Instructions to use MoNafea01/bert-eou-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MoNafea01/bert-eou-classifier with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("aubmindlab/bert-base-arabertv02") model = PeftModel.from_pretrained(base_model, "MoNafea01/bert-eou-classifier") - Transformers
How to use MoNafea01/bert-eou-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MoNafea01/bert-eou-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MoNafea01/bert-eou-classifier") model = AutoModelForSequenceClassification.from_pretrained("MoNafea01/bert-eou-classifier") - Notebooks
- Google Colab
- Kaggle
LoRA merged – final model
Browse files- config.json +33 -0
- model.safetensors +3 -0
config.json
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{
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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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"dtype": "float16",
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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": "Not EOU",
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"1": "EOU"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"EOU": 1,
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"Not EOU": 0
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},
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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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"transformers_version": "4.57.3",
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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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3b0c3c252793354a2fa22406bf8e91a545eef7a2d16eeb538509b702db57532c
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size 270413300
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