File size: 3,400 Bytes
beaf758 e5e91d3 beaf758 6e6e935 beaf758 6e6e935 e5e91d3 beaf758 e5e91d3 6e6e935 e5e91d3 beaf758 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-4-disfluency
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. -->
# deberta-4-disfluency
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0093
- Precision: 0.9149
- Recall: 0.9306
- F1: 0.9226
- Accuracy: 0.9841
- Disfluent Precision: 0.9533
- Disfluent Recall: 0.9667
- Disfluent F1: 0.9599
- False Positive Rate: 0.0115
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 192
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Disfluent Precision | Disfluent Recall | Disfluent F1 | False Positive Rate |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------------:|:----------------:|:------------:|:-------------------:|
| 0.0079 | 0.1821 | 250 | 0.0090 | 0.9100 | 0.9259 | 0.9179 | 0.9833 | 0.9522 | 0.9633 | 0.9577 | 0.0117 |
| 0.0077 | 0.3642 | 500 | 0.0098 | 0.9118 | 0.9296 | 0.9206 | 0.9840 | 0.9595 | 0.9592 | 0.9594 | 0.0098 |
| 0.0059 | 0.5462 | 750 | 0.0146 | 0.9168 | 0.9287 | 0.9227 | 0.9836 | 0.9654 | 0.9508 | 0.9581 | 0.0083 |
| 0.0058 | 0.7283 | 1000 | 0.0109 | 0.9157 | 0.9242 | 0.9199 | 0.9837 | 0.9549 | 0.9623 | 0.9586 | 0.0110 |
| 0.0055 | 0.9104 | 1250 | 0.0116 | 0.9131 | 0.9279 | 0.9205 | 0.9834 | 0.9530 | 0.9632 | 0.9581 | 0.0115 |
| 0.0072 | 1.0925 | 1500 | 0.0093 | 0.8936 | 0.9201 | 0.9067 | 0.9815 | 0.9396 | 0.9679 | 0.9535 | 0.0150 |
| 0.0062 | 1.2746 | 1750 | 0.0103 | 0.9085 | 0.9226 | 0.9155 | 0.9822 | 0.9455 | 0.9652 | 0.9553 | 0.0134 |
| 0.0059 | 1.4567 | 2000 | 0.0088 | 0.9079 | 0.9225 | 0.9151 | 0.9816 | 0.9371 | 0.9715 | 0.9540 | 0.0157 |
| 0.0062 | 1.6387 | 2250 | 0.0093 | 0.9149 | 0.9306 | 0.9226 | 0.9841 | 0.9533 | 0.9667 | 0.9599 | 0.0115 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|