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End of training

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README.md CHANGED
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  ---
 
 
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  tags:
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- - adapter-transformers
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- - bart
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  datasets:
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- - snli
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Adapter `varun-v-rao/bart-base-bn-adapter-895K-snli-model1` for facebook/bart-base
 
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- An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [snli](https://huggingface.co/datasets/snli/) dataset.
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- This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
 
 
 
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- ## Usage
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- First, install `adapters`:
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- ```
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- pip install -U adapters
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- ```
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- Now, the adapter can be loaded and activated like this:
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- ```python
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- from adapters import AutoAdapterModel
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- model = AutoAdapterModel.from_pretrained("facebook/bart-base")
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- adapter_name = model.load_adapter("varun-v-rao/bart-base-bn-adapter-895K-snli-model1", source="hf", set_active=True)
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- ```
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- ## Architecture & Training
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- <!-- Add some description here -->
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- ## Evaluation results
 
 
 
 
 
 
 
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- <!-- Add some description here -->
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- ## Citation
 
 
 
 
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- <!-- Add some description here -->
 
 
 
 
 
 
 
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  ---
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+ license: apache-2.0
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+ base_model: facebook/bart-base
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  tags:
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+ - generated_from_trainer
 
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  datasets:
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+ - stanfordnlp/snli
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bart-base-bn-adapter-895K-snli-model1
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: snli
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+ type: stanfordnlp/snli
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8574476732371469
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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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+ # bart-base-bn-adapter-895K-snli-model1
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+ This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the snli dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3775
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+ - Accuracy: 0.8574
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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: 2e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 32
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+ - seed: 65
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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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.5241 | 1.0 | 8584 | 0.4209 | 0.8340 |
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+ | 0.4838 | 2.0 | 17168 | 0.3869 | 0.8509 |
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+ | 0.4716 | 3.0 | 25752 | 0.3775 | 0.8574 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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+ "config_id": "9076f36a74755ac4",
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+ "hidden_size": 768,
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+ "model_class": "BartForSequenceClassification",
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+ "model_name": "facebook/bart-base",
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+ "model_type": "bart",
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+ "name": "snli",
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+ "version": "0.1.1"
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+ }
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+ "num_labels": 3,
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