Instructions to use hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- fka/awesome-chatgpt-prompts
- allenai/dolma
- nampdn-ai/tiny-strange-textbooks
language:
- vi
metrics:
- character
pipeline_tag: table-question-answering
tags:
- climate