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.gitattributes

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README.md

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+---
+datasets:
+- squad_v2
+language: en
+license: mit
+pipeline_tag: question-answering
+tags:
+- deberta
+- deberta-v3
+model-index:
+- name: navteca/deberta-v3-base-squad2
+  results:
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_v2
+      type: squad_v2
+      config: squad_v2
+      split: validation
+    metrics:
+    - name: Exact Match
+      type: exact_match
+      value: 83.8248
+      verified: true
+    - name: F1
+      type: f1
+      value: 87.41
+      verified: true
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad
+      type: squad
+      config: plain_text
+      split: validation
+    metrics:
+    - name: Exact Match
+      type: exact_match
+      value: 84.9678
+      verified: true
+    - name: F1
+      type: f1
+      value: 92.2777
+      verified: true
+---
+
+# Deberta v3 base model for QA (SQuAD 2.0)
+
+This is the [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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.
+
+## Training Data
+The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
+
+It can be used for question answering task.
+
+## Usage and Performance
+The trained model can be used like this:
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+# Load model & tokenizer
+deberta_model = AutoModelForQuestionAnswering.from_pretrained('navteca/deberta-v3-base-squad2')
+deberta_tokenizer = AutoTokenizer.from_pretrained('navteca/deberta-v3-base-squad2')
+
+# Get predictions
+nlp = pipeline('question-answering', model=deberta_model, tokenizer=deberta_tokenizer)
+
+result = nlp({
+    'question': 'How many people live in Berlin?',
+    'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
+})
+
+print(result)
+
+#{
+#  "answer": "3,520,031"
+#  "end": 36,
+#  "score": 0.96186668,
+#  "start": 27,
+#}
+```
+
+## Author
+[deepset](http://deepset.ai/)

added_tokens.json

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+}

config.json

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+{
+  "architectures": [
+    "DebertaV2ForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
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+  "language": "english",
+  "layer_norm_eps": 1e-7,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "name": "DebertaV2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "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": "4.20.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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tokenizer_config.json

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+  "sp_model_kwargs": {},
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+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}