File size: 2,271 Bytes
af3997b 94bdd0d fe431ac 42a680f af3997b 26e754f 42a680f 94bdd0d 42a680f 94bdd0d af3997b |
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 |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of distilbert-base-uncased on the sentiment140 (https://huggingface.co/datasets/sentiment140) dataset.
Due to time and computational limits, I <b>used only 10% of the dataset</b> with a data augmentation. Despite minimal training, model's accuracy <b>improved by 59.5%</b> in comparison with non-fine tuned DistilBert LLM!<br>
It achieves the following results on the evaluation set:
- Loss: 0.4732
- Accuracy: 0.7753
<b>Classification Scores:</b><br>
- Fine Tuned DistilBert LLM with LoRa:
Accuracy = 0.78, Precision = 0.77, Recall = 0.78, F1 Score = 0.78
- Non-Fine Tuned DistilBert LLM:
Accuracy = 0.49, Precision = 0.45, Recall = 0.14, F1 Score = 0.22
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5092 | 1.0 | 5688 | 0.4979 | 0.7591 |
| 0.4928 | 2.0 | 11376 | 0.4876 | 0.7662 |
| 0.4915 | 3.0 | 17064 | 0.4821 | 0.7711 |
| 0.4787 | 4.0 | 22752 | 0.4779 | 0.7731 |
| 0.4757 | 5.0 | 28440 | 0.4767 | 0.7746 |
| 0.473 | 6.0 | 34128 | 0.4743 | 0.775 |
| 0.4649 | 7.0 | 39816 | 0.4741 | 0.7751 |
| 0.4709 | 8.0 | 45504 | 0.4732 | 0.7753 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|