---
license: mit
base_model: intfloat/multilingual-e5-small
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: multi-e5-small_lmd-comments_v1
  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. -->

# multi-e5-small_lmd-comments_v1

This model is a fine-tuned version of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9808
- F1: 0.7036
- Accuracy: 0.7122

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.0969        | 0.04  | 100  | 1.0991          | 0.4109 | 0.4964   |
| 1.0764        | 0.08  | 200  | 1.0768          | 0.5217 | 0.5971   |
| 0.955         | 0.12  | 300  | 0.9313          | 0.5802 | 0.6691   |
| 0.8137        | 0.17  | 400  | 0.8927          | 0.5864 | 0.6475   |
| 0.7837        | 0.21  | 500  | 0.8711          | 0.6238 | 0.6475   |
| 0.7234        | 0.25  | 600  | 0.9953          | 0.5641 | 0.6475   |
| 0.6983        | 0.29  | 700  | 0.9111          | 0.6226 | 0.6475   |
| 0.6574        | 0.33  | 800  | 0.8557          | 0.6686 | 0.6835   |
| 0.6653        | 0.37  | 900  | 0.7925          | 0.7087 | 0.7122   |
| 0.6444        | 0.41  | 1000 | 0.8338          | 0.7056 | 0.7122   |
| 0.6155        | 0.46  | 1100 | 0.8339          | 0.7257 | 0.7338   |
| 0.5726        | 0.5   | 1200 | 0.8078          | 0.7140 | 0.7194   |
| 0.6279        | 0.54  | 1300 | 0.9534          | 0.6917 | 0.7050   |
| 0.6083        | 0.58  | 1400 | 0.9515          | 0.6914 | 0.7050   |
| 0.5525        | 0.62  | 1500 | 0.9281          | 0.6846 | 0.7050   |
| 0.6849        | 0.66  | 1600 | 0.8352          | 0.6917 | 0.7050   |
| 0.5924        | 0.7   | 1700 | 1.0702          | 0.6602 | 0.6906   |
| 0.5614        | 0.75  | 1800 | 0.9689          | 0.6801 | 0.6978   |
| 0.5936        | 0.79  | 1900 | 1.0179          | 0.6896 | 0.7050   |
| 0.5582        | 0.83  | 2000 | 0.8858          | 0.7320 | 0.7410   |
| 0.5479        | 0.87  | 2100 | 0.9373          | 0.7030 | 0.7122   |
| 0.6278        | 0.91  | 2200 | 0.8694          | 0.6858 | 0.6978   |
| 0.4819        | 0.95  | 2300 | 0.9440          | 0.7074 | 0.7194   |
| 0.5425        | 0.99  | 2400 | 1.0661          | 0.6765 | 0.6906   |
| 0.5804        | 1.04  | 2500 | 0.8904          | 0.7189 | 0.7266   |
| 0.5025        | 1.08  | 2600 | 1.0105          | 0.6886 | 0.7050   |
| 0.5148        | 1.12  | 2700 | 0.9934          | 0.7076 | 0.7194   |
| 0.5359        | 1.16  | 2800 | 0.9249          | 0.7291 | 0.7410   |
| 0.5002        | 1.2   | 2900 | 0.7503          | 0.7047 | 0.7050   |
| 0.4563        | 1.24  | 3000 | 0.8149          | 0.7230 | 0.7266   |
| 0.4837        | 1.28  | 3100 | 0.8956          | 0.7125 | 0.7194   |
| 0.4486        | 1.33  | 3200 | 0.9013          | 0.7110 | 0.7194   |
| 0.4721        | 1.37  | 3300 | 1.0545          | 0.7142 | 0.7266   |
| 0.5482        | 1.41  | 3400 | 1.0139          | 0.7014 | 0.7122   |
| 0.4488        | 1.45  | 3500 | 0.9427          | 0.7162 | 0.7266   |
| 0.4859        | 1.49  | 3600 | 1.1337          | 0.7074 | 0.7194   |
| 0.504         | 1.53  | 3700 | 1.0299          | 0.7178 | 0.7266   |
| 0.4555        | 1.57  | 3800 | 0.8830          | 0.7273 | 0.7338   |
| 0.502         | 1.62  | 3900 | 1.0340          | 0.7142 | 0.7266   |
| 0.5131        | 1.66  | 4000 | 1.0997          | 0.7031 | 0.7194   |
| 0.5208        | 1.7   | 4100 | 1.0845          | 0.7025 | 0.7194   |
| 0.4329        | 1.74  | 4200 | 1.0553          | 0.7132 | 0.7266   |
| 0.4612        | 1.78  | 4300 | 1.0458          | 0.7074 | 0.7194   |
| 0.4857        | 1.82  | 4400 | 0.9425          | 0.7120 | 0.7194   |
| 0.4986        | 1.86  | 4500 | 0.9965          | 0.7237 | 0.7338   |
| 0.4066        | 1.91  | 4600 | 0.9520          | 0.7041 | 0.7122   |
| 0.4638        | 1.95  | 4700 | 0.9558          | 0.6979 | 0.7050   |
| 0.4541        | 1.99  | 4800 | 0.9808          | 0.7036 | 0.7122   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2