metadata

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

my_distilbert_model

This model is a fine-tuned version of 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

!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