RichardErkhov
/

deepset_-_roberta-large-squad2-4bits

+Quantization made by Richard Erkhov.
+[Github](https://github.com/RichardErkhov)
+[Discord](https://discord.gg/pvy7H8DZMG)
+[Request more models](https://github.com/RichardErkhov/quant_request)
+roberta-large-squad2 - bnb 4bits
+- Model creator: https://huggingface.co/deepset/
+- Original model: https://huggingface.co/deepset/roberta-large-squad2/
+Original model description:
+---
+language: en
+license: cc-by-4.0
+datasets:
+- squad_v2
+base_model: roberta-large
+model-index:
+- name: deepset/roberta-large-squad2
+  results:
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_v2
+      type: squad_v2
+      config: squad_v2
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 85.168
+      name: Exact Match
+    - type: f1
+      value: 88.349
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad
+      type: squad
+      config: plain_text
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 87.162
+      name: Exact Match
+    - type: f1
+      value: 93.603
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: adversarial_qa
+      type: adversarial_qa
+      config: adversarialQA
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 35.900
+      name: Exact Match
+    - type: f1
+      value: 48.923
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_adversarial
+      type: squad_adversarial
+      config: AddOneSent
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 81.142
+      name: Exact Match
+    - type: f1
+      value: 87.099
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts amazon
+      type: squadshifts
+      config: amazon
+      split: test
+    metrics:
+    - type: exact_match
+      value: 72.453
+      name: Exact Match
+    - type: f1
+      value: 86.325
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts new_wiki
+      type: squadshifts
+      config: new_wiki
+      split: test
+    metrics:
+    - type: exact_match
+      value: 82.338
+      name: Exact Match
+    - type: f1
+      value: 91.974
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts nyt
+      type: squadshifts
+      config: nyt
+      split: test
+    metrics:
+    - type: exact_match
+      value: 84.352
+      name: Exact Match
+    - type: f1
+      value: 92.645
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts reddit
+      type: squadshifts
+      config: reddit
+      split: test
+    metrics:
+    - type: exact_match
+      value: 74.722
+      name: Exact Match
+    - type: f1
+      value: 86.860
+      name: F1
+---
+# roberta-large for QA
+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.
+## Overview
+**Language model:** roberta-large
+**Language:** English
+**Downstream-task:** Extractive QA
+**Training data:** SQuAD 2.0
+**Eval data:** SQuAD 2.0
+**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
+**Infrastructure**: 4x Tesla v100
+## Hyperparameters
+```
+base_LM_model = "roberta-large"
+```
+## Using a distilled model instead
+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.
+## Usage
+### In Haystack
+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/):
+```python
+reader = FARMReader(model_name_or_path="deepset/roberta-large-squad2")
+# or
+reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2")
+```
+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)
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+model_name = "deepset/roberta-large-squad2"
+# a) Get predictions
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+QA_input = {
+    'question': 'Why is model conversion important?',
+    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
+}
+res = nlp(QA_input)
+# b) Load model & tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+## Authors
+**Branden Chan:** branden.chan@deepset.ai
+**Timo Möller:** timo.moeller@deepset.ai
+**Malte Pietsch:** malte.pietsch@deepset.ai
+**Tanay Soni:**  tanay.soni@deepset.ai
+## About us
+<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
+    <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
+         <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
+     </div>
+     <div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
+         <img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
+     </div>
+</div>
+[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.
+Some of our other work:
+- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
+- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
+- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
+## Get in touch and join the Haystack community
+<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>.
+We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
+[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)
+By the way: [we're hiring!](http://www.deepset.ai/jobs)