Instructions to use RameshArvind/roberta_long_answer_nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RameshArvind/roberta_long_answer_nq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RameshArvind/roberta_long_answer_nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RameshArvind/roberta_long_answer_nq") model = AutoModelForSequenceClassification.from_pretrained("RameshArvind/roberta_long_answer_nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from RameshArvind/roberta_long_answer_nq: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/added_tokens.json
- Command line
-
hf download hf://RameshArvind/roberta_long_answer_nq/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/RameshArvind/roberta_long_answer_nq/resolve/main/added_tokens.json
287 Bytes
| {"<Ul>": 50281, "</Tr>": 50276, "<Li>": 50279, "<Ol>": 50277, "</Table>": 50265, "<Table>": 50266, "</Li>": 50280, "</Td>": 50272, "</Th>": 50274, "<P>": 50268, "<Tr>": 50275, "</Ul>": 50282, "<Head>": 50267, "<Th>": 50273, "</Head>": 50270, "</P>": 50269, "<Td>": 50271, "</Ol>": 50278} |