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---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: DeepPavlov/distilrubert-tiny-cased-conversational-v1
model-index:
- name: distilrubert-tiny-cased-conversational-v1_empathy_preprocessed_punct_lowercasing
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-cased-conversational-v1_empathy_preprocessed_punct_lowercasing
This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6036
- Accuracy: 0.7458
- F1: 0.7409
- Precision: 0.7420
- Recall: 0.7458
## 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=0.0001
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0953 | 1.0 | 9 | 1.0692 | 0.4661 | 0.3740 | 0.3740 | 0.4661 |
| 1.066 | 2.0 | 18 | 1.0242 | 0.5593 | 0.5491 | 0.5446 | 0.5593 |
| 1.0119 | 3.0 | 27 | 0.9259 | 0.6102 | 0.6106 | 0.6147 | 0.6102 |
| 0.9118 | 4.0 | 36 | 0.8659 | 0.5847 | 0.5349 | 0.5835 | 0.5847 |
| 0.8921 | 5.0 | 45 | 0.7925 | 0.6356 | 0.6133 | 0.6275 | 0.6356 |
| 0.83 | 6.0 | 54 | 0.7776 | 0.6271 | 0.6087 | 0.6199 | 0.6271 |
| 0.8015 | 7.0 | 63 | 0.7675 | 0.6695 | 0.6601 | 0.6871 | 0.6695 |
| 0.7334 | 8.0 | 72 | 0.7133 | 0.6780 | 0.6659 | 0.6748 | 0.6780 |
| 0.696 | 9.0 | 81 | 0.6939 | 0.6864 | 0.6758 | 0.6833 | 0.6864 |
| 0.6349 | 10.0 | 90 | 0.6555 | 0.7119 | 0.7057 | 0.7085 | 0.7119 |
| 0.6482 | 11.0 | 99 | 0.6585 | 0.7288 | 0.7202 | 0.7339 | 0.7288 |
| 0.5924 | 12.0 | 108 | 0.6223 | 0.7373 | 0.7332 | 0.7343 | 0.7373 |
| 0.5437 | 13.0 | 117 | 0.6364 | 0.7288 | 0.7231 | 0.7296 | 0.7288 |
| 0.5653 | 14.0 | 126 | 0.6158 | 0.7373 | 0.7266 | 0.7342 | 0.7373 |
| 0.5314 | 15.0 | 135 | 0.6104 | 0.7458 | 0.7439 | 0.7435 | 0.7458 |
| 0.4912 | 16.0 | 144 | 0.6119 | 0.7458 | 0.7433 | 0.7442 | 0.7458 |
| 0.4819 | 17.0 | 153 | 0.6040 | 0.7458 | 0.7452 | 0.7448 | 0.7458 |
| 0.4873 | 18.0 | 162 | 0.6113 | 0.7288 | 0.7248 | 0.7275 | 0.7288 |
| 0.4729 | 19.0 | 171 | 0.6035 | 0.7373 | 0.7292 | 0.7341 | 0.7373 |
| 0.4654 | 20.0 | 180 | 0.6036 | 0.7458 | 0.7409 | 0.7420 | 0.7458 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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