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Retrain on real mteb/amazon_massive_intent: acc=88.2 f1=85.1

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Files changed (4) hide show
  1. README.md +17 -8
  2. config.json +128 -45
  3. model.safetensors +2 -2
  4. tokenizer_config.json +3 -45
README.md CHANGED
@@ -4,18 +4,27 @@ license: mit
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  pipeline_tag: text-classification
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  base_model: dbmdz/bert-base-turkish-cased
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  tags: [turkish, text-classification, werea]
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- datasets: [GoktugD/turkish-intent-classification-1m]
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
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- > ℹ️ Bu, [Werea-co/Werea-TR-Intent](https://huggingface.co/Werea-co/Werea-TR-Intent) modelinin GoktugD profilindeki aynasıdır; asıl geliştirme Werea-co altında sürer.
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- # Werea-TR-Intent — Türkçe e-ticaret niyet sınıflandırma
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- [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) tabanının Türkçe e-ticaret niyet sınıflandırma için fine-tune'u.
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- 20 sınıf. Doğrulama: **accuracy 100.0%**, macro-F1 100.0%.
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- > Not: Sentetik ([GoktugD/turkish-intent-classification-1m](https://huggingface.co/datasets/GoktugD/turkish-intent-classification-1m)) veriyle eğitildi; gerçek-dünya kullanımında doğrulama önerilir.
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  ## Kullanım
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  ```python
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  from transformers import pipeline
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- clf = pipeline("text-classification", model="Werea-co/Werea-TR-Intent")
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- print(clf("Örnek Türkçe metin"))
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  ```
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  💜 [werea.co](https://werea.co)
 
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  pipeline_tag: text-classification
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  base_model: dbmdz/bert-base-turkish-cased
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  tags: [turkish, text-classification, werea]
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+ datasets: [mteb/amazon_massive_intent]
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+ metrics: [accuracy, f1]
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  ---
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+ > ℹ️ [Werea-co/Werea-TR-Intent](https://huggingface.co/Werea-co/Werea-TR-Intent) aynasıdır.
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+
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+ # Werea-TR-Intent — Türkçe niyet sınıflandırma
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+
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+ **Gerçek, insan-etiketli [mteb/amazon_massive_intent](https://huggingface.co/datasets/mteb/amazon_massive_intent) verisiyle eğitildi; aynı setin ayrılmış test bölümünde dürüstçe ölçüldü.** 60 sınıf.
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+
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+ ## Benchmark (gerçek test)
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+ | Metrik | Skor |
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+ |---|---|
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+ | Accuracy | 88.2% |
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+ | Macro-F1 | 85.1% |
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+
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+ [BERTurk](https://huggingface.co/dbmdz/bert-base-turkish-cased) tabanlı, 6 epoch.
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  ## Kullanım
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  ```python
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  from transformers import pipeline
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+ clf=pipeline("text-classification",model="Werea-co/Werea-TR-Intent")
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+ print(clf("text metni"))
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  ```
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  💜 [werea.co](https://werea.co)
config.json CHANGED
@@ -1,58 +1,142 @@
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  {
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- "_name_or_path": "dbmdz/bert-base-turkish-cased",
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  "architectures": [
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  "BertForSequenceClassification"
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  ],
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  "attention_probs_dropout_prob": 0.1,
 
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  "classifier_dropout": null,
 
 
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  "hidden_act": "gelu",
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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- "0": "adres_degisikligi",
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- "1": "fatura_talebi",
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- "2": "fiyat_bilgisi",
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- "3": "garanti_sorgusu",
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- "4": "hesap_dogrulama",
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- "5": "iade_talebi",
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- "6": "iletisim_talebi",
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- "7": "iptal_talebi",
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- "8": "kampanya_bilgisi",
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- "9": "kargo_gecikmesi",
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- "10": "magaza_saatleri",
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- "11": "odeme_sorunu",
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- "12": "randevu_alma",
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- "13": "randevu_iptali",
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- "14": "sifre_sifirlama",
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- "15": "siparis_durumu",
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- "16": "stok_sorgusu",
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- "17": "teknik_destek",
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- "18": "urun_karsilastirma",
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- "19": "uyelik_iptali"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
 
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  },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
@@ -60,11 +144,10 @@
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  "num_attention_heads": 12,
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- "position_embedding_type": "absolute",
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  "hidden_act": "gelu",
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  "hidden_size": 768,
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  "id2label": {
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+ "0": "alarm_query",
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+ "2": "alarm_set",
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+ "3": "audio_volume_down",
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+ "4": "audio_volume_mute",
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+ "10": "cooking_query",
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+ "14": "email_addcontact",
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+ "18": "general_greet",
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+ "19": "general_joke",
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+ "20": "general_quirky",
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  "max_position_embeddings": 512,
 
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