | 0.6967 | 0.7066 |
| 1.2921 | 8.0 | 3752 | 1.0049 | 0.6976 | 0.7013 | 0.6884 | 0.6813 |
| 1.178 | 9.0 | 4221 | 1.0438 | 0.648 | 0.7746 | 0.6423 | 0.6387 |
| 1.2324 | 10.0 | 4690 | 1.0203 | 0.6896 | 0.7096 | 0.6831 | 0.6704 |
| 1.1899 | 11.0 | 5159 | 1.0193 | 0.6864 | 0.7391 | 0.6819 | 0.6834 |
| 1.1515 | 12.0 | 5628 | 0.9722 | 0.6944 | 0.7164 | 0.6924 | 0.6860 |
| 1.1604 | 13.0 | 6097 | 0.9372 | 0.7312 | 0.7543 | 0.7311 | 0.7259 |
| 1.1229 | 14.0 | 6566 | 0.9265 | 0.72 | 0.7278 | 0.7139 | 0.7147 |
| 1.1459 | 15.0 | 7035 | 0.8896 | 0.7376 | 0.7264 | 0.7323 | 0.7183 |
| 1.1281 | 16.0 | 7504 | 0.9074 | 0.7152 | 0.7107 | 0.7087 | 0.7012 |
| 1.1794 | 17.0 | 7973 | 0.8914 | 0.7424 | 0.7293 | 0.7354 | 0.7266 |
| 1.1101 | 18.0 | 8442 | 0.8707 | 0.7216 | 0.7161 | 0.7141 | 0.7059 |
| 1.1215 | 19.0 | 8911 | 0.8656 | 0.7408 | 0.7322 | 0.7348 | 0.7274 |
| 1.0483 | 20.0 | 9380 | 0.8683 | 0.7424 | 0.7345 | 0.7367 | 0.7302 |
Framework versions
- Transformers 4.43.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jul 26, 2024
- Updated
- Jul 26, 2024