---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/bart-large-mnli
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: finetuned_bart
  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. -->

# finetuned_bart

This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
- F1: 0.9236
- Precision: 0.9000
- Recall: 0.9485
- Accuracy: 0.9216

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.0856        | 0.0933 | 50   | 0.0695          | 0.9122 | 0.9010    | 0.9238 | 0.9111   |
| 0.0593        | 0.1866 | 100  | 0.0685          | 0.9152 | 0.8970    | 0.9341 | 0.9135   |
| 0.0572        | 0.2799 | 150  | 0.0681          | 0.9149 | 0.8997    | 0.9306 | 0.9135   |
| 0.0549        | 0.3731 | 200  | 0.0679          | 0.9150 | 0.9054    | 0.9249 | 0.9141   |
| 0.0529        | 0.4664 | 250  | 0.0678          | 0.9174 | 0.9043    | 0.9308 | 0.9162   |
| 0.0776        | 0.5597 | 300  | 0.0673          | 0.9158 | 0.9079    | 0.9238 | 0.9151   |
| 0.0799        | 0.6530 | 350  | 0.0647          | 0.9201 | 0.8964    | 0.9450 | 0.9179   |
| 0.0806        | 0.7463 | 400  | 0.0647          | 0.9196 | 0.8968    | 0.9436 | 0.9175   |
| 0.0781        | 0.8396 | 450  | 0.0635          | 0.9193 | 0.8982    | 0.9415 | 0.9174   |
| 0.0771        | 0.9328 | 500  | 0.0633          | 0.9189 | 0.9019    | 0.9366 | 0.9174   |
| 0.0787        | 1.0261 | 550  | 0.0629          | 0.9202 | 0.8994    | 0.9420 | 0.9184   |
| 0.0737        | 1.1194 | 600  | 0.0627          | 0.9210 | 0.8989    | 0.9442 | 0.9190   |
| 0.0722        | 1.2127 | 650  | 0.0634          | 0.9212 | 0.8981    | 0.9455 | 0.9192   |
| 0.0684        | 1.3060 | 700  | 0.0630          | 0.9217 | 0.9065    | 0.9374 | 0.9204   |
| 0.0655        | 1.3993 | 750  | 0.0629          | 0.9228 | 0.8974    | 0.9496 | 0.9205   |
| 0.0739        | 1.4925 | 800  | 0.0625          | 0.9229 | 0.8993    | 0.9477 | 0.9208   |
| 0.0666        | 1.5858 | 850  | 0.0625          | 0.9233 | 0.8962    | 0.9521 | 0.9209   |
| 0.0703        | 1.6791 | 900  | 0.0621          | 0.9238 | 0.9001    | 0.9488 | 0.9218   |
| 0.0738        | 1.7724 | 950  | 0.0617          | 0.9227 | 0.9007    | 0.9458 | 0.9208   |
| 0.068         | 1.8657 | 1000 | 0.0620          | 0.9233 | 0.9002    | 0.9477 | 0.9213   |
| 0.069         | 1.9590 | 1050 | 0.0620          | 0.9236 | 0.9000    | 0.9485 | 0.9216   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1