Training complete

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- generated_from_trainer
+model-index:
+- name: deberta-med-ner-2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# deberta-med-ner-2
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.1

added_tokens.json

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+}

config.json

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+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
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+  ],
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+    "14": "I-CLINICAL_EVENT",
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+    "17": "I-COREFERENCE",
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+    "19": "B-DATE",
+    "20": "I-DATE",
+    "21": "I-DETAILED_DESCRIPTION",
+    "22": "B-DETAILED_DESCRIPTION",
+    "23": "I-DIAGNOSTIC_PROCEDURE",
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+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
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+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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special_tokens_map.json

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tokenizer_config.json

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