Upload TFBertForSequenceClassification
Browse files- README.md +47 -3
- config.json +32 -0
- tf_model.h5 +3 -0
README.md
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---
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---
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library_name: transformers
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tags:
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- generated_from_keras_callback
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model-index:
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- name: indobert-ham-spam-detection
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# indobert-ham-spam-detection
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: None
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- training_precision: float32
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### Training results
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### Framework versions
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- Transformers 4.46.3
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- TensorFlow 2.17.1
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- Tokenizers 0.20.3
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config.json
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{
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"_name_or_path": "indobert_ham_spam",
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"_num_labels": 5,
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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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"directionality": "bidi",
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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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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"transformers_version": "4.46.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 50000
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:23760e8c72ebc50ad7dcce1ff5c2ced9f51c5995c7470cffc5185afec803345f
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size 498059544
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