End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: Noisy10per-deberta-v3-small-Label_B-768-epochs-9
+  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. -->
+
+# Noisy10per-deberta-v3-small-Label_B-768-epochs-9
+
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1297
+- Accuracy: 0.9854
+- F1: 0.9854
+- Precision: 0.9856
+- Recall: 0.9854
+
+## 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: 12
+- eval_batch_size: 12
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 48
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 9
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0764        | 0.9995 | 1066 | 0.1081          | 0.9730   | 0.9731 | 0.9733    | 0.9730 |
+| 0.083         | 1.9993 | 2132 | 0.1059          | 0.9763   | 0.9762 | 0.9770    | 0.9763 |
+| 0.0474        | 2.9991 | 3198 | 0.0775          | 0.9833   | 0.9833 | 0.9834    | 0.9833 |
+| 0.0028        | 3.9998 | 4265 | 0.1005          | 0.9818   | 0.9818 | 0.9821    | 0.9818 |
+| 0.0025        | 4.9995 | 5331 | 0.1092          | 0.9841   | 0.9842 | 0.9843    | 0.9841 |
+| 0.0287        | 5.9993 | 6397 | 0.1633          | 0.9820   | 0.9821 | 0.9827    | 0.9820 |
+| 0.0085        | 6.9991 | 7463 | 0.1640          | 0.9814   | 0.9814 | 0.9818    | 0.9814 |
+| 0.001         | 7.9998 | 8530 | 0.1297          | 0.9854   | 0.9854 | 0.9856    | 0.9854 |
+| 0.0           | 8.9977 | 9594 | 0.1368          | 0.9851   | 0.9852 | 0.9853    | 0.9851 |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.4.0
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-small",
+  "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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "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": 6,
+  "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": "4.46.3",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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+size 567613932

training_args.bin

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