Duplicate from tangminhanh/ts_cate

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README.md

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+---
+license: mit
+base_model: tangminhanh/ts_tg
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: ts_cate
+  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. -->
+
+# ts_cate
+
+This model is a fine-tuned version of [tangminhanh/ts_tg](https://huggingface.co/tangminhanh/ts_tg) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0263
+- Accuracy: 0.8555
+- F1: 0.8736
+- Precision: 0.8824
+- Recall: 0.8649
+
+## 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: 32
+- eval_batch_size: 64
+- 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   | 404  | 0.0458          | 0.6802   | 0.7650 | 0.8750    | 0.6796 |
+| 0.1001        | 2.0   | 808  | 0.0318          | 0.7717   | 0.8259 | 0.8817    | 0.7767 |
+| 0.0321        | 3.0   | 1212 | 0.0278          | 0.8251   | 0.8564 | 0.8862    | 0.8286 |
+| 0.0215        | 4.0   | 1616 | 0.0256          | 0.8322   | 0.8627 | 0.8909    | 0.8363 |
+| 0.0161        | 5.0   | 2020 | 0.0256          | 0.8455   | 0.8679 | 0.8812    | 0.8551 |
+| 0.0161        | 6.0   | 2424 | 0.0259          | 0.8499   | 0.8711 | 0.8831    | 0.8594 |
+| 0.0124        | 7.0   | 2828 | 0.0255          | 0.8536   | 0.8713 | 0.8836    | 0.8594 |
+| 0.0105        | 8.0   | 3232 | 0.0262          | 0.8533   | 0.8723 | 0.8836    | 0.8612 |
+| 0.0087        | 9.0   | 3636 | 0.0261          | 0.8567   | 0.8746 | 0.8838    | 0.8656 |
+| 0.0078        | 10.0  | 4040 | 0.0263          | 0.8555   | 0.8736 | 0.8824    | 0.8649 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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config.json

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+  "_name_or_path": "tangminhanh/ts_tg",
+  "architectures": [
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+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Biometric",
+    "1": "Document",
+    "2": "On Boarding",
+    "3": "Ticketing Support",
+    "4": "Asset Management",
+    "5": "Off Boarding",
+    "6": "CCTV",
+    "7": "Checklist",
+    "8": "Hardware",
+    "9": "General Assistance",
+    "10": "TV Advertisement",
+    "11": "Network",
+    "12": "Network and Infrastructure Design",
+    "13": "Circuit",
+    "14": "Outage",
+    "15": "Network Security",
+    "16": "Notification",
+    "17": "System Security",
+    "18": "Server Management",
+    "19": "Server Projects",
+    "20": "Services Management",
+    "21": "User Account",
+    "22": "Deployment / Movement",
+    "23": "Email",
+    "24": "Printer",
+    "25": "Software",
+    "26": "Telephony",
+    "27": "Development",
+    "28": "Web/Application Security",
+    "29": "CASA",
+    "30": "Compliance",
+    "31": "Email Security",
+    "32": "Procurement",
+    "33": "TPMO",
+    "34": "BCP Activation"
+  },
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+    "Network Security": 15,
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+    "Notification": 16,
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+    "Procurement": 32,
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+    "Server Projects": 19,
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+    "TV Advertisement": 10,
+    "Telephony": 26,
+    "Ticketing Support": 3,
+    "User Account": 21,
+    "Web/Application Security": 28
+  },
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+  "pooler_hidden_size": 768,
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+  "position_biased_input": false,
+  "position_buckets": 256,
+  "problem_type": "multi_label_classification",
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+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
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