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twscrape-prepared-regression-Linq-Embed-Mistral-scorer
AlekseyKorshuk/twscrape-prepared-regression-Linq-Embed-Mistral-scorer
twscrape-prepared-regression-Linq-Embed-Mistral-scorer is a machine learning model from AlekseyKorshuk. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- cc-by-nc-4.0
- Model type
- mistral
- Access
- Public
- Created
- Mar 11, 2025
- Updated
- Mar 11, 2025
- SHA
- ace416b6
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. -->
twscrape-prepared-regression-Linq-Embed-Mistral-scorer
This model is a fine-tuned version of Linq-AI-Research/Linq-Embed-Mistral on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 12.6464
- Mse: 12.6428
- Target 0 Mse: 37.8821
- Target 1 Mse: 8.8989
- Target 2 Mse: 2.5619
- Target 3 Mse: 1.2285
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: 1e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 192
- total_eval_batch_size: 192
- optimizer: Use 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: 0.1
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Target 0 Mse | Target 1 Mse | Target 2 Mse | Target 3 Mse |
|---|---|---|---|---|---|---|---|---|
| 428.5 | 1.0 | 280 | 347.7662 | 347.8059 | 633.5776 | 263.5729 | 370.2541 | 123.8188 |
| 9.9395 | 2.0 | 560 | 20.6962 | 20.7335 | 42.0764 | 18.6294 | 16.8553 | 5.3730 |
| 11.7539 | 3.0 | 840 | 14.0448 | 14.0469 | 37.0272 | 10.4259 | 5.5888 | 3.1457 |
| 12.293 | 4.0 | 1120 | 13.0462 | 13.0442 | 36.9399 | 8.8813 | 4.1221 | 2.2336 |
| 7.3027 | 5.0 | 1400 | 12.8216 | 12.8139 | 37.2984 | 8.7870 | 3.4468 | 1.7233 |
| 12.8594 | 6.0 | 1680 | 12.7524 | 12.7500 | 37.7420 | 8.7288 | 3.0093 | 1.5198 |
| 6.7754 | 7.0 | 1960 | 12.7292 | 12.7237 | 37.9368 | 8.8719 | 2.7380 | 1.3482 |
| 9.9688 | 8.0 | 2240 | 12.6771 | 12.6815 | 37.9204 | 8.9231 | 2.6111 | 1.2715 |
| 11.9023 | 9.0 | 2520 | 12.6444 | 12.6388 | 37.8705 | 8.8944 | 2.5619 | 1.2285 |
| 19.6836 | 10.0 | 2800 | 12.6464 | 12.6428 | 37.8821 | 8.8989 | 2.5619 | 1.2285 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.0.1
- Tokenizers 0.21.0
- Library
- transformers
- Type
- mistral
- License
- cc-by-nc-4.0
- Created
- Mar 11, 2025
- Updated
- Mar 11, 2025