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---
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 |