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README.md

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 ---
+language: en
+tags:
+- text-classification
+- product-detection
+datasets:
+- your-dataset-name
 license: apache-2.0
+model_name: Quintu/deberta-v3-large-1280-product
+library_name: transformers
+pipeline_tag: text-classification
 ---
+# Quintu/deberta-v3-large-1280-product
+
+Mô hình `Quintu/deberta-v3-large-1280-product` được thiết kế để thực hiện phân loại văn bản liên quan đến phát hiện loại sản phẩm.
+
+## Cách sử dụng
+
+Dưới đây là cách sử dụng mô hình này với thư viện `transformers`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Tải mô hình và tokenizer
+model_name = "Quintu/deberta-v3-large-1280-product"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Sử dụng mô hình để phân loại văn bản
+text = "This is an example text to classify."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Dự đoán
+logits = outputs.logits
+print(logits)

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-large",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "alcoholic beverages",
+    "1": "cereals and bakery products",
+    "2": "cocoa and cocoa preparations, coffee and tea",
+    "3": "confectionery",
+    "4": "dietetic foods, food supplements, fortified foods",
+    "5": "fats and oils",
+    "6": "feed materials",
+    "7": "food additives and flavourings",
+    "8": "food contact materials",
+    "9": "fruits and vegetables",
+    "10": "herbs and spices",
+    "11": "honey and royal jelly",
+    "12": "ices and desserts",
+    "13": "meat, egg and dairy products",
+    "14": "non-alcoholic beverages",
+    "15": "nuts, nut products and seeds",
+    "16": "other food product / mixed",
+    "17": "pet feed",
+    "18": "prepared dishes and snacks",
+    "19": "seafood",
+    "20": "soups, broths, sauces and condiments",
+    "21": "sugars and syrups"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "alcoholic beverages": 0,
+    "cereals and bakery products": 1,
+    "cocoa and cocoa preparations, coffee and tea": 2,
+    "confectionery": 3,
+    "dietetic foods, food supplements, fortified foods": 4,
+    "fats and oils": 5,
+    "feed materials": 6,
+    "food additives and flavourings": 7,
+    "food contact materials": 8,
+    "fruits and vegetables": 9,
+    "herbs and spices": 10,
+    "honey and royal jelly": 11,
+    "ices and desserts": 12,
+    "meat, egg and dairy products": 13,
+    "non-alcoholic beverages": 14,
+    "nuts, nut products and seeds": 15,
+    "other food product / mixed": 16,
+    "pet feed": 17,
+    "prepared dishes and snacks": 18,
+    "seafood": 19,
+    "soups, broths, sauces and condiments": 20,
+    "sugars and syrups": 21
+  },
+  "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": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:07aaa13fe76770c8715d36d8d9b6543b889dafb8cd3e84f5f81d3ec9f80ab0bb
+size 1740386448