---
library_name: transformers
license: mit
base_model: intfloat/e5-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: intfloat-e5-base-english-fp16
  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. -->

# intfloat-e5-base-english-fp16

This model is a fine-tuned version of [intfloat/e5-base](https://huggingface.co/intfloat/e5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3367
- Accuracy: 0.8772
- Precision: 0.8771
- Recall: 0.8772
- F1: 0.8760

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1413        | 0.3922 | 50   | 0.9915          | 0.6095   | 0.5647    | 0.6095 | 0.5607 |
| 0.8772        | 0.7843 | 100  | 0.7794          | 0.6611   | 0.7168    | 0.6611 | 0.6038 |
| 0.6664        | 1.1725 | 150  | 0.5313          | 0.8340   | 0.8323    | 0.8340 | 0.8319 |
| 0.4674        | 1.5647 | 200  | 0.4284          | 0.8585   | 0.8599    | 0.8585 | 0.8560 |
| 0.3922        | 1.9569 | 250  | 0.3721          | 0.8585   | 0.8625    | 0.8585 | 0.8555 |
| 0.3105        | 2.3451 | 300  | 0.3367          | 0.8772   | 0.8771    | 0.8772 | 0.8760 |
| 0.2881        | 2.7373 | 350  | 0.3321          | 0.8718   | 0.8725    | 0.8718 | 0.8714 |
| 0.2545        | 3.1255 | 400  | 0.3565          | 0.8806   | 0.8819    | 0.8806 | 0.8788 |
| 0.1997        | 3.5176 | 450  | 0.3320          | 0.8811   | 0.8812    | 0.8811 | 0.8809 |
| 0.2236        | 3.9098 | 500  | 0.3384          | 0.8826   | 0.8822    | 0.8826 | 0.8817 |
| 0.1454        | 4.2980 | 550  | 0.3805          | 0.8782   | 0.8782    | 0.8782 | 0.8768 |
| 0.1494        | 4.6902 | 600  | 0.3793          | 0.8806   | 0.8806    | 0.8806 | 0.8796 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1