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---
base_model: microsoft/mdeberta-v3-base
datasets:
- tweet_sentiment_multilingual
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
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. -->
# scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1517
- Accuracy: 0.5571
- F1: 0.5567
## 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: 32
- seed: 44
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| 1.0024 | 1.0870 | 500 | 0.9815 | 0.5556 | 0.5555 |
| 0.8411 | 2.1739 | 1000 | 0.9889 | 0.5772 | 0.5763 |
| 0.6502 | 3.2609 | 1500 | 1.1977 | 0.5633 | 0.5598 |
| 0.4723 | 4.3478 | 2000 | 1.6466 | 0.5617 | 0.5626 |
| 0.3276 | 5.4348 | 2500 | 1.7205 | 0.5498 | 0.5519 |
| 0.2221 | 6.5217 | 3000 | 2.0190 | 0.5590 | 0.5600 |
| 0.167 | 7.6087 | 3500 | 2.5446 | 0.5552 | 0.5562 |
| 0.1317 | 8.6957 | 4000 | 2.5112 | 0.5525 | 0.5539 |
| 0.1141 | 9.7826 | 4500 | 2.6152 | 0.5594 | 0.5545 |
| 0.1007 | 10.8696 | 5000 | 3.2079 | 0.5513 | 0.5416 |
| 0.0827 | 11.9565 | 5500 | 2.7099 | 0.5590 | 0.5590 |
| 0.0653 | 13.0435 | 6000 | 3.1595 | 0.5721 | 0.5678 |
| 0.0644 | 14.1304 | 6500 | 3.1304 | 0.5679 | 0.5667 |
| 0.054 | 15.2174 | 7000 | 3.0885 | 0.5590 | 0.5573 |
| 0.0504 | 16.3043 | 7500 | 3.5769 | 0.5583 | 0.5580 |
| 0.0394 | 17.3913 | 8000 | 3.5597 | 0.5606 | 0.5608 |
| 0.0419 | 18.4783 | 8500 | 3.8739 | 0.5525 | 0.5501 |
| 0.0406 | 19.5652 | 9000 | 3.5220 | 0.5667 | 0.5660 |
| 0.0355 | 20.6522 | 9500 | 4.0325 | 0.5691 | 0.5667 |
| 0.0281 | 21.7391 | 10000 | 3.7630 | 0.5602 | 0.5614 |
| 0.0266 | 22.8261 | 10500 | 4.0162 | 0.5617 | 0.5553 |
| 0.0283 | 23.9130 | 11000 | 3.9135 | 0.5525 | 0.5529 |
| 0.027 | 25.0 | 11500 | 4.0734 | 0.5563 | 0.5541 |
| 0.0205 | 26.0870 | 12000 | 4.2900 | 0.5583 | 0.5586 |
| 0.0198 | 27.1739 | 12500 | 4.2693 | 0.5579 | 0.5572 |
| 0.0155 | 28.2609 | 13000 | 4.7029 | 0.5563 | 0.5435 |
| 0.0187 | 29.3478 | 13500 | 4.4409 | 0.5640 | 0.5616 |
| 0.014 | 30.4348 | 14000 | 4.4588 | 0.5571 | 0.5568 |
| 0.0147 | 31.5217 | 14500 | 4.3420 | 0.5652 | 0.5640 |
| 0.0128 | 32.6087 | 15000 | 4.5721 | 0.5598 | 0.5575 |
| 0.0099 | 33.6957 | 15500 | 4.5574 | 0.5586 | 0.5599 |
| 0.0101 | 34.7826 | 16000 | 4.3777 | 0.5610 | 0.5613 |
| 0.0053 | 35.8696 | 16500 | 4.8103 | 0.5610 | 0.5617 |
| 0.0107 | 36.9565 | 17000 | 4.2925 | 0.5590 | 0.5589 |
| 0.0081 | 38.0435 | 17500 | 4.5884 | 0.5606 | 0.5591 |
| 0.0071 | 39.1304 | 18000 | 4.7187 | 0.5617 | 0.5621 |
| 0.0075 | 40.2174 | 18500 | 4.7305 | 0.5594 | 0.5591 |
| 0.0081 | 41.3043 | 19000 | 4.5589 | 0.5602 | 0.5607 |
| 0.0059 | 42.3913 | 19500 | 4.6516 | 0.5598 | 0.5589 |
| 0.0061 | 43.4783 | 20000 | 4.6553 | 0.5613 | 0.5605 |
| 0.0032 | 44.5652 | 20500 | 4.9672 | 0.5567 | 0.5569 |
| 0.0031 | 45.6522 | 21000 | 5.0283 | 0.5590 | 0.5595 |
| 0.0032 | 46.7391 | 21500 | 5.0801 | 0.5563 | 0.5552 |
| 0.0032 | 47.8261 | 22000 | 5.0934 | 0.5602 | 0.5604 |
| 0.0017 | 48.9130 | 22500 | 5.1305 | 0.5579 | 0.5581 |
| 0.0017 | 50.0 | 23000 | 5.1517 | 0.5571 | 0.5567 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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