Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use eric10y/finetuning-sentiment-model-3000-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eric10y/finetuning-sentiment-model-3000-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eric10y/finetuning-sentiment-model-3000-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eric10y/finetuning-sentiment-model-3000-samples") model = AutoModelForSequenceClassification.from_pretrained("eric10y/finetuning-sentiment-model-3000-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f13cc2dc1af52e1760a5206483ab8a13e3ab4e98e65672595d69ab8d0f95e55
- Size of remote file:
- 5.37 kB
- SHA256:
- 78dd5b73d4e8ac7786291ad6c1e7439aa95f412eabdffb847f4c4d6fbb778d4b
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