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LoRA-SemEval
Priyanka-Balivada/LoRA-SemEval
LoRA-SemEval is a machine learning model from Priyanka-Balivada. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024
- SHA
- ca8d0052
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
LoRA-SemEval
This model is a fine-tuned version of bert-base-uncased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.7185
- Accuracy: 0.6830
- Precision: 0.6857
- Recall: 0.6830
- Micro-avg-recall: 0.6830
- Micro-avg-precision: 0.6830
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Micro-avg-recall | Micro-avg-precision |
|---|---|---|---|---|---|---|---|---|
| 0.8156 | 1.0 | 2851 | 0.7505 | 0.6628 | 0.6653 | 0.6628 | 0.6628 | 0.6628 |
| 0.6812 | 2.0 | 5702 | 0.7254 | 0.6789 | 0.6819 | 0.6789 | 0.6789 | 0.6789 |
| 0.661 | 3.0 | 8553 | 0.7185 | 0.6830 | 0.6857 | 0.6830 | 0.6830 | 0.6830 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
- Library
- peft
- License
- apache-2.0
- Created
- Jan 16, 2024
- Updated
- Jan 16, 2024