Instructions to use User00121/Essay-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use User00121/Essay-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="User00121/Essay-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("User00121/Essay-analysis") model = AutoModelForSequenceClassification.from_pretrained("User00121/Essay-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from User00121/Essay-analysis: direct link, hf CLI and curl.
- Browser
- Download file 282 Bytes
-
https://huggingface.co/User00121/Essay-analysis/resolve/main/config.json
- Command line
-
hf download hf://User00121/Essay-analysis/config.json
-
curl -L -o config.json https://huggingface.co/User00121/Essay-analysis/resolve/main/config.json
282 Bytes
| { | |
| "_name_or_path": "distilbert-base-uncased", | |
| "architectures": [ | |
| "DistilBertForSequenceClassification" | |
| ], | |
| "id2label": { | |
| "0": "bad", | |
| "1": "good" | |
| }, | |
| "label2id": { | |
| "bad": 0, | |
| "good": 1 | |
| }, | |
| "model_type": "distilbert", | |
| "num_labels": 2 | |
| } |