Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use SADAF-IMAMU/Is_there_relation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SADAF-IMAMU/Is_there_relation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SADAF-IMAMU/Is_there_relation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SADAF-IMAMU/Is_there_relation") model = AutoModelForSequenceClassification.from_pretrained("SADAF-IMAMU/Is_there_relation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
f5ce32a
1
Parent(s): 0d106ad
Training in progress, epoch 0
Browse files
.gitignore
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checkpoint-*/
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config.json
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{
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"_name_or_path": "aubmindlab/bert-base-arabertv02",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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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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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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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": "4.29.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 64000
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}
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pytorch_model.bin
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training_args.bin
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