End of training

README.md

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+---
+license: mit
+library_name: peft
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+base_model: microsoft/deberta-v3-xsmall
+model-index:
+- name: STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test
+  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. -->
+
+# STS-Lora-Fine-Tuning-Capstone-Deberta-old-model-pipe-test
+
+This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4820
+- Accuracy: 0.3771
+
+## 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 360  | 1.7474          | 0.2429   |
+| 1.7416        | 2.0   | 720  | 1.7279          | 0.2429   |
+| 1.6866        | 3.0   | 1080 | 1.6799          | 0.2883   |
+| 1.6866        | 4.0   | 1440 | 1.6220          | 0.3372   |
+| 1.6241        | 5.0   | 1800 | 1.5787          | 0.3466   |
+| 1.5474        | 6.0   | 2160 | 1.5306          | 0.3604   |
+| 1.484         | 7.0   | 2520 | 1.5180          | 0.3626   |
+| 1.484         | 8.0   | 2880 | 1.5028          | 0.3706   |
+| 1.4452        | 9.0   | 3240 | 1.4871          | 0.3753   |
+| 1.429         | 10.0  | 3600 | 1.4820          | 0.3771   |
+
+
+### Framework versions
+
+- PEFT 0.9.0
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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