Instructions to use dongxq/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dongxq/test_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="dongxq/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dongxq/test_model") model = AutoModelForSeq2SeqLM.from_pretrained("dongxq/test_model") - Notebooks
- Google Colab
- Kaggle
dongxiaoqun commited on
Commit ·
1f876d1
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Parent(s): 7cdad11
Update README.md
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README.md
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@@ -29,8 +29,8 @@ from transformers import PegasusForConditionalGeneration,BertTokenizer
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class PegasusTokenizer(BertTokenizer):
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.add_special_tokens({'additional_special_tokens':["<mask_1>"]})
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def build_inputs_with_special_tokens(
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class PegasusTokenizer(BertTokenizer):
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(self, **kwargs):
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super().__init__(pre_tokenizer=lambda x: jieba.cut(x, HMM=False), **kwargs)
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# super().__init__(**kwargs)
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self.add_special_tokens({'additional_special_tokens':["<mask_1>"]})
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def build_inputs_with_special_tokens(
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