End of training

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: DeBERTaV3_model_multilabel
+  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. -->
+
+# DeBERTaV3_model_multilabel
+
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3255
+- Accuracy: 0.8667
+- F1: 0.7333
+- Precision: 0.8148
+- Recall: 0.6667
+
+## 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: 5
+- eval_batch_size: 5
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| No log        | 1.0   | 25   | 0.4956          | 0.7667   | 0.4167 | 0.6667    | 0.3030 |
+| No log        | 2.0   | 50   | 0.4877          | 0.7667   | 0.4167 | 0.6667    | 0.3030 |
+| No log        | 3.0   | 75   | 0.4476          | 0.7667   | 0.4167 | 0.6667    | 0.3030 |
+| No log        | 4.0   | 100  | 0.4332          | 0.7833   | 0.4800 | 0.7059    | 0.3636 |
+| No log        | 5.0   | 125  | 0.3847          | 0.8167   | 0.6071 | 0.7391    | 0.5152 |
+| No log        | 6.0   | 150  | 0.3680          | 0.8583   | 0.7302 | 0.7667    | 0.6970 |
+| No log        | 7.0   | 175  | 0.3332          | 0.8667   | 0.7500 | 0.7742    | 0.7273 |
+| No log        | 8.0   | 200  | 0.3545          | 0.85     | 0.7000 | 0.7778    | 0.6364 |
+| No log        | 9.0   | 225  | 0.3255          | 0.8667   | 0.7333 | 0.8148    | 0.6667 |
+| No log        | 10.0  | 250  | 0.3322          | 0.8583   | 0.7213 | 0.7857    | 0.6667 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

added_tokens.json

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+}

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": "N/D",
+    "1": "staff",
+    "2": "clothes",
+    "3": "shop",
+    "4": "cash desk",
+    "5": "fitting rooms",
+    "6": "queue",
+    "7": "others"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "N/D": 0,
+    "cash desk": 4,
+    "clothes": 2,
+    "fitting rooms": 5,
+    "others": 7,
+    "queue": 6,
+    "shop": 3,
+    "staff": 1
+  },
+  "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,
+  "problem_type": "multi_label_classification",
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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special_tokens_map.json

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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