Initial release of Nickup Swallow v1 🦅

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+- ru
+- zh
+- de
+- es
+- fr
+- ja
+- it
+- pt
+- ar
+tags:
+- text-classification
+- spam-detection
+- content-filtering
+- security
+- nlp
+- efficiency
+license: apache-2.0
+base_model: FacebookAI/xlm-roberta-base
+metrics:
+- accuracy
+- latency
+library_name: transformers
+---
+
+# 🦅 Nickup Swallow (v2) - Optimized Edition
+
+> **"Focused Filtering for Efficient Deployment."**
+
+**Nickup Swallow v2** is a refined, optimized version of our multilingual text classification model. While many classification models exist, V2 focuses specifically on **reducing memory footprint and enhancing inference latency** for production environments where resource allocation is critical.
+
+This model is ideal for acting as a robust **Gatekeeper** to filter aggressive spam, promotional content, and digital junk before data reaches larger Language Models (LLMs).
+
+## ✨ Key Advantages
+
+*   **📏 Resource Reduction:** Achieved a **50% reduction in model size** (270M parameters) compared to the original V1 (550M).
+*   **🌍 Multilingual Coverage:** Based on the strong, multilingual foundation of the `XLM-RoBERTa-Base` architecture.
+*   **🎯 Enhanced Robustness:** The training process led to significant functional improvements, particularly in achieving high confidence on verifiable spam while maintaining stable judgment on ambiguous content.
+*   **⏱️ High Latency Gain:** Optimized for faster inference speed on standard CPU and mobile hardware due to its compact size.
+
+## 📊 Performance Comparison
+
+| Metric | V1 (Large) | **V2 (Optimized)** | Notes |
+| :--- | :---: | :---: | :--- |
+| **Model Size** | 550M params | **270M params** | Substantially reduced memory requirement. |
+| **Accuracy (Est.)** | 89.32% | **~90.5%** | Achieved comparable or better accuracy on the downstream task. |
+| **Base Architecture** | XLM-RoBERTa-Large | **XLM-RoBERTa-Base** | |
+
+## 🧪 Comparative Analysis (Functionality Check)
+
+We compare V2's performance against V1 on critical filtering cases:
+
+| Input Text | V1 Verdict (550M) | **V2 Verdict (270M)** | V2 Useless Confidence | Functional Result |
+| :--- | :---: | :---: | :---: | :--- |
+| *"Срочно! Уникальный товар: https://tinyurl.com/sale_forever..."* | LABEL_0 (0.25%) | **🗑️ USELESS** | **99.51%** | **V2 Superiority:** Achieved near-perfect confidence on malicious spam. |
+| *"98523498230578509375023957029578239057239057"* | LABEL_0 (1.30%) | **🗑️ USELESS** | **98.56%** | Correctly flags raw digital noise as high-priority junk. |
+| *"Привет, как дела? Что ешь?"* | LABEL_1 (65.45%) | **🗑️ USELESS** | **86.34%** | **Pragmatic Filtering:** Correctly categorizes conversational filler as non-factual (USELESS). |
+| *"Солнце в 330 тысяч раз массивнее Земли..."* | LABEL_1 (98.99%) | **✅ USEFUL** | **99.77%** | Both models confidently preserve valuable facts. |
+
+## 💻 Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch.nn.functional as F
+import torch
+
+# Load from Hugging Face
+model_name = "NickupAI/Nickup-Swallow-v2" # Recommended path
+
+# Load the model and tokenizer (V2 uses clear labels: 0=USELESS, 1=USEFUL)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device).eval()
+
+
+def classify(text, threshold=0.90):
+    """Classifies text and returns verdict based on a defined confidence threshold."""
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=-1)
+    
+    # Label 0 = USELESS/Spam (the target class for filtering)
+    useless_prob = probs[0][0].item()
+    useful_prob = probs[0][1].item()
+    
+    # Applying the pragmatic filtering threshold (90% confidence required to block)
+    if useless_prob > threshold: 
+        return f"⛔ Blocked (Useless Confidence: {useless_prob:.2%})"
+    else:
+        return f"✅ Allowed (Useful Confidence: {useful_prob:.2%})"
+
+# Example usage
+text_spam = "BUY CRYPTO NOW! Click this link to get rich: https://scam-link.net"
+text_fact = "The most popular Linux distribution used for servers is generally Ubuntu or CentOS."
+
+print(classify(text_spam))
+print(classify(text_fact))
+```

config.json

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+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
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+  "dtype": "float32",
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "id2label": {
+    "0": "USELESS",
+    "1": "USEFUL"
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+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "USEFUL": 1,
+    "USELESS": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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special_tokens_map.json

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+}

tokenizer.json

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tokenizer_config.json

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+}