File size: 2,573 Bytes
85e949b 58ad3a6 85e949b 58ad3a6 85e949b 58ad3a6 85e949b |
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 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trueparagraph.ai-DistilBERT
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. -->
# trueparagraph.ai-DistilBERT
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9427
- F1: 0.9429
- Precision: 0.9352
- Recall: 0.9506
- Mcc: 0.8854
- Roc Auc: 0.9427
- Pr Auc: 0.9136
- Log Loss: 0.9232
- Loss: 0.3017
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Mcc | Roc Auc | Pr Auc | Log Loss | Validation Loss |
|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:------:|:-------:|:------:|:--------:|:---------------:|
| 0.5806 | 0.6297 | 500 | 0.8207 | 0.8349 | 0.7708 | 0.9108 | 0.6525 | 0.8211 | 0.7464 | 3.1049 | 0.4137 |
| 0.3015 | 1.2594 | 1000 | 0.8919 | 0.8885 | 0.9137 | 0.8646 | 0.7849 | 0.8918 | 0.8574 | 1.7818 | 0.3298 |
| 0.2287 | 1.8892 | 1500 | 0.9175 | 0.9155 | 0.9330 | 0.8987 | 0.8354 | 0.9174 | 0.8889 | 1.3631 | 0.2585 |
| 0.1444 | 2.5189 | 2000 | 0.9310 | 0.9312 | 0.9240 | 0.9386 | 0.8621 | 0.9310 | 0.8978 | 1.1225 | 0.2439 |
| 0.1149 | 3.1486 | 2500 | 0.9272 | 0.9304 | 0.8874 | 0.9778 | 0.8589 | 0.9274 | 0.8788 | 1.1773 | 0.3574 |
| 0.0716 | 3.7783 | 3000 | 0.9401 | 0.9405 | 0.9311 | 0.95 | 0.8805 | 0.9402 | 0.9095 | 0.9662 | 0.2655 |
| 0.0411 | 4.4081 | 3500 | 0.9427 | 0.9429 | 0.9352 | 0.9506 | 0.8854 | 0.9427 | 0.9136 | 0.9232 | 0.3017 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|