Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
imdb
text-embeddings-inference
Instructions to use phanerozoic/BERT-Sentiment-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phanerozoic/BERT-Sentiment-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phanerozoic/BERT-Sentiment-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("phanerozoic/BERT-Sentiment-Classifier") model = AutoModelForSequenceClassification.from_pretrained("phanerozoic/BERT-Sentiment-Classifier") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -14,7 +14,7 @@ widget:
|
|
| 14 |
|
| 15 |
# BERT-Sentiment-Classifier
|
| 16 |
|
| 17 |
-
The BERT-Sentiment-Classifier is a sophisticated model based on the `bert-base-uncased` architecture. It has been fine-tuned specifically for binary sentiment classification using the IMDB movie reviews dataset, aiming to provide nuanced understanding of textual sentiments.
|
| 18 |
|
| 19 |
- **Developed by**: phanerozoic
|
| 20 |
- **Model type**: BertForSequenceClassification
|
|
|
|
| 14 |
|
| 15 |
# BERT-Sentiment-Classifier
|
| 16 |
|
| 17 |
+
The BERT-Sentiment-Classifier is a sophisticated model based on the `bert-base-uncased` architecture. It has been fine-tuned specifically for binary sentiment classification (positive/negative) using the IMDB movie reviews dataset, aiming to provide nuanced understanding of textual sentiments.
|
| 18 |
|
| 19 |
- **Developed by**: phanerozoic
|
| 20 |
- **Model type**: BertForSequenceClassification
|