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Accuknoxtechnologies
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CodeLanguage-Encoder-v1

Text Classification
Transformers
Safetensors
modernbert
code
language-identification
multi-label
llm-guard
encoder
text-embeddings-inference
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upload Code Language-ID encoder (multi-label classifier)

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  1. README.md +25 -48
  2. checkpoint-1126/config.json +101 -0
  3. checkpoint-1126/model.safetensors +3 -0
  4. checkpoint-1126/optimizer.pt +3 -0
  5. checkpoint-1126/rng_state.pth +3 -0
  6. checkpoint-1126/scheduler.pt +3 -0
  7. checkpoint-1126/special_tokens_map.json +55 -0
  8. checkpoint-1126/tokenizer.json +3 -0
  9. checkpoint-1126/tokenizer_config.json +2018 -0
  10. checkpoint-1126/trainer_state.json +450 -0
  11. checkpoint-1126/training_args.bin +3 -0
  12. config.json +2 -2
  13. model.safetensors +1 -1
  14. training_args.bin +1 -1
  15. training_provenance.json +2 -2
README.md CHANGED
@@ -13,59 +13,36 @@ tags:
13
 
14
  # Code Language Identification (encoder, multi-label)
15
 
16
- Encoder classifier that detects which programming languages (out of
17
- 25) appear in an input. Fine-tuned from
18
- **[`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)**.
19
- Replaces the 2B Qwen decoder LoRA with a single-forward-pass encoder for
20
- lower-latency runtime-security use in LLM-Guard's `Code` scanner.
21
 
22
  - **Base model**: [`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)
 
23
  - **Labels (25)**: Python, JavaScript, Java, C, C++, C#, Go, Rust, Kotlin, Swift, Ruby, R, Scala, Perl, Lua, Bash, PowerShell, Batch, SQL, Dockerfile, YAML, Makefile, Terraform, AWK, jq
24
- - **Output**: per-language sigmoid; `is_valid` = any language above threshold
25
- (0.5).
26
- - **Multilingual / long context**: inherited from the base encoder; trained with
27
- inputs up to the base model's positional limit.
28
 
29
- ## Test-set metrics (n=500)
30
 
31
- | Metric | Value |
32
- |--------|-------|
33
- | is_valid accuracy | 0.976 |
34
- | category-set (exact) accuracy | 0.904 |
35
- | micro-F1 | 0.952 |
36
- | macro-F1 | 0.950 |
37
- | latency mean (ms/example) | 2.45145196095109 |
38
- | latency p95 (ms/example) | 4.068814963102341 |
39
- | device | cuda:0 |
40
 
41
- ### Per-language F1
 
 
42
 
43
- | Language | F1 |
44
- |----------|----|
45
- | AWK | 0.926 |
46
- | Bash | 0.812 |
47
- | Batch | 0.964 |
48
- | C | 1.000 |
49
- | C# | 0.950 |
50
- | C++ | 0.958 |
51
- | Dockerfile | 0.955 |
52
- | Go | 0.950 |
53
- | Java | 1.000 |
54
- | JavaScript | 0.863 |
55
- | Kotlin | 1.000 |
56
- | Lua | 0.938 |
57
- | Makefile | 0.977 |
58
- | Perl | 0.947 |
59
- | PowerShell | 0.943 |
60
- | Python | 0.980 |
61
- | R | 0.963 |
62
- | Ruby | 0.977 |
63
- | Rust | 1.000 |
64
- | SQL | 1.000 |
65
- | Scala | 0.821 |
66
- | Swift | 0.939 |
67
- | Terraform | 0.950 |
68
- | YAML | 0.952 |
69
- | jq | 0.974 |
70
 
71
- *Evaluated on `test_dataset_langid.csv`. Generated 2026-06-01 18:00 UTC.*
 
 
 
 
 
 
 
 
 
 
13
 
14
  # Code Language Identification (encoder, multi-label)
15
 
16
+ Multi-label classifier over 25 programming languages, fine-tuned from
17
+ **[`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)**. Single forward pass;
18
+ `is_valid` = any language above threshold (0.5).
 
 
19
 
20
  - **Base model**: [`jhu-clsp/mmBERT-base`](https://huggingface.co/jhu-clsp/mmBERT-base)
21
+ - **Trained with**: max_seq_length=3072, epochs=2, lr=2e-05
22
  - **Labels (25)**: Python, JavaScript, Java, C, C++, C#, Go, Rust, Kotlin, Swift, Ruby, R, Scala, Perl, Lua, Bash, PowerShell, Batch, SQL, Dockerfile, YAML, Makefile, Terraform, AWK, jq
 
 
 
 
23
 
24
+ ## Usage
25
 
26
+ ```python
27
+ import torch
28
+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 
 
 
 
 
29
 
30
+ REPO = "Accuknoxtechnologies/CodeLanguage-Encoder-v1"
31
+ tokenizer = AutoTokenizer.from_pretrained(REPO)
32
+ model = AutoModelForSequenceClassification.from_pretrained(REPO).eval()
33
 
34
+ text = "def add(a, b):\n return a + b"
35
+ enc = tokenizer(text, truncation=True, max_length=3072, return_tensors="pt")
36
+ with torch.no_grad():
37
+ probs = model(**enc).logits.sigmoid()[0] # per-language sigmoid
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38
 
39
+ threshold = 0.5
40
+ id2label = model.config.id2label # {0: "Python", 1: "JavaScript", ...}
41
+ present = {id2label[i]: round(float(p), 3) for i, p in enumerate(probs) if p >= threshold}
42
+
43
+ # Same schema the original Qwen scanner emitted: is_valid = any language fired.
44
+ result = {"is_valid": bool(present), "category": {k: True for k in present}}
45
+ print(result) # e.g. {"is_valid": True, "category": {"Python": True}}
46
+ ```
47
+
48
+ > Test-set metrics are added by `eval_and_push_card.py` after evaluation.
checkpoint-1126/config.json ADDED
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+ {
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+ "architectures": [
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+ "ModernBertForSequenceClassification"
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+ "attention_bias": false,
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+ "classifier_pooling": "mean",
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+ "dtype": "float32",
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+ "global_rope_theta": 160000,
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+ "gradient_checkpointing": false,
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+ "hidden_activation": "gelu",
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Python",
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+ "1": "JavaScript",
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+ "2": "Java",
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+ "3": "C",
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+ "4": "C++",
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+ "5": "C#",
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+ "6": "Go",
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+ "7": "Rust",
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+ "8": "Kotlin",
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+ "9": "Swift",
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+ "10": "Ruby",
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+ "11": "R",
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+ "12": "Scala",
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+ "14": "Lua",
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+ "15": "Bash",
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+ "19": "Dockerfile",
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+ "20": "YAML",
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+ "21": "Makefile",
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+ "22": "Terraform",
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+ "23": "AWK",
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+ "24": "jq"
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+ },
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+ "initializer_cutoff_factor": 2.0,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 1152,
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "local_attention": 128,
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+ "local_rope_theta": 160000,
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+ "mask_token_id": 4,
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+ "max_position_embeddings": 8192,
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+ "mlp_bias": false,
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+ "mlp_dropout": 0.0,
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+ "model_type": "modernbert",
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+ "norm_bias": false,
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+ "problem_type": "multi_label_classification",
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+ "transformers_version": "4.57.6",
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+ }
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