End of training

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+base_model: Alireza1044/albert-base-v2-mnli
+model-index:
+- name: NLI-Lora-Fine-Tuning-10K-ALBERT
+  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. -->
+
+# NLI-Lora-Fine-Tuning-10K-ALBERT
+
+This model is a fine-tuned version of [Alireza1044/albert-base-v2-mnli](https://huggingface.co/Alireza1044/albert-base-v2-mnli) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5040
+- Accuracy: 0.8087
+
+## 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   | 312  | 0.5855          | 0.7969   |
+| 0.6904        | 2.0   | 624  | 0.5286          | 0.7992   |
+| 0.6904        | 3.0   | 936  | 0.5205          | 0.8010   |
+| 0.5659        | 4.0   | 1248 | 0.5168          | 0.8021   |
+| 0.5529        | 5.0   | 1560 | 0.5128          | 0.8042   |
+| 0.5529        | 6.0   | 1872 | 0.5096          | 0.8054   |
+| 0.5459        | 7.0   | 2184 | 0.5071          | 0.8076   |
+| 0.5459        | 8.0   | 2496 | 0.5055          | 0.8081   |
+| 0.5319        | 9.0   | 2808 | 0.5044          | 0.8086   |
+| 0.5319        | 10.0  | 3120 | 0.5040          | 0.8087   |
+
+
+### Framework versions
+
+- PEFT 0.9.1.dev0
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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