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+ Quantization made by Richard Erkhov.
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+
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+ [Github](https://github.com/RichardErkhov)
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+
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+ [Discord](https://discord.gg/pvy7H8DZMG)
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+
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+ [Request more models](https://github.com/RichardErkhov/quant_request)
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+
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+
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+ roberta-large-squad2 - bnb 8bits
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+ - Model creator: https://huggingface.co/deepset/
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+ - Original model: https://huggingface.co/deepset/roberta-large-squad2/
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+
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+
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+
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+
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+ Original model description:
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+ ---
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+ language: en
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+ license: cc-by-4.0
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+ datasets:
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+ - squad_v2
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+ base_model: roberta-large
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+ model-index:
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+ - name: deepset/roberta-large-squad2
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+ results:
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squad_v2
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+ type: squad_v2
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+ config: squad_v2
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+ split: validation
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+ metrics:
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+ - type: exact_match
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+ value: 85.168
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+ name: Exact Match
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+ - type: f1
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+ value: 88.349
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squad
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+ type: squad
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+ config: plain_text
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+ split: validation
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+ metrics:
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+ - type: exact_match
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+ value: 87.162
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+ name: Exact Match
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+ - type: f1
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+ value: 93.603
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: adversarial_qa
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+ type: adversarial_qa
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+ config: adversarialQA
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+ split: validation
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+ metrics:
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+ - type: exact_match
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+ value: 35.900
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+ name: Exact Match
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+ - type: f1
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+ value: 48.923
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squad_adversarial
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+ type: squad_adversarial
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+ config: AddOneSent
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+ split: validation
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+ metrics:
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+ - type: exact_match
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+ value: 81.142
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+ name: Exact Match
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+ - type: f1
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+ value: 87.099
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squadshifts amazon
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+ type: squadshifts
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+ config: amazon
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+ split: test
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+ metrics:
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+ - type: exact_match
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+ value: 72.453
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+ name: Exact Match
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+ - type: f1
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+ value: 86.325
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squadshifts new_wiki
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+ type: squadshifts
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+ config: new_wiki
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+ split: test
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+ metrics:
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+ - type: exact_match
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+ value: 82.338
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+ name: Exact Match
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+ - type: f1
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+ value: 91.974
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squadshifts nyt
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+ type: squadshifts
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+ config: nyt
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+ split: test
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+ metrics:
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+ - type: exact_match
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+ value: 84.352
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+ name: Exact Match
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+ - type: f1
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+ value: 92.645
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+ name: F1
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+ - task:
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+ type: question-answering
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+ name: Question Answering
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+ dataset:
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+ name: squadshifts reddit
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+ type: squadshifts
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+ config: reddit
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+ split: test
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+ metrics:
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+ - type: exact_match
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+ value: 74.722
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+ name: Exact Match
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+ - type: f1
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+ value: 86.860
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+ name: F1
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+ ---
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+
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+ # roberta-large for QA
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+
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+ This is the [roberta-large](https://huggingface.co/roberta-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
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+
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+
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+ ## Overview
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+ **Language model:** roberta-large
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+ **Language:** English
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+ **Downstream-task:** Extractive QA
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+ **Training data:** SQuAD 2.0
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+ **Eval data:** SQuAD 2.0
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+ **Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
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+ **Infrastructure**: 4x Tesla v100
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+
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+ ## Hyperparameters
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+
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+ ```
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+ base_LM_model = "roberta-large"
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+ ```
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+
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+ ## Using a distilled model instead
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+ Please note that we have also released a distilled version of this model called [deepset/roberta-base-squad2-distilled](https://huggingface.co/deepset/roberta-base-squad2-distilled). The distilled model has a comparable prediction quality and runs at twice the speed of the large model.
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+
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+ ## Usage
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+
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+ ### In Haystack
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+ Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
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+ ```python
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+ reader = FARMReader(model_name_or_path="deepset/roberta-large-squad2")
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+ # or
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+ reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2")
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+ ```
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+ For a complete example of ``roberta-large-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
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+
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+ ### In Transformers
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+ ```python
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+ from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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+
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+ model_name = "deepset/roberta-large-squad2"
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+
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+ # a) Get predictions
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+ nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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+ QA_input = {
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+ 'question': 'Why is model conversion important?',
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+ 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
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+ }
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+ res = nlp(QA_input)
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+
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+ # b) Load model & tokenizer
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+ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ ```
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+
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+ ## Authors
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+ **Branden Chan:** branden.chan@deepset.ai
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+ **Timo Möller:** timo.moeller@deepset.ai
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+ **Malte Pietsch:** malte.pietsch@deepset.ai
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+ **Tanay Soni:** tanay.soni@deepset.ai
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+
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+ ## About us
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+
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+ <div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
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+ <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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+ <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
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+ </div>
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+ <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
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+ <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
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+ </div>
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+ </div>
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+
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+ [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
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+
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+
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+ Some of our other work:
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+ - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
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+ - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
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+ - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
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+
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+ ## Get in touch and join the Haystack community
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+
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+ <p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.
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+ We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
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+ [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
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+
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+ By the way: [we're hiring!](http://www.deepset.ai/jobs)
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+