Model save

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: checkpoints_29_9_microsoft_deberta_V1
+  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. -->
+
+# checkpoints_29_9_microsoft_deberta_V1
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6667
+- Map@3: 0.8692
+- Accuracy: 0.8
+
+## 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-06
+- train_batch_size: 2
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Map@3  | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
+| 1.6098        | 0.05  | 100  | 1.6090          | 0.5250 | 0.38     |
+| 1.6092        | 0.11  | 200  | 1.6040          | 0.7408 | 0.63     |
+| 1.1308        | 0.16  | 300  | 1.1807          | 0.7475 | 0.63     |
+| 1.0343        | 0.21  | 400  | 0.9997          | 0.8108 | 0.705    |
+| 0.9673        | 0.27  | 500  | 0.9104          | 0.8042 | 0.69     |
+| 0.9579        | 0.32  | 600  | 0.8178          | 0.8542 | 0.775    |
+| 0.8286        | 0.37  | 700  | 0.7612          | 0.8592 | 0.785    |
+| 0.8198        | 0.43  | 800  | 0.7236          | 0.8600 | 0.795    |
+| 0.8379        | 0.48  | 900  | 0.7237          | 0.8583 | 0.79     |
+| 0.8646        | 0.53  | 1000 | 0.7052          | 0.8583 | 0.785    |
+| 0.8876        | 0.59  | 1100 | 0.6899          | 0.8692 | 0.8      |
+| 0.8598        | 0.64  | 1200 | 0.6897          | 0.8683 | 0.8      |
+| 0.8218        | 0.69  | 1300 | 0.6655          | 0.8725 | 0.805    |
+| 0.8695        | 0.75  | 1400 | 0.6742          | 0.8692 | 0.8      |
+| 0.8136        | 0.8   | 1500 | 0.6739          | 0.8692 | 0.8      |
+| 0.7843        | 0.85  | 1600 | 0.6644          | 0.8725 | 0.81     |
+| 0.8477        | 0.91  | 1700 | 0.6655          | 0.8717 | 0.805    |
+| 0.7881        | 0.96  | 1800 | 0.6667          | 0.8692 | 0.8      |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.0.0
+- Datasets 2.9.0
+- Tokenizers 0.13.3

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