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---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented
model-index:
- name: distilrubert_tiny-2nd-finetune-epru
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert_tiny-2nd-finetune-epru
This model is a fine-tuned version of [mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented](https://huggingface.co/mmillet/distilrubert-tiny-cased-conversational-v1_single_finetuned_on_cedr_augmented) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4467
- Accuracy: 0.8712
- F1: 0.8718
- Precision: 0.8867
- Recall: 0.8712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4947 | 1.0 | 12 | 0.4142 | 0.8773 | 0.8777 | 0.8907 | 0.8773 |
| 0.2614 | 2.0 | 24 | 0.3178 | 0.9018 | 0.9011 | 0.9069 | 0.9018 |
| 0.2079 | 3.0 | 36 | 0.3234 | 0.8773 | 0.8784 | 0.8850 | 0.8773 |
| 0.1545 | 4.0 | 48 | 0.3729 | 0.8834 | 0.8830 | 0.8946 | 0.8834 |
| 0.1028 | 5.0 | 60 | 0.2964 | 0.9018 | 0.9016 | 0.9073 | 0.9018 |
| 0.0986 | 6.0 | 72 | 0.2971 | 0.9141 | 0.9139 | 0.9152 | 0.9141 |
| 0.0561 | 7.0 | 84 | 0.3482 | 0.8957 | 0.8962 | 0.9023 | 0.8957 |
| 0.0336 | 8.0 | 96 | 0.3731 | 0.8957 | 0.8953 | 0.9014 | 0.8957 |
| 0.0364 | 9.0 | 108 | 0.4467 | 0.8712 | 0.8718 | 0.8867 | 0.8712 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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