Instructions to use Ermira/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ermira/qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Ermira/qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ermira/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("Ermira/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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---
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license: apache-2.0
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base_model: distilbert/distilbert-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- squad
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model-index:
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- name: qa_model
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results: []
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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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# qa_model
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.6498
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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: 16
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- eval_batch_size: 16
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- seed: 42
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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 |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 1.0 | 50 | 4.1982 |
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| No log | 2.0 | 100 | 3.7093 |
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| No log | 3.0 | 150 | 3.6498 |
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### Framework versions
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- Transformers 4.38.1
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- Pytorch 2.1.2
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- Datasets 2.1.0
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- Tokenizers 0.15.2
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model.safetensors
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