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README.md

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+---
+language: en
+license: cc-by-4.0
+tags:
+- deberta
+- deberta-v3
+- deberta-v3-large
+datasets:
+- squad_v2
+model-index:
+- name: deepset/deberta-v3-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: 88.0876
+      name: Exact Match
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZmE0MWEwNjBkNTA1MmU0ZDkyYTA1OGEwNzY3NGE4NWU4NGI0NTQzNjRlNjY1NGRmNDU2MjA0NjU1N2JlZmNhYiIsInZlcnNpb24iOjF9.PnBF_vD0HujNBSShGJzsJnjmiBP_qT8xb2E7ORmpKfNspKXEuN_pBk9iV0IHRzdqOSyllcxlCv93XMPblNjWDw
+    - type: f1
+      value: 91.1623
+      name: F1
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDBkNDUzZmNkNDQwOGRkMmVlZjkxZWVlMzk3NzFmMGIxMTFmMjZlZDcyOWFiMjljNjM5MThlZDM4OWRmNzMwOCIsInZlcnNpb24iOjF9.bacyetziNI2DxO67GWpTyeRPXqF1POkyv00wEHXlyZu71pZngsNpZyrnuj2aJlCqQwHGnF_lT2ysaXKHprQRBg
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad
+      type: squad
+      config: plain_text
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 89.2366
+      name: Exact Match
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjQ1Yjk3YTdiYTY1NmYxMTI1ZGZlMjRkNTlhZTkyNjRkNjgxYWJiNDk2NzE3NjAyYmY3YmRjNjg4YmEyNDkyYyIsInZlcnNpb24iOjF9.SEWyqX_FPQJOJt2KjOCNgQ2giyVeLj5bmLI5LT_Pfo33tbWPWD09TySYdsthaVTjUGT5DvDzQLASSwBH05FyBw
+    - type: f1
+      value: 95.0569
+      name: F1
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2QyODQ1NWVlYjQxMjA0YTgyNmQ2NmIxOWY3MDRmZjE3ZWI5Yjc4ZDE4NzA2YjE2YTE1YTBlNzNiYmNmNzI3NCIsInZlcnNpb24iOjF9.NcXEc9xoggV76w1bQKxuJDYbOTxFzdny2k-85_b6AIMtfpYV3rGR1Z5YF6tVY2jyp7mgm5Jd5YSgGI3NvNE-CQ
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: adversarial_qa
+      type: adversarial_qa
+      config: adversarialQA
+      split: validation
+    metrics:
+    - type: exact_match
+      value: 42.100
+      name: Exact Match
+    - type: f1
+      value: 56.587
+      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: 83.548
+      name: Exact Match
+    - type: f1
+      value: 89.385
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts amazon
+      type: squadshifts
+      config: amazon
+      split: test
+    metrics:
+    - type: exact_match
+      value: 72.979
+      name: Exact Match
+    - type: f1
+      value: 87.254
+      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: 83.938
+      name: Exact Match
+    - type: f1
+      value: 92.695
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts nyt
+      type: squadshifts
+      config: nyt
+      split: test
+    metrics:
+    - type: exact_match
+      value: 85.534
+      name: Exact Match
+    - type: f1
+      value: 93.153
+      name: F1
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squadshifts reddit
+      type: squadshifts
+      config: reddit
+      split: test
+    metrics:
+    - type: exact_match
+      value: 73.284
+      name: Exact Match
+    - type: f1
+      value: 85.307
+      name: F1
+---
+# deberta-v3-large for QA 
+
+This is the [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-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:** deberta-v3-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**: 1x NVIDIA A10G
+
+## Hyperparameters
+
+```
+batch_size = 2
+grad_acc_steps = 32
+n_epochs = 6
+base_LM_model = "microsoft/deberta-v3-large"
+max_seq_len = 512
+learning_rate = 7e-6
+lr_schedule = LinearWarmup
+warmup_proportion = 0.2
+doc_stride=128
+max_query_length=64
+``` 
+
+## 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/deberta-v3-large-squad2")
+# or 
+reader = TransformersReader(model_name_or_path="deepset/deberta-v3-large-squad2",tokenizer="deepset/deberta-v3-large-squad2")
+```
+
+### In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "deepset/deberta-v3-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)
+```
+
+## Performance
+Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
+
+```
+"exact": 87.6105449338836,
+"f1": 90.75307008866517,
+
+"total": 11873,
+"HasAns_exact": 84.37921727395411,
+"HasAns_f1": 90.6732795483674,
+"HasAns_total": 5928,
+"NoAns_exact": 90.83263246425568,
+"NoAns_f1": 90.83263246425568,
+"NoAns_total": 5945
+```
+
+## 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://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" 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://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" 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://haystack.deepset.ai">Documentation</a></strong>. 
+
+We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join">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/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
+
+By the way: [we're hiring!](http://www.deepset.ai/jobs) 

added_tokens.json

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config.json

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+{
+  "_name_or_path": "checkpoint_deberta_large/language_model.bin",
+  "architectures": [
+    "DebertaV2ForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "language": "english",
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "name": "DebertaV2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
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+  "pooler_hidden_size": 1024,
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+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "summary_activation": "tanh",
+  "summary_last_dropout": 0,
+  "summary_type": "first",
+  "summary_use_proj": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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