---
license: mit
base_model: croissantllm/CroissantCool-v0.2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llm2vec-croissant-mntp
  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. -->

# llm2vec-croissant-mntp

This model is a fine-tuned version of [croissantllm/CroissantCool-v0.2](https://huggingface.co/croissantllm/CroissantCool-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8867
- Accuracy: 0.6078

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.0884 | 100  | 4.7866          | 0.1990   |
| No log        | 0.1768 | 200  | 4.0496          | 0.3309   |
| No log        | 0.2653 | 300  | 3.6525          | 0.3779   |
| No log        | 0.3537 | 400  | 3.2410          | 0.4258   |
| 3.9116        | 0.4421 | 500  | 3.6305          | 0.3912   |
| 3.9116        | 0.5305 | 600  | 3.1770          | 0.4406   |
| 3.9116        | 0.6189 | 700  | 2.4478          | 0.5199   |
| 3.9116        | 0.7073 | 800  | 2.2383          | 0.5508   |
| 3.9116        | 0.7958 | 900  | 2.1547          | 0.5635   |
| 2.4568        | 0.8842 | 1000 | 2.0868          | 0.5759   |
| 2.4568        | 0.9726 | 1100 | 2.0399          | 0.5820   |
| 2.4568        | 1.0610 | 1200 | 2.0102          | 0.5873   |
| 2.4568        | 1.1494 | 1300 | 1.9805          | 0.5897   |
| 2.4568        | 1.2378 | 1400 | 1.9590          | 0.5955   |
| 1.9305        | 1.3263 | 1500 | 1.9381          | 0.5982   |
| 1.9305        | 1.4147 | 1600 | 1.9249          | 0.5995   |
| 1.9305        | 1.5031 | 1700 | 1.9223          | 0.6017   |
| 1.9305        | 1.5915 | 1800 | 1.9091          | 0.6037   |
| 1.9305        | 1.6799 | 1900 | 1.9038          | 0.6042   |
| 1.8511        | 1.7683 | 2000 | 1.8982          | 0.6045   |
| 1.8511        | 1.8568 | 2100 | 1.8924          | 0.6060   |
| 1.8511        | 1.9452 | 2200 | 1.8844          | 0.6072   |
| 1.8511        | 2.0336 | 2300 | 1.8873          | 0.6087   |
| 1.8511        | 2.1220 | 2400 | 1.8889          | 0.6068   |
| 1.8197        | 2.2104 | 2500 | 1.8848          | 0.6080   |
| 1.8197        | 2.2989 | 2600 | 1.8736          | 0.6091   |
| 1.8197        | 2.3873 | 2700 | 1.8858          | 0.6072   |
| 1.8197        | 2.4757 | 2800 | 1.8814          | 0.6088   |
| 1.8197        | 2.5641 | 2900 | 1.8649          | 0.6103   |
| 1.8116        | 2.6525 | 3000 | 1.8647          | 0.6091   |
| 1.8116        | 2.7409 | 3100 | 1.8755          | 0.6101   |
| 1.8116        | 2.8294 | 3200 | 1.8755          | 0.6099   |
| 1.8116        | 2.9178 | 3300 | 1.8867          | 0.6078   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.0.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1