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bert-base-uncased-gretel-safety-alignment-classifier

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  1. README.md +58 -0
  2. config.json +45 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: results
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # results
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - eval_loss: 0.5240
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+ - eval_model_preparation_time: 0.0045
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+ - eval_accuracy: 0.8757
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+ - eval_f1_score: 0.8762
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+ - eval_runtime: 9.6734
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+ - eval_samples_per_second: 122.294
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+ - eval_steps_per_second: 15.3
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+ - step: 0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+
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+ ### Framework versions
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+
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+ - Transformers 5.0.0
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+ - Pytorch 2.9.0+cu128
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.2
config.json ADDED
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+ {
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+ "add_cross_attention": false,
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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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+ "bos_token_id": null,
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+ "classifier_dropout": null,
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+ "dtype": "float32",
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+ "eos_token_id": null,
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+ "gradient_checkpointing": false,
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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": "Discrimination",
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+ "1": "Information Hazards",
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+ "2": "Malicious Use",
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+ "3": "Societal Risks",
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+ "4": "System Risks"
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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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+ "Discrimination": 0,
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+ "Information Hazards": 1,
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+ "Malicious Use": 2,
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+ "Societal Risks": 3,
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+ "System Risks": 4
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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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+ "tie_word_embeddings": true,
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+ "transformers_version": "5.0.0",
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+ "type_vocab_size": 2,
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+ "use_cache": false,
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+ "vocab_size": 30522
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+ }
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