Training completed!
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README.md
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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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model-index:
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- name: empathy_model
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results: []
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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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# empathy_model
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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: 0.0043
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- Mse: 0.0043
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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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- learning_rate: 5e-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: 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: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mse |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.0109 | 0.05 | 50 | 0.0050 | 0.0050 |
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| 0.0063 | 0.11 | 100 | 0.0092 | 0.0092 |
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| 0.0074 | 0.16 | 150 | 0.0045 | 0.0045 |
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| 0.0056 | 0.22 | 200 | 0.0060 | 0.0060 |
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| 0.0082 | 0.27 | 250 | 0.0046 | 0.0046 |
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| 0.0055 | 0.32 | 300 | 0.0056 | 0.0056 |
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| 0.0061 | 0.38 | 350 | 0.0045 | 0.0045 |
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| 0.0079 | 0.43 | 400 | 0.0060 | 0.0060 |
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| 0.0061 | 0.48 | 450 | 0.0043 | 0.0043 |
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| 0.0078 | 0.54 | 500 | 0.0046 | 0.0046 |
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| 0.0066 | 0.59 | 550 | 0.0043 | 0.0043 |
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| 0.0055 | 0.65 | 600 | 0.0044 | 0.0044 |
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| 0.0059 | 0.7 | 650 | 0.0043 | 0.0043 |
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| 0.0048 | 0.75 | 700 | 0.0056 | 0.0056 |
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| 0.0051 | 0.81 | 750 | 0.0043 | 0.0043 |
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| 0.0046 | 0.86 | 800 | 0.0043 | 0.0043 |
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| 0.0055 | 0.92 | 850 | 0.0043 | 0.0043 |
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| 0.0053 | 0.97 | 900 | 0.0043 | 0.0043 |
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### Framework versions
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- Transformers 4.39.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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model.safetensors
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