Instructions to use mathildeparlo/base_seq_lab_bengali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathildeparlo/base_seq_lab_bengali 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="mathildeparlo/base_seq_lab_bengali")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mathildeparlo/base_seq_lab_bengali") model = AutoModelForQuestionAnswering.from_pretrained("mathildeparlo/base_seq_lab_bengali", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mathildeparlo/base_seq_lab_bengali: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/mathildeparlo/base_seq_lab_bengali/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mathildeparlo/base_seq_lab_bengali/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mathildeparlo/base_seq_lab_bengali/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 0f69a07f097dd304777fa75bcd0990f2b0779e217928915ed4b23be1184cc1b2
- Size of remote file:
- 265 MB
- SHA256:
- a0c715dc7c7a28e4298f93ab4a7b53016a667d0b84d13552731f291b86e581ff
路
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