Model save

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: deberta-v3-large-orgs-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. -->
+
+# deberta-v3-large-orgs-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: 0.1343
+- Precision: 0.8037
+- Recall: 0.7601
+- F1: 0.7813
+- Accuracy: 0.9617
+
+## 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: 8e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 20
+- num_epochs: 3.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0612        | 1.0   | 1710 | 0.1069          | 0.7827    | 0.7741 | 0.7784 | 0.9612   |
+| 0.0502        | 2.0   | 3420 | 0.1225          | 0.8034    | 0.7461 | 0.7737 | 0.9606   |
+| 0.0285        | 3.0   | 5130 | 0.1343          | 0.8037    | 0.7601 | 0.7813 | 0.9617   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0a0+32f93b1
+- Datasets 2.15.0
+- Tokenizers 0.15.0