Text Classification
Transformers
Safetensors
distilbert
sentiment-analysis
smol-course
text-embeddings-inference
Instructions to use AIICLAB/my-first-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIICLAB/my-first-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIICLAB/my-first-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIICLAB/my-first-model") model = AutoModelForSequenceClassification.from_pretrained("AIICLAB/my-first-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8da7fead76a0563247f834eedfd39dc018b72b3e60a956234eeb0327aa8a450
- Size of remote file:
- 268 MB
- SHA256:
- dcee24fcaa1514dd0c8800192a908347f9ef7bcacaa0ee6ceba1eef637be13bc
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