Instructions to use deepakvk/albert-base-v2-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepakvk/albert-base-v2-squad2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="deepakvk/albert-base-v2-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepakvk/albert-base-v2-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepakvk/albert-base-v2-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from deepakvk/albert-base-v2-squad2: direct link, hf CLI and curl.
- Browser
- Download file 476 Bytes
-
https://huggingface.co/deepakvk/albert-base-v2-squad2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://deepakvk/albert-base-v2-squad2/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/deepakvk/albert-base-v2-squad2/resolve/main/tokenizer_config.json
476 Bytes
| {"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "./models/albert-base-v2", "tokenizer_class": "AlbertTokenizer"} |