File size: 3,865 Bytes
7d73d6c e14882c 7d73d6c e14882c 7d73d6c e14882c 7d73d6c 31cdea6 7d73d6c e14882c 7d73d6c e14882c 7d73d6c e14882c 7d73d6c e992833 7d73d6c 31cdea6 7d73d6c e14882c |
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 82 83 84 85 86 87 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mega-base-wikitext-News_About_Gold
results: []
language:
- en
pipeline_tag: text-classification
---
# mega-base-wikitext-News_About_Gold
This model is a fine-tuned version of [mnaylor/mega-base-wikitext](https://huggingface.co/mnaylor/mega-base-wikitext).
It achieves the following results on the evaluation set:
- Loss: 1.0031
- Accuracy: 0.5014
- F1
- Weighted: 0.4023
- Micro: 0.5014
- Macro: 0.3282
- Recall
- Weighted: 0.5014
- Micro: 0.5014
- Macro: 0.3835
- Precision
- Weighted: 0.5783
- Micro: 0.5014
- Macro: 0.4548
## Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/News%20About%20Gold%20-%20Sentiment%20Analysis%20-%20MEGA%20with%20W%26B.ipynb
This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: https://github.com/DunnBC22/NLP_Projects/tree/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold
_Input Word Length:_
/Images/Input%20Word%20Length.png)
_Class Distribution:_
/Images/Class%20Distribution.png)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.2255 | 1.0 | 133 | 1.1365 | 0.4134 | 0.2437 | 0.4134 | 0.1487 | 0.4134 | 0.4134 | 0.2507 | 0.2652 | 0.4134 | 0.2285 |
| 1.1337 | 2.0 | 266 | 1.0851 | 0.4532 | 0.3257 | 0.4532 | 0.2539 | 0.4532 | 0.4532 | 0.3161 | 0.3015 | 0.4532 | 0.2705 |
| 1.0847 | 3.0 | 399 | 1.0384 | 0.4759 | 0.3591 | 0.4759 | 0.2915 | 0.4759 | 0.4759 | 0.3520 | 0.6352 | 0.4759 | 0.4942 |
| 1.05 | 4.0 | 532 | 1.0112 | 0.4962 | 0.3917 | 0.4962 | 0.3206 | 0.4962 | 0.4962 | 0.3783 | 0.5846 | 0.4962 | 0.4596 |
| 1.0309 | 5.0 | 665 | 1.0031 | 0.5014 | 0.4023 | 0.5014 | 0.3282 | 0.5014 | 0.5014 | 0.3835 | 0.5783 | 0.5014 | 0.4548 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |