---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
- NLP
- Language-Model
- Sentiment-Analysis
- Analysis
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my_distilbert_model
  results: []
datasets:
- cornell-movie-review-data/rotten_tomatoes
---

<!-- 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. -->

# my_distilbert_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an cornell-movie-review-data/rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5379
- Accuracy: 0.8424
- F1: 0.8424
- Precision: 0.8424
- Recall: 0.8424

## Model description


## How to use the model
``` python
!pip install -q  transformers

from huggingface_hub import notebook_login
notebook_login()#after running this line enter the access token generated on your hugging face account

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("gokarn09/my_distilbert_model")
model = AutoModelForSequenceClassification.from_pretrained("gokarn09/my_distilbert_model")

from transformers import pipeline
text=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
classifier = pipeline("sentiment-analysis", model="gokarn09/my_distilbert_model")
classifier(text)
```

## 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
- 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
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4193        | 1.0   | 534  | 0.4263          | 0.8180   | 0.8162 | 0.8311    | 0.8180 |
| 0.2548        | 2.0   | 1068 | 0.4289          | 0.8377   | 0.8376 | 0.8383    | 0.8377 |
| 0.1582        | 3.0   | 1602 | 0.5379          | 0.8424   | 0.8424 | 0.8424    | 0.8424 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0