| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: intfloat/e5-small-v2 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: e5-small-v2-sentiment-twitter |
| | 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. --> |
| |
|
| | # e5-small-v2-sentiment-twitter |
| |
|
| | This model is a fine-tuned version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6420 |
| | - Accuracy: 0.7191 |
| | - F1: 0.7184 |
| | - Precision: 0.7232 |
| | - Recall: 0.7191 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 2 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.7497 | 0.1754 | 500 | 0.7109 | 0.699 | 0.6941 | 0.7013 | 0.699 | |
| | | 0.6768 | 0.3508 | 1000 | 0.6320 | 0.7225 | 0.7229 | 0.7242 | 0.7225 | |
| | | 0.633 | 0.5261 | 1500 | 0.6572 | 0.706 | 0.7110 | 0.7340 | 0.706 | |
| | | 0.6566 | 0.7015 | 2000 | 0.6138 | 0.7235 | 0.7179 | 0.7311 | 0.7235 | |
| | | 0.6164 | 0.8769 | 2500 | 0.5928 | 0.754 | 0.7545 | 0.7589 | 0.754 | |
| | | 0.525 | 1.0523 | 3000 | 0.6018 | 0.75 | 0.7501 | 0.7510 | 0.75 | |
| | | 0.5706 | 1.2276 | 3500 | 0.5946 | 0.7525 | 0.7535 | 0.7554 | 0.7525 | |
| | | 0.5166 | 1.4030 | 4000 | 0.6254 | 0.753 | 0.7520 | 0.7540 | 0.753 | |
| | | 0.5242 | 1.5784 | 4500 | 0.5979 | 0.741 | 0.7423 | 0.7452 | 0.741 | |
| | | 0.4989 | 1.7538 | 5000 | 0.5992 | 0.754 | 0.7545 | 0.7560 | 0.754 | |
| | | 0.5139 | 1.9291 | 5500 | 0.5917 | 0.755 | 0.7552 | 0.7564 | 0.755 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.55.4 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.21.4 |
| |
|
