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salt_language_Classification

Text Classification
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Generated from Trainer
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  1. README.md +125 -0
  2. config.json +48 -0
  3. model.safetensors +3 -0
  4. runs/May29_10-16-53_b6251adfe593/events.out.tfevents.1716977831.b6251adfe593.420.0 +3 -0
  5. runs/May29_10-16-53_b6251adfe593/events.out.tfevents.1716978804.b6251adfe593.420.1 +3 -0
  6. runs/May29_10-16-53_b6251adfe593/events.out.tfevents.1716980692.b6251adfe593.420.2 +3 -0
  7. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/t5-efficient-tiny
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - generator
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: salt_language_Classification
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: generator
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+ type: generator
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 1.0
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+ - name: Recall
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+ type: recall
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+ value: 1.0
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+ - name: F1
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+ type: f1
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+ value: 1.0
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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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+ # salt_language_Classification
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+
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+ This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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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: 0.001
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 10
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+ - training_steps: 20000
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:---:|
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+ | 0.0 | 0.025 | 500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.05 | 1000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.075 | 1500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.1 | 2000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.125 | 2500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.15 | 3000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.175 | 3500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.2 | 4000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.225 | 4500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.25 | 5000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.275 | 5500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.3 | 6000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.325 | 6500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.35 | 7000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.375 | 7500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.4 | 8000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.425 | 8500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.45 | 9000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.475 | 9500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.5 | 10000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.525 | 10500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.55 | 11000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.575 | 11500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.6 | 12000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.625 | 12500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.65 | 13000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.675 | 13500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.7 | 14000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.725 | 14500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.75 | 15000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.775 | 15500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.8 | 16000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.825 | 16500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.85 | 17000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.875 | 17500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.9 | 18000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.925 | 18500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.95 | 19000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 0.975 | 19500 | 0.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0 | 1.0 | 20000 | 0.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.1
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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+ "dense_act_fn": "relu",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "relu",
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+ "nyn": 5,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 4,
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+ "num_heads": 4,
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+ "num_layers": 4,
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+ "pad_token_id": 0,
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+ "problem_type": "single_label_classification",
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.1",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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