---
license: mit
base_model: intfloat/multilingual-e5-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: multi-e5-base_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-base_lmd-comments_v1

This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1145
- F1: 0.7338
- Accuracy: 0.7410

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 1.1179        | 0.04  | 100  | 1.1781          | 0.3969 | 0.4748   |
| 1.0342        | 0.08  | 200  | 1.0924          | 0.5137 | 0.5899   |
| 0.7423        | 0.12  | 300  | 0.9700          | 0.6454 | 0.6691   |
| 0.7046        | 0.17  | 400  | 0.8990          | 0.6462 | 0.6691   |
| 0.6419        | 0.21  | 500  | 0.9583          | 0.6220 | 0.6475   |
| 0.6679        | 0.25  | 600  | 0.8621          | 0.6757 | 0.6835   |
| 0.6244        | 0.29  | 700  | 0.8017          | 0.7399 | 0.7410   |
| 0.5747        | 0.33  | 800  | 0.8040          | 0.6950 | 0.6906   |
| 0.5575        | 0.37  | 900  | 1.1045          | 0.6774 | 0.6906   |
| 0.5994        | 0.41  | 1000 | 1.1592          | 0.6725 | 0.6978   |
| 0.5565        | 0.46  | 1100 | 0.9960          | 0.7303 | 0.7338   |
| 0.511         | 0.5   | 1200 | 1.0861          | 0.7377 | 0.7482   |
| 0.5448        | 0.54  | 1300 | 0.7945          | 0.7155 | 0.7122   |
| 0.6059        | 0.58  | 1400 | 0.8167          | 0.6879 | 0.6906   |
| 0.4865        | 0.62  | 1500 | 1.1002          | 0.7181 | 0.7266   |
| 0.566         | 0.66  | 1600 | 0.7388          | 0.6678 | 0.6691   |
| 0.4756        | 0.7   | 1700 | 1.1751          | 0.7385 | 0.7482   |
| 0.5595        | 0.75  | 1800 | 1.0169          | 0.7204 | 0.7266   |
| 0.5838        | 0.79  | 1900 | 0.7718          | 0.7005 | 0.6978   |
| 0.573         | 0.83  | 2000 | 0.9156          | 0.7174 | 0.7266   |
| 0.5623        | 0.87  | 2100 | 0.8405          | 0.7416 | 0.7482   |
| 0.4929        | 0.91  | 2200 | 0.8329          | 0.7484 | 0.7554   |
| 0.5135        | 0.95  | 2300 | 1.1845          | 0.7008 | 0.7194   |
| 0.5217        | 0.99  | 2400 | 1.1482          | 0.7204 | 0.7338   |
| 0.4342        | 1.04  | 2500 | 1.3326          | 0.7078 | 0.7266   |
| 0.4975        | 1.08  | 2600 | 1.0527          | 0.7048 | 0.7194   |
| 0.4135        | 1.12  | 2700 | 0.9742          | 0.7431 | 0.7482   |
| 0.3562        | 1.16  | 2800 | 1.0554          | 0.7359 | 0.7410   |
| 0.3892        | 1.2   | 2900 | 1.1289          | 0.7403 | 0.7482   |
| 0.5041        | 1.24  | 3000 | 0.9690          | 0.7642 | 0.7698   |
| 0.4808        | 1.28  | 3100 | 0.9745          | 0.7378 | 0.7410   |
| 0.3532        | 1.33  | 3200 | 1.0141          | 0.7521 | 0.7554   |
| 0.4679        | 1.37  | 3300 | 0.9923          | 0.7410 | 0.7482   |
| 0.432         | 1.41  | 3400 | 1.0650          | 0.7486 | 0.7554   |
| 0.4543        | 1.45  | 3500 | 1.1235          | 0.7474 | 0.7554   |
| 0.4716        | 1.49  | 3600 | 1.0688          | 0.7316 | 0.7410   |
| 0.4251        | 1.53  | 3700 | 1.0290          | 0.7415 | 0.7482   |
| 0.3676        | 1.57  | 3800 | 1.1651          | 0.7546 | 0.7626   |
| 0.4031        | 1.62  | 3900 | 0.9981          | 0.7559 | 0.7626   |
| 0.4356        | 1.66  | 4000 | 0.9815          | 0.7558 | 0.7626   |
| 0.4355        | 1.7   | 4100 | 1.0349          | 0.7443 | 0.7482   |
| 0.4113        | 1.74  | 4200 | 1.1226          | 0.7333 | 0.7410   |
| 0.4447        | 1.78  | 4300 | 0.9854          | 0.7423 | 0.7482   |
| 0.4601        | 1.82  | 4400 | 1.0193          | 0.7348 | 0.7410   |
| 0.4474        | 1.86  | 4500 | 1.0177          | 0.7423 | 0.7482   |
| 0.3585        | 1.91  | 4600 | 1.0460          | 0.7276 | 0.7338   |
| 0.4064        | 1.95  | 4700 | 1.0995          | 0.7276 | 0.7338   |
| 0.4443        | 1.99  | 4800 | 1.1145          | 0.7338 | 0.7410   |


### Framework versions

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