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
Update config.json
Browse files- config.json +0 -29
config.json
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@@ -23,35 +23,6 @@
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.21.3",
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"use_cache": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.21.3",
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"use_cache": true,
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