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---
library_name: transformers
license: mit
base_model: intfloat/e5-large-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: intfloat-e5-large-v2-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-large-v2-english-fp16
This model is a fine-tuned version of [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2999
- Accuracy: 0.8919
- Precision: 0.8922
- Recall: 0.8919
- F1: 0.8905
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0668 | 0.3922 | 50 | 0.9147 | 0.5648 | 0.6855 | 0.5648 | 0.4637 |
| 0.6872 | 0.7843 | 100 | 0.4339 | 0.8384 | 0.8374 | 0.8384 | 0.8369 |
| 0.3677 | 1.1725 | 150 | 0.3228 | 0.8802 | 0.8803 | 0.8802 | 0.8800 |
| 0.2966 | 1.5647 | 200 | 0.3345 | 0.8816 | 0.8827 | 0.8816 | 0.8798 |
| 0.3005 | 1.9569 | 250 | 0.3261 | 0.8762 | 0.8806 | 0.8762 | 0.8728 |
| 0.2175 | 2.3451 | 300 | 0.2999 | 0.8919 | 0.8922 | 0.8919 | 0.8905 |
| 0.2136 | 2.7373 | 350 | 0.3109 | 0.8846 | 0.8856 | 0.8846 | 0.8850 |
| 0.1841 | 3.1255 | 400 | 0.3765 | 0.8821 | 0.8824 | 0.8821 | 0.8818 |
| 0.1327 | 3.5176 | 450 | 0.3523 | 0.8900 | 0.8900 | 0.8900 | 0.8900 |
### Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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