Downloads · 30 days
6
10% of all-time downloads
xlm-roberta-base_esp-hau-eng_train_spearman_corr
shanhy/xlm-roberta-base_esp-hau-eng_train_spearman_corr
xlm-roberta-base_esp-hau-eng_train_spearman_corr is a text classification model from shanhy. 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
59
Public
Parameters
278M
1.1 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.1 GB · 98%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Feb 16, 2024
- Updated
- Feb 16, 2024
- SHA
- ac2efa2e
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. -->
xlm-roberta-base_esp-hau-eng_train_spearman_corr
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0326
- Spearman Corr: 0.7185
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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|---|---|---|---|---|
| No log | 0.48 | 200 | 0.0302 | 0.6884 |
| No log | 0.97 | 400 | 0.0260 | 0.7104 |
| No log | 1.45 | 600 | 0.0263 | 0.7087 |
| No log | 1.94 | 800 | 0.0335 | 0.7321 |
| 0.0348 | 2.42 | 1000 | 0.0320 | 0.7267 |
| 0.0348 | 2.91 | 1200 | 0.0362 | 0.7322 |
| 0.0348 | 3.39 | 1400 | 0.0336 | 0.7274 |
| 0.0348 | 3.88 | 1600 | 0.0306 | 0.7201 |
| 0.0214 | 4.36 | 1800 | 0.0307 | 0.7186 |
| 0.0214 | 4.85 | 2000 | 0.0325 | 0.7358 |
| 0.0214 | 5.33 | 2200 | 0.0305 | 0.7363 |
| 0.0214 | 5.82 | 2400 | 0.0365 | 0.7410 |
| 0.0146 | 6.3 | 2600 | 0.0298 | 0.7279 |
| 0.0146 | 6.79 | 2800 | 0.0358 | 0.7292 |
| 0.0146 | 7.27 | 3000 | 0.0370 | 0.7143 |
| 0.0146 | 7.76 | 3200 | 0.0346 | 0.7223 |
| 0.0103 | 8.24 | 3400 | 0.0297 | 0.7304 |
| 0.0103 | 8.73 | 3600 | 0.0309 | 0.7132 |
| 0.0103 | 9.21 | 3800 | 0.0369 | 0.7249 |
| 0.0103 | 9.7 | 4000 | 0.0326 | 0.7185 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Feb 16, 2024
- Updated
- Feb 16, 2024