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README.md
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model-index:
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- name: albert-base-v2-finetuned-squad2
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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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# albert-base-v2-finetuned-squad2
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9900
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## Model description
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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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##
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### Training hyperparameters
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The following hyperparameters were used during training:
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| 0.6593 | 2.0 | 16496 | 0.8548 |
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| 0.4314 | 3.0 | 24744 | 0.9900 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.3
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- Tokenizers 0.13.3
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model-index:
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- name: albert-base-v2-finetuned-squad2
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results: []
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language:
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- en
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metrics:
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- exact_match
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- f1
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pipeline_tag: zero-shot-classification
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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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## Model description
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ALBERTbase fine-tuned on SQuAD 2.0 : Encoder-based Transformer Language model, pretrained with Parameter Reduction techniques and Sentence Order Prediction<br>
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Suitable for Question-Answering tasks, predicts answer spans within the context provided.<br>
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**Language model:** albert-base-v2
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**Language:** English
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**Downstream-task:** Question-Answering
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**Training data:** Train-set SQuAD 2.0
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**Evaluation data:** Evaluation-set SQuAD 2.0
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**Hardware Accelerator used**: GPU Tesla T4
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## Intended uses & limitations
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For Question-Answering -
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```python
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!pip install transformers
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from transformers import pipeline
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model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
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question_answerer = pipeline("question-answering", model=model_checkpoint)
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context = """
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🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
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between them. It's straightforward to train your models with one before loading them for inference with the other.
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"""
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question = "Which deep learning libraries back 🤗 Transformers?"
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question_answerer(question=question, context=context)
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```
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## Results
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Evaluation on SQuAD 2.0 validation dataset:
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```
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exact: 78.12684241556472,
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f1: 81.54753481344116,
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total: 11873,
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HasAns_exact: 73.80229419703105,
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HasAns_f1: 80.65348867071317,
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HasAns_total: 5928,
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NoAns_exact: 82.4390243902439,
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NoAns_f1: 82.4390243902439,
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NoAns_total: 5945,
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best_exact: 78.12684241556472,
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best_exact_thresh: 0.9990358352661133,
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best_f1: 81.54753481344157,
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best_f1_thresh: 0.9990358352661133,
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total_time_in_seconds: 248.44505145400035,
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samples_per_second: 47.78923923223437,
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latency_in_seconds: 0.020925212789859374
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```
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### Training hyperparameters
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The following hyperparameters were used during training:
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| 0.6593 | 2.0 | 16496 | 0.8548 |
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| 0.4314 | 3.0 | 24744 | 0.9900 |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9900
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.3
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- Tokenizers 0.13.3
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