---
library_name: transformers
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: modelsent_test
  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. -->

# modelsent_test

This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2379
- Accuracy: 0.9261
- F1: 0.9261
- Precision: 0.9261
- Recall: 0.9261
- Accuracy Label Negative: 0.9242
- Accuracy Label Positive: 0.9278

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Accuracy Label Negative | Accuracy Label Positive |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------------------:|:-----------------------:|
| 0.5403        | 0.2442 | 100  | 0.5274          | 0.7611   | 0.7596 | 0.7728    | 0.7611 | 0.8535                  | 0.6746                  |
| 0.2673        | 0.4884 | 200  | 0.2806          | 0.8980   | 0.8980 | 0.8994    | 0.8980 | 0.9230                  | 0.8746                  |
| 0.247         | 0.7326 | 300  | 0.2610          | 0.9029   | 0.9024 | 0.9074    | 0.9029 | 0.8434                  | 0.9586                  |
| 0.2357        | 0.9768 | 400  | 0.2560          | 0.9084   | 0.9084 | 0.9096    | 0.9084 | 0.9318                  | 0.8864                  |
| 0.2094        | 1.2198 | 500  | 0.3127          | 0.9090   | 0.9089 | 0.9123    | 0.9090 | 0.9508                  | 0.8698                  |
| 0.1695        | 1.4640 | 600  | 0.2298          | 0.9188   | 0.9187 | 0.9189    | 0.9188 | 0.9053                  | 0.9314                  |
| 0.2024        | 1.7082 | 700  | 0.2218          | 0.9206   | 0.9206 | 0.9214    | 0.9206 | 0.9394                  | 0.9030                  |
| 0.1155        | 1.9524 | 800  | 0.2061          | 0.9236   | 0.9236 | 0.9236    | 0.9236 | 0.9192                  | 0.9278                  |
| 0.1361        | 2.1954 | 900  | 0.2299          | 0.9218   | 0.9218 | 0.9226    | 0.9218 | 0.9407                  | 0.9041                  |
| 0.1235        | 2.4396 | 1000 | 0.2668          | 0.9212   | 0.9212 | 0.9246    | 0.9212 | 0.9634                  | 0.8817                  |
| 0.084         | 2.6838 | 1100 | 0.2733          | 0.9218   | 0.9218 | 0.9240    | 0.9218 | 0.9545                  | 0.8911                  |
| 0.1326        | 2.9280 | 1200 | 0.2395          | 0.9249   | 0.9249 | 0.9249    | 0.9249 | 0.9192                  | 0.9302                  |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0