Instructions to use dtorber/NAS-bilingue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/NAS-bilingue 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="dtorber/NAS-bilingue")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/NAS-bilingue") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/NAS-bilingue") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- tokenizer_config.json +1 -1
- training_args.bin +1 -1
config.json
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{
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"_name_or_path": ".
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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{
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"_name_or_path": "./NAS-bilingue/checkpoint-2546384",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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pytorch_model.bin
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tokenizer_config.json
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"single_word": false
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},
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"model_max_length": 512,
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"name_or_path": ".
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"pad_token": {
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"__type": "AddedToken",
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"content": "<pad>",
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"single_word": false
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},
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"model_max_length": 512,
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"name_or_path": "./NAS-bilingue/checkpoint-2546384",
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"pad_token": {
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"__type": "AddedToken",
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"content": "<pad>",
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a6588531cc88f30b736508f250464517e5a59f9076013a4470ad0bfe552c4764
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size 3643
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