---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english_07112024T125645
  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. -->

# distilbert-base-uncased-finetuned-sst-2-english_07112024T125645

This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) on the MR Analysis Phase-3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5776
- F1: 0.8426
- Learning Rate: 0.0

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log        | 1.0   | 141  | 1.1776          | 0.5721 | 0.0000 |
| No log        | 2.0   | 282  | 0.9785          | 0.6619 | 0.0000 |
| No log        | 3.0   | 423  | 0.8326          | 0.7194 | 0.0000 |
| 1.1084        | 4.0   | 564  | 0.6920          | 0.7808 | 0.0000 |
| 1.1084        | 5.0   | 705  | 0.6907          | 0.7973 | 0.0000 |
| 1.1084        | 6.0   | 846  | 0.6107          | 0.8284 | 0.0000 |
| 1.1084        | 7.0   | 987  | 0.5776          | 0.8426 | 0.0000 |
| 0.4572        | 8.0   | 1128 | 0.6100          | 0.8523 | 0.0000 |
| 0.4572        | 9.0   | 1269 | 0.6279          | 0.8570 | 0.0000 |
| 0.4572        | 10.0  | 1410 | 0.6638          | 0.8587 | 0.0000 |
| 0.1637        | 11.0  | 1551 | 0.7340          | 0.8568 | 0.0000 |
| 0.1637        | 12.0  | 1692 | 0.7564          | 0.8596 | 7e-06  |
| 0.1637        | 13.0  | 1833 | 0.8077          | 0.8568 | 0.0000 |
| 0.1637        | 14.0  | 1974 | 0.7234          | 0.8667 | 0.0000 |
| 0.069         | 15.0  | 2115 | 0.7535          | 0.8664 | 3e-06  |
| 0.069         | 16.0  | 2256 | 0.7818          | 0.8659 | 0.0000 |
| 0.069         | 17.0  | 2397 | 0.8064          | 0.8646 | 0.0000 |
| 0.0376        | 18.0  | 2538 | 0.8203          | 0.8626 | 5e-07  |
| 0.0376        | 19.0  | 2679 | 0.8233          | 0.8629 | 1e-07  |
| 0.0376        | 20.0  | 2820 | 0.8235          | 0.8632 | 0.0    |

### Testing Results

|           class               | precision | recall | f1-score |
|:------------------------:|:---------:|:------:|:--------:|
| change_request           |    0.918  |  0.651 |   0.762  |
| discussion_participation |   0.839   |  0.882 |    0.860 |
| discussion_trigger   |   0.879   |  0.902 |    0.890 |
| acknowledgement   |   0.847   |  0.920 |    0.882 |
|  critical   |   0.686   |  0.940 |    0.793 |
| reference   |   0.802   |  0.947 |    0.869 |
| ----------- | ---------- | --------| --------- |
|  **accuracy**   |           |        |    0.828 |
|  **macro avg**   |  0.828    | 0.874  |   0.843  |
|  **weighted avg**   |   0.845   |  0.828 |    0.825 |
 
### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.19.1