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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: sileod/deberta-v3-base-tasksource-nli |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: deberta-v3-bass-complex-questions_classifier |
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results: [] |
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widget: |
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- text: "Why did the company decide to enter the Latin America region?" |
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example_title: "Simple Query" |
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- text: "What was the Company's net profit margin in the last fiscal year, and how does it compare to the industry average?" |
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example_title: "Multiple Queries" |
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- text: "Compare the customer growth rates in the SaaS sector of CloudServices Inc. with that of SaaSSolutions Tech over the last two years." |
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example_title: "Comparable Query" |
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- text: "What are your favorite ways to show friends you're thinking of them?" |
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example_title: "SmallTalk Query" |
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- text: "Alter the proposal to emphasize sustainability practices." |
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example_title: "Functional Query" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-bass-complex-questions_classifier |
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This model is a fine-tuned version of [sileod/deberta-v3-base-tasksource-nli](https://huggingface.co/sileod/deberta-v3-base-tasksource-nli) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0001 |
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- Accuracy: 1.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 0 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:---:| |
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| 0.0532 | 2.3585 | 500 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.1 |
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- Datasets 2.15.0 |
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- Tokenizers 0.19.1 |
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