End of training

README.md

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+---
+library_name: transformers
+base_model: gates04/Structured-FP16-KD-NID
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: Structured-FP16-KD-SD-NID
+  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. -->
+
+# Structured-FP16-KD-SD-NID
+
+This model is a fine-tuned version of [gates04/Structured-FP16-KD-NID](https://huggingface.co/gates04/Structured-FP16-KD-NID) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0156
+- Accuracy: 0.9927
+- Precision: 0.9631
+- Recall: 0.9678
+- F1 score: 0.9646
+
+## 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: 500
+- eval_batch_size: 500
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1 score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 0.0224        | 1.0   | 2376  | 0.0209          | 0.9915   | 0.9310    | 0.9308 | 0.9298   |
+| 0.0203        | 2.0   | 4752  | 0.0198          | 0.9918   | 0.9308    | 0.9306 | 0.9289   |
+| 0.0163        | 3.0   | 7128  | 0.0186          | 0.9919   | 0.9612    | 0.9423 | 0.9468   |
+| 0.0168        | 4.0   | 9504  | 0.0195          | 0.9919   | 0.9351    | 0.9200 | 0.9245   |
+| 0.0132        | 5.0   | 11880 | 0.0170          | 0.9923   | 0.9625    | 0.9595 | 0.9603   |
+| 0.0154        | 6.0   | 14256 | 0.0169          | 0.9924   | 0.9694    | 0.9603 | 0.9630   |
+| 0.0105        | 7.0   | 16632 | 0.0165          | 0.9925   | 0.9701    | 0.9590 | 0.9627   |
+| 0.0133        | 8.0   | 19008 | 0.0161          | 0.9927   | 0.9661    | 0.9663 | 0.9650   |
+| 0.013         | 9.0   | 21384 | 0.0158          | 0.9927   | 0.9782    | 0.9677 | 0.9711   |
+| 0.0112        | 10.0  | 23760 | 0.0156          | 0.9927   | 0.9631    | 0.9678 | 0.9646   |
+
+
+### Framework versions
+
+- Transformers 4.50.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.1
+- Tokenizers 0.21.1

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "attention_probs_dropout_prob": 0.1,
+  "cell": {},
+  "dim": 312,
+  "dropout": 0.1,
+  "emb_size": 312,
+  "hidden_act": "gelu",
+  "hidden_dim": 3072,
+  "hidden_dropout_prob": 0.1,
+  "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",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19",
+    "20": "LABEL_20",
+    "21": "LABEL_21",
+    "22": "LABEL_22",
+    "23": "LABEL_23"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1200,
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+    "LABEL_9": 9
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 4,
+  "pad_token_id": 0,
+  "pre_trained": "",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "structure": [],
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.0",
+  "type_vocab_size": 2,
+  "vocab_size": 30522
+}

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training_args.bin

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