Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use GhylB/Sentiment_Analysis_BERT_Based_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhylB/Sentiment_Analysis_BERT_Based_MODEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GhylB/Sentiment_Analysis_BERT_Based_MODEL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL") model = AutoModelForSequenceClassification.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload BertForSequenceClassification
a227ec7 -
Upload tokenizer
f40bde8 -
update model card README.md
3a5b309 -
GhylB/Sentiment_Analysis_BERT_Based_MODEL
f1cbe2b -
Training in progress, step 2500
68dafb6 -
Training in progress, step 2000
30b5e5a -
Training in progress, step 1500
e2ab028 -
Training in progress, step 1000
982059a -
Training in progress, step 500
3c9e598
-
initial commit
db041d3