Initial Commit

README.md

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+---
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+datasets:
+- massive
+metrics:
+- accuracy
+- f1
+model-index:
+- name: scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: massive
+      type: massive
+      config: all_1.1
+      split: validation
+      args: all_1.1
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8643440917174317
+    - name: F1
+      type: f1
+      value: 0.8368032657773605
+---
+
+<!-- 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. -->
+
+# scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0026
+- Accuracy: 0.8643
+- F1: 0.8368
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 64
+- seed: 66
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
+| 0.5131        | 0.27  | 5000  | 0.6674          | 0.8368   | 0.7780 |
+| 0.3715        | 0.53  | 10000 | 0.6554          | 0.8527   | 0.8145 |
+| 0.3066        | 0.8   | 15000 | 0.6924          | 0.8471   | 0.8103 |
+| 0.2194        | 1.07  | 20000 | 0.7348          | 0.8548   | 0.8238 |
+| 0.2112        | 1.34  | 25000 | 0.7297          | 0.8581   | 0.8288 |
+| 0.1907        | 1.6   | 30000 | 0.7308          | 0.8558   | 0.8288 |
+| 0.1816        | 1.87  | 35000 | 0.7785          | 0.8565   | 0.8281 |
+| 0.1297        | 2.14  | 40000 | 0.8493          | 0.8567   | 0.8278 |
+| 0.127         | 2.41  | 45000 | 0.8757          | 0.8576   | 0.8310 |
+| 0.1148        | 2.67  | 50000 | 0.8581          | 0.8577   | 0.8300 |
+| 0.1287        | 2.94  | 55000 | 0.8479          | 0.8597   | 0.8341 |
+| 0.0875        | 3.21  | 60000 | 0.8763          | 0.8656   | 0.8392 |
+| 0.0832        | 3.47  | 65000 | 0.9379          | 0.8620   | 0.8341 |
+| 0.0837        | 3.74  | 70000 | 0.9044          | 0.8625   | 0.8339 |
+| 0.0617        | 4.01  | 75000 | 0.9840          | 0.8618   | 0.8352 |
+| 0.0524        | 4.28  | 80000 | 0.9955          | 0.8639   | 0.8385 |
+| 0.0496        | 4.54  | 85000 | 1.0026          | 0.8643   | 0.8368 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.1.1+cu121
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "microsoft/mdeberta-v3-base",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
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+    "14": "LABEL_14",
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+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
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+  "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"
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+  "position_buckets": 256,
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+  "torch_dtype": "float32",
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+  "vocab_size": 251000
+}

eval_results_ml.json

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