Instructions to use intelcomp/ipc_level1_D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intelcomp/ipc_level1_D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intelcomp/ipc_level1_D")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intelcomp/ipc_level1_D") model = AutoModelForSequenceClassification.from_pretrained("intelcomp/ipc_level1_D", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from intelcomp/ipc_level1_D: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
-
https://huggingface.co/intelcomp/ipc_level1_D/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://intelcomp/ipc_level1_D/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/intelcomp/ipc_level1_D/resolve/main/tokenizer_config.json
314 Bytes
| {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "special_tokens_map_file": null, "name_or_path": "../models/roberta-large/", "tokenizer_class": "RobertaTokenizer"} |