Instructions to use Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer") model = AutoModelForSequenceClassification.from_pretrained("Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
4e97eab
1
Parent(s): 1537375
Training in progress, epoch 1
Browse files
config.json
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.36.0.dev0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 250002
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model.safetensors
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runs/Nov10_07-38-15_02128ece2ae5/events.out.tfevents.1699601926.02128ece2ae5.48.0
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sentencepiece.bpe.model
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training_args.bin
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size
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