---
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2310
- Accuracy: 0.9279
- F1: 0.9279
- Precision: 0.9280
- Recall: 0.9279
- Accuracy Label Negative: 0.9192
- Accuracy Label Positive: 0.9361

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------------------:|:-----------------------:|
| 0.5427        | 0.2442 | 100  | 0.5228          | 0.7544   | 0.7539 | 0.7602    | 0.7544 | 0.8157                  | 0.6970                  |
| 0.2745        | 0.4884 | 200  | 0.2897          | 0.8937   | 0.8937 | 0.8940    | 0.8937 | 0.9028                  | 0.8852                  |
| 0.2409        | 0.7326 | 300  | 0.3172          | 0.8992   | 0.8985 | 0.9069    | 0.8992 | 0.8232                  | 0.9704                  |
| 0.2717        | 0.9768 | 400  | 0.2341          | 0.9163   | 0.9163 | 0.9169    | 0.9163 | 0.9306                  | 0.9030                  |
| 0.2178        | 1.2210 | 500  | 0.2670          | 0.9169   | 0.9169 | 0.9171    | 0.9169 | 0.9230                  | 0.9112                  |
| 0.2011        | 1.4652 | 600  | 0.2634          | 0.9145   | 0.9143 | 0.9158    | 0.9145 | 0.8813                  | 0.9456                  |
| 0.2179        | 1.7094 | 700  | 0.2657          | 0.9016   | 0.9015 | 0.9027    | 0.9016 | 0.8699                  | 0.9314                  |
| 0.1465        | 1.9536 | 800  | 0.2150          | 0.9212   | 0.9210 | 0.9228    | 0.9212 | 0.8851                  | 0.9550                  |
| 0.1602        | 2.1978 | 900  | 0.2421          | 0.9261   | 0.9261 | 0.9264    | 0.9261 | 0.9356                  | 0.9172                  |
| 0.1293        | 2.4420 | 1000 | 0.2693          | 0.9181   | 0.9181 | 0.9204    | 0.9181 | 0.9520                  | 0.8864                  |
| 0.1023        | 2.6862 | 1100 | 0.2392          | 0.9236   | 0.9237 | 0.9240    | 0.9236 | 0.9343                  | 0.9136                  |
| 0.1663        | 2.9304 | 1200 | 0.2326          | 0.9267   | 0.9267 | 0.9269    | 0.9267 | 0.9116                  | 0.9408                  |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1