Question Answering
Transformers
PyTorch
TensorFlow
JAX
Vietnamese
t5
text2text-generation
summarization
translation
text-generation-inference
Instructions to use VietAI/vit5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VietAI/vit5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VietAI/vit5-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base") model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base") - Inference
- Notebooks
- Google Colab
- Kaggle
root commited on
Commit ·
931b33a
1
Parent(s): 72d0543
Add flax model
Browse files- config.json +3 -1
- flax_model.msgpack +3 -0
config.json
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"vocab_size": 36096
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}
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"transformers_version": "4.17.0",
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"use_cache": true,
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"vocab_size": 36096
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}
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:cc62bdd0758d8a575b632379dd8963b96830ebbe26a794b500213dbde4fa8f5d
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size 1014701999
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