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
| 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 | |