Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
sentiment-analysis
twitter
nlp
sentiment140
Stanford
Egypt
Ain shams university
Hatem Moushir
Eval Results (legacy)
text-embeddings-inference
Instructions to use HatemMoushir/sentiment140-distilbert-hatem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HatemMoushir/sentiment140-distilbert-hatem with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HatemMoushir/sentiment140-distilbert-hatem")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HatemMoushir/sentiment140-distilbert-hatem") model = AutoModelForSequenceClassification.from_pretrained("HatemMoushir/sentiment140-distilbert-hatem") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f48d6e5b1771ca667c4536b212ba2578f5fb80835948e371c0adf11799f242a
- Size of remote file:
- 5.3 kB
- SHA256:
- 731f545ca230da237f432b5871a71656b35b2a49d11156666b3a32a651dcc672
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