Text Classification
Transformers
Safetensors
bert
sentiment-analysis
imdb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Nuhil000/imdb-bert-student-0-4798 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nuhil000/imdb-bert-student-0-4798 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nuhil000/imdb-bert-student-0-4798")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nuhil000/imdb-bert-student-0-4798") model = AutoModelForSequenceClassification.from_pretrained("Nuhil000/imdb-bert-student-0-4798") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
tags:
- text-classification
- sentiment-analysis
- imdb
- bert
model-index:
- name: imdb-bert-student-0-4798
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: IMDB
type: imdb
metrics:
- name: accuracy
type: accuracy
value: 0.9246
- name: f1
type: f1
value: 0.9246
imdb-bert-student-0-4798
Checkpoint: student_predistill_best_0.4798
Tokenizer source: student_merged_0.4656
Evaluated on IMDB test: accuracy/F1 ≈ 0.9246.