
<!-- 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. -->

# Scope_Classifier

This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7243
- Precision Macro: 0.3636
- Precision Weighted: 0.5289
- Recall Macro: 0.5
- Recall Weighted: 0.7273
- F1-score: 0.4211
- Accuracy: 0.7273

## 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: 8.281255573753255e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100.0
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | F1-score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:------------:|:---------------:|:--------:|:--------:|
| No log        | 1.0   | 97   | 0.7363          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| No log        | 2.0   | 194  | 0.7242          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| No log        | 3.0   | 291  | 0.7547          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| No log        | 4.0   | 388  | 0.7323          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| No log        | 5.0   | 485  | 0.7149          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| 0.6907        | 6.0   | 582  | 0.7634          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| 0.6907        | 7.0   | 679  | 0.7057          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| 0.6907        | 8.0   | 776  | 0.7070          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| 0.6907        | 9.0   | 873  | 0.7276          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |
| 0.6907        | 10.0  | 970  | 0.7243          | 0.3636          | 0.5289             | 0.5          | 0.7273          | 0.4211   | 0.7273   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/all-mpnet-base-v2", "model-index": [{"name": "Scope_Classifier", "results": []}]}
transformers tensorboard safetensors mpnet text-classification generated_from_trainer base_model:sentence-transformers/all-mpnet-base-v2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us