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: 600 Bytes
36550eb 7bc7eaf 36550eb 7bc7eaf 36550eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"base_model": "jhu-clsp/mmBERT-base",
"task": "code-language-identification",
"problem_type": "multi_label_classification",
"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"
],
"max_seq_length": 3072,
"epochs": 2,
"learning_rate": 2e-05,
"threshold": 0.5,
"trained_at": "2026-06-02T09:22:34+00:00"
} |