Text Classification
Transformers
ONNX
Safetensors
GGUF
Transformers.js
English
modernbert
shell
security
coding-agents
multi-label-classification
text-embeddings-inference
Instructions to use jensilo/acclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jensilo/acclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jensilo/acclass")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jensilo/acclass") model = AutoModelForSequenceClassification.from_pretrained("jensilo/acclass", device_map="auto") - Transformers.js
How to use jensilo/acclass with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'jensilo/acclass'); - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jensilo/acclass: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/jensilo/acclass/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jensilo/acclass/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jensilo/acclass/resolve/main/tokenizer_config.json
241 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "model_max_length": 2048, | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "sep_token": "[SEP]", | |
| "pad_token": "[PAD]", | |
| "mask_token": "[MASK]", | |
| "unk_token": "[UNK]" | |
| } | |