Instructions to use oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned") model = AutoModelForQuestionAnswering.from_pretrained("oguzhanolm/loodos-bert-base-uncased-QA-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d9391bd
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README.md
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---
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language: tr
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---
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# Turkish SQuAD Model : Question Answering
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I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
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--output_dir "./model"
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```
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```
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language: tr
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tags:
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- question-answering
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- loodos-bert-base
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- TQuAD
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- tr
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model-index:
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- name: loodos-bert-base-uncased-QA-fine-tuned
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results:
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- task:
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name: Question Answering
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type: question-answering
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dataset:
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name: TQuAD
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type: Question-Answering-Dataset
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args: tr
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metrics:
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- name: Accuracy
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- value: 0.9125744047619048
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```
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# Example Usage
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> Load Model
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---
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language: tr
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tags:
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- question-answering
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- loodos-bert-base
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- TQuAD
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- tr
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datasets:
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- TQuAD
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model-index:
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- name: loodos-bert-base-uncased-QA-fine-tuned
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results:
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- task:
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name: Question Answering
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type: question-answering
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dataset:
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name: TQuAD
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type: question-answering
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args: tr
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metrics:
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- name: Accuracy
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type: acc
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value: 0.91
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---
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# Turkish SQuAD Model : Question Answering
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I fine-tuned Loodos-Turkish-Bert-Model for Question-Answering problem with TQuAD dataset
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--output_dir "./model"
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```
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# Example Usage
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> Load Model
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