Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use singhshiva/robert_mnli_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use singhshiva/robert_mnli_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="singhshiva/robert_mnli_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("singhshiva/robert_mnli_3") model = AutoModelForSequenceClassification.from_pretrained("singhshiva/robert_mnli_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "
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"1": "
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"2": "kaggle",
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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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": "4.38.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "twitter_racism",
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"1": "aggression",
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"2": "kaggle",
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"3": "twitter_sexism",
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"4": "youtube",
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"5": "toxicity",
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"6": "attack",
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"7": "twitter"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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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": "4.38.2",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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model.safetensors
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runs/Mar14_10-45-50_e27a547ef9bb/events.out.tfevents.1710413152.e27a547ef9bb.462.0
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size 5415
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training_args.bin
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size 4920
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