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my_awesome_model

Text Classification
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distilbert
Generated from Trainer
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  1. README.md +75 -0
  2. config.json +25 -0
  3. model.safetensors +3 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - recall
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+ model-index:
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+ - name: my_awesome_model
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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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+ # my_awesome_model
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7362
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+ - Accuracy: {'accuracy': 0.7291666666666666}
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+ - F1: {'f1': 0.7417218543046359}
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+ - Recall: {'recall': 0.7417218543046358}
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+ - Auc: {'roc_auc': 0.7285251607289602}
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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: 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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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Auc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:------------------------------:|:-------------------------------:|
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+ | No log | 0.91 | 10 | 0.5662 | {'accuracy': 0.6875} | {'f1': 0.7} | {'recall': 0.695364238410596} | {'roc_auc': 0.6870981775994585} |
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+ | No log | 1.82 | 20 | 0.5665 | {'accuracy': 0.6909722222222222} | {'f1': 0.6832740213523132} | {'recall': 0.6357615894039735} | {'roc_auc': 0.693793203461111} |
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+ | No log | 2.73 | 30 | 0.5643 | {'accuracy': 0.7256944444444444} | {'f1': 0.7127272727272727} | {'recall': 0.6490066225165563} | {'roc_auc': 0.729612800309373} |
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+ | No log | 3.64 | 40 | 0.5743 | {'accuracy': 0.7465277777777778} | {'f1': 0.750853242320819} | {'recall': 0.7284768211920529} | {'roc_auc': 0.7474500894281433} |
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+ | No log | 4.55 | 50 | 0.6057 | {'accuracy': 0.7430555555555556} | {'f1': 0.7448275862068965} | {'recall': 0.7152317880794702} | {'roc_auc': 0.7444772079083483} |
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+ | No log | 5.45 | 60 | 0.6318 | {'accuracy': 0.7291666666666666} | {'f1': 0.7382550335570469} | {'recall': 0.7284768211920529} | {'roc_auc': 0.7292019142456615} |
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+ | No log | 6.36 | 70 | 0.6664 | {'accuracy': 0.7291666666666666} | {'f1': 0.7450980392156863} | {'recall': 0.7549668874172185} | {'roc_auc': 0.7278484072122589} |
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+ | No log | 7.27 | 80 | 0.7007 | {'accuracy': 0.7222222222222222} | {'f1': 0.7241379310344827} | {'recall': 0.695364238410596} | {'roc_auc': 0.7235945279644221} |
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+ | No log | 8.18 | 90 | 0.7178 | {'accuracy': 0.7326388888888888} | {'f1': 0.7458745874587459} | {'recall': 0.7483443708609272} | {'roc_auc': 0.7318364190071059} |
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+ | No log | 9.09 | 100 | 0.7396 | {'accuracy': 0.7256944444444444} | {'f1': 0.7285223367697595} | {'recall': 0.7019867549668874} | {'roc_auc': 0.7269057862425677} |
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+ | No log | 10.0 | 110 | 0.7362 | {'accuracy': 0.7291666666666666} | {'f1': 0.7417218543046359} | {'recall': 0.7417218543046358} | {'roc_auc': 0.7285251607289602} |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ "model_type": "distilbert",
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+ "n_heads": 12,
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+ "problem_type": "single_label_classification",
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+ "tie_weights_": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.2",
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+ "vocab_size": 30522
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