---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_clf_results
  results: []
datasets:
- app_reviews
language:
- en
library_name: transformers
pipeline_tag: text-classification
---

<!-- 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. -->

# bert_clf_results

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9611
- Accuracy: 0.7011

## 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: 16
- eval_batch_size: 32
- 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_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0767        | 1.0   | 5401  | 0.8447          | 0.7087   |
| 0.6523        | 2.0   | 10803 | 0.8287          | 0.7156   |
| 0.7209        | 3.0   | 16204 | 0.8852          | 0.7121   |
| 0.4274        | 4.0   | 21604 | 0.9611          | 0.7011   |

### Code Implementation
```
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Andyrasika/bert_clf_results")
inputs = tokenizer(prompt, return_tensors="pt")


model = AutoModelForSequenceClassification.from_pretrained("Andyrasika/bert_clf_results")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
```
Output
```
'LABEL_4'
```

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0