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README.md

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+---
+license: cc-by-nc-4.0
+pipeline_tag: question-answering
+tags:
+    - question-answering
+    - transformers
+    - generated_from_trainer
+datasets:
+    - squad_v2
+    - LLukas22/nq-simplified
+---
+
+# deberta-v3-base-qa-en
+This model is an extractive qa model.
+It's a fine-tuned version of [deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the following datasets: [squad_v2](https://huggingface.co/datasets/squad_v2), [LLukas22/nq-simplified](https://huggingface.co/datasets/LLukas22/nq-simplified).
+
+
+
+## Usage
+
+You can use the model like this:
+
+```python
+from transformers import pipeline
+
+#Make predictions
+model_name = "LLukas22/deberta-v3-base-qa-en"
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+
+QA_input = {
+    "question": "What's my name?",
+    "context": "My name is Clara and I live in Berkeley."
+}
+
+result = nlp(QA_input)
+print(result)
+```
+Alternatively you can load the model and tokenizer on their own:
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+#Make predictions
+model_name = "LLukas22/deberta-v3-base-qa-en"
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+
+## Training hyperparameters
+The following hyperparameters were used during training:
+
+- learning_rate: 2E-05
+- per device batch size: 15
+- effective batch size: 45
+- seed: 42
+- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
+- weight decay: 1E-02
+- D-Adaptation: False
+- Warmup: False
+- number of epochs: 10
+- mixed_precision_training: bf16
+
+## Training results
+| Epoch | Train Loss | Validation Loss |
+| ----- | ---------- | --------------- |
+| 0 | 1.57 | 1.47 |
+
+## Evaluation results
+| Epoch | f1 | exact_match |
+| ----- | ----- | ----- |
+| 0 | 0.658 | 0.514 |
+
+## Framework versions
+- Transformers: 4.25.1
+- PyTorch: 2.0.0+cu118
+- PyTorch Lightning: 1.8.6
+- Datasets: 2.7.1
+- Tokenizers: 0.13.1
+- Sentence Transformers: 2.2.2
+
+## Additional Information
+This model was trained as part of my Master's Thesis **'Evaluation of transformer based language models for use in service information systems'**. The source code is available on [Github](https://github.com/LLukas22/Master).

added_tokens.json

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+}

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
+    "DebertaV2ForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "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,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

lightning_logs/version_0/events.out.tfevents.1679217724.ki-jupyternotebook-8bdd

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lightning_logs/version_0/hparams.yaml

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special_tokens_map.json

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tokenizer_config.json

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+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "microsoft/deberta-v3-base",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
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+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}