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library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-distil-finalVersion
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. -->
# finetuning-sentiment-model-distil-finalVersion
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6034
- Precision Negative: 0.8333
- Recall Negative: 0.5556
- F1 Negative: 0.6667
- Precision Neutral: 0.75
- Recall Neutral: 0.9
- F1 Neutral: 0.8182
- Precision Positive: 0.8462
- Recall Positive: 0.7857
- F1 Positive: 0.8148
- Accuracy: 0.7907
- Confusion Matrix: [[20, 14, 2], [2, 72, 6], [2, 10, 44]]
## 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: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision Negative | Recall Negative | F1 Negative | Precision Neutral | Recall Neutral | F1 Neutral | Precision Positive | Recall Positive | F1 Positive | Accuracy | Confusion Matrix |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:-----------------:|:--------------:|:----------:|:------------------:|:---------------:|:-----------:|:--------:|:--------------------------------------:|
| 0.9478 | 1.0 | 22 | 0.9752 | 0.0 | 0.0 | 0.0 | 0.5064 | 0.9875 | 0.6695 | 0.875 | 0.25 | 0.3889 | 0.5407 | [[0, 35, 1], [0, 79, 1], [0, 42, 14]] |
| 0.7207 | 2.0 | 44 | 0.6483 | 0.8667 | 0.3611 | 0.5098 | 0.6847 | 0.95 | 0.7958 | 0.8913 | 0.7321 | 0.8039 | 0.7558 | [[13, 21, 2], [1, 76, 3], [1, 14, 41]] |
| 0.4066 | 3.0 | 66 | 0.6153 | 0.7586 | 0.6111 | 0.6769 | 0.7308 | 0.95 | 0.8261 | 1.0 | 0.6964 | 0.8211 | 0.7965 | [[22, 14, 0], [4, 76, 0], [3, 14, 39]] |
| 0.2355 | 4.0 | 88 | 0.6367 | 0.8 | 0.5556 | 0.6557 | 0.7170 | 0.95 | 0.8172 | 0.9756 | 0.7143 | 0.8247 | 0.7907 | [[20, 16, 0], [3, 76, 1], [2, 14, 40]] |
| 0.1048 | 5.0 | 110 | 0.5976 | 0.8333 | 0.5556 | 0.6667 | 0.75 | 0.9 | 0.8182 | 0.8462 | 0.7857 | 0.8148 | 0.7907 | [[20, 14, 2], [2, 72, 6], [2, 10, 44]] |
| 0.0745 | 6.0 | 132 | 0.6034 | 0.8333 | 0.5556 | 0.6667 | 0.75 | 0.9 | 0.8182 | 0.8462 | 0.7857 | 0.8148 | 0.7907 | [[20, 14, 2], [2, 72, 6], [2, 10, 44]] |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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