Instructions to use VijayRam1812/content-classifier-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VijayRam1812/content-classifier-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VijayRam1812/content-classifier-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VijayRam1812/content-classifier-roberta") model = AutoModelForSequenceClassification.from_pretrained("VijayRam1812/content-classifier-roberta") - Notebooks
- Google Colab
- Kaggle
Upload RobertaForSequenceClassification
Browse files- config.json +6 -1
- model.safetensors +2 -2
config.json
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"problem_type": "single_label_classification",
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"tie_word_embeddings": true,
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"transformers_version": "5.2.0",
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"type_vocab_size": 1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"is_decoder": false,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"tie_word_embeddings": true,
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"transformers_version": "5.2.0",
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"type_vocab_size": 1,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 498609724
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