Downloads · 30 days
18
6% of all-time downloads
bge-small-en-v1.5-quality-weight-0.7
gutsartificial/bge-small-en-v1.5-quality-weight-0.7
bge-small-en-v1.5-quality-weight-0.7 is a text classification model from gutsartificial. 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
290
Public
Parameters
33.4M
133 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors133 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Dec 8, 2024
- Updated
- Dec 9, 2024
- SHA
- e4b48e67
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. -->
bge-small-en-v1.5-2024-12-08_15-44-34-quality-weight-0.7
This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0187
- Spearman: 0.9299
- Pearson: 0.9262
- Mse: 0.0187
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman | Pearson | Mse |
|---|---|---|---|---|---|---|
| 0.0283 | 0.3998 | 1055 | 0.0252 | 0.9012 | 0.8968 | 0.0252 |
| 0.0236 | 0.7997 | 2110 | 0.0225 | 0.9116 | 0.9081 | 0.0225 |
| 0.021 | 1.1995 | 3165 | 0.0217 | 0.9179 | 0.9147 | 0.0217 |
| 0.0206 | 1.5994 | 4220 | 0.0197 | 0.9230 | 0.9199 | 0.0197 |
| 0.0182 | 1.9992 | 5275 | 0.0197 | 0.9246 | 0.9223 | 0.0197 |
| 0.0163 | 2.3991 | 6330 | 0.0192 | 0.9256 | 0.9243 | 0.0192 |
| 0.0149 | 2.7989 | 7385 | 0.0185 | 0.9273 | 0.9260 | 0.0185 |
| 0.0119 | 3.1988 | 8440 | 0.0188 | 0.9283 | 0.9253 | 0.0188 |
| 0.0131 | 3.5986 | 9495 | 0.0184 | 0.9294 | 0.9273 | 0.0184 |
| 0.0133 | 3.9985 | 10550 | 0.0183 | 0.9296 | 0.9276 | 0.0183 |
| 0.0102 | 4.3983 | 11605 | 0.0185 | 0.9298 | 0.9275 | 0.0185 |
| 0.0116 | 4.7982 | 12660 | 0.0185 | 0.9299 | 0.9276 | 0.0185 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 2.19.2
- Tokenizers 0.20.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Dec 8, 2024
- Updated
- Dec 9, 2024