Text Classification
Transformers
Safetensors
modernbert
code
language-identification
multi-label
llm-guard
encoder
text-embeddings-inference
Instructions to use Accuknoxtechnologies/CodeLanguage-Encoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Accuknoxtechnologies/CodeLanguage-Encoder-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Accuknoxtechnologies/CodeLanguage-Encoder-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Accuknoxtechnologies/CodeLanguage-Encoder-v1") model = AutoModelForSequenceClassification.from_pretrained("Accuknoxtechnologies/CodeLanguage-Encoder-v1") - Notebooks
- Google Colab
- Kaggle
File size: 2,921 Bytes
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"architectures": [
"ModernBertForSequenceClassification"
],
"attention_bias": false,
"attention_dropout": 0.0,
"base_model": "jhu-clsp/mmBERT-base",
"bos_token_id": 2,
"classifier_activation": "gelu",
"classifier_bias": false,
"classifier_dropout": 0.0,
"classifier_pooling": "mean",
"cls_token_id": 1,
"decoder_bias": true,
"deterministic_flash_attn": false,
"dtype": "float32",
"embedding_dropout": 0.0,
"eos_token_id": 1,
"global_attn_every_n_layers": 3,
"global_rope_theta": 160000,
"gradient_checkpointing": false,
"hidden_activation": "gelu",
"hidden_size": 768,
"id2label": {
"0": "Python",
"1": "JavaScript",
"2": "Java",
"3": "C",
"4": "C++",
"5": "C#",
"6": "Go",
"7": "Rust",
"8": "Kotlin",
"9": "Swift",
"10": "Ruby",
"11": "R",
"12": "Scala",
"13": "Perl",
"14": "Lua",
"15": "Bash",
"16": "PowerShell",
"17": "Batch",
"18": "SQL",
"19": "Dockerfile",
"20": "YAML",
"21": "Makefile",
"22": "Terraform",
"23": "AWK",
"24": "jq"
},
"initializer_cutoff_factor": 2.0,
"initializer_range": 0.02,
"intermediate_size": 1152,
"label2id": {
"AWK": 23,
"Bash": 15,
"Batch": 17,
"C": 3,
"C#": 5,
"C++": 4,
"Dockerfile": 19,
"Go": 6,
"Java": 2,
"JavaScript": 1,
"Kotlin": 8,
"Lua": 14,
"Makefile": 21,
"Perl": 13,
"PowerShell": 16,
"Python": 0,
"R": 11,
"Ruby": 10,
"Rust": 7,
"SQL": 18,
"Scala": 12,
"Swift": 9,
"Terraform": 22,
"YAML": 20,
"jq": 24
},
"layer_norm_eps": 1e-05,
"local_attention": 128,
"local_rope_theta": 160000,
"mask_token_id": 4,
"max_position_embeddings": 8192,
"mlp_bias": false,
"mlp_dropout": 0.0,
"model_type": "modernbert",
"norm_bias": false,
"norm_eps": 1e-05,
"num_attention_heads": 12,
"num_hidden_layers": 22,
"pad_token_id": 0,
"position_embedding_type": "sans_pos",
"problem_type": "multi_label_classification",
"repad_logits_with_grad": false,
"sep_token_id": 1,
"sparse_pred_ignore_index": -100,
"sparse_prediction": false,
"training_provenance": {
"base_model": "jhu-clsp/mmBERT-base",
"epochs": 2,
"labels": [
"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"
],
"learning_rate": 2e-05,
"max_seq_length": 3072,
"problem_type": "multi_label_classification",
"task": "code-language-identification",
"threshold": 0.5,
"trained_at": "2026-06-02T09:22:34+00:00"
},
"transformers_version": "4.57.6",
"vocab_size": 256000
}
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