Fill-Mask
Transformers
Safetensors
Rust
English
modernbert
code
code-search
retrieval
sentence-similarity
bert
deep-learning
machine-learning
nlp
programming
multi-language
python
java
javascript
php
ruby
go
Instructions to use Shuu12121/CodeModernBERT-Owl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shuu12121/CodeModernBERT-Owl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Shuu12121/CodeModernBERT-Owl")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeModernBERT-Owl") model = AutoModelForMaskedLM.from_pretrained("Shuu12121/CodeModernBERT-Owl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - Shuu12121/rust-codesearch-dataset-open | |
| - Shuu12121/java-codesearch-dataset-open | |
| - code-search-net/code_search_net | |
| - google/code_x_glue_ct_code_to_text | |
| language: | |
| - en | |
| pipeline_tag: fill-mask | |
| tags: | |
| - code | |
| - code-search | |
| - retrieval | |
| - sentence-similarity | |
| - bert | |
| - transformers | |
| - deep-learning | |
| - machine-learning | |
| - nlp | |
| - programming | |
| - multi-language | |
| - rust | |
| - python | |
| - java | |
| - javascript | |
| - php | |
| - ruby | |
| - go | |
| new_version: Shuu12121/CodeModernBERT-Owl-v3-len2048 | |
| # **🦉CodeModernBERT-Owl** | |
| 旧版のモデルです。[こちらのモデル](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-v3-len2048)など最新版をお使いください!! | |
| ## **モデルパラメータ / Model Parameters** | |
| | パラメータ / Parameter | 値 / Value | | |
| |-------------------------|------------| | |
| | **vocab_size** | 50,004 | | |
| | **hidden_size** | 768 | | |
| | **num_hidden_layers** | 12 | | |
| | **num_attention_heads**| 12 | | |
| | **intermediate_size** | 3,072 | | |
| | **max_position_embeddings** | 2,048 | | |
| | **type_vocab_size** | 2 | | |
| | **hidden_dropout_prob**| 0.1 | | |
| | **attention_probs_dropout_prob** | 0.1 | | |
| | **local_attention_window** | 128 | | |
| | **rope_theta** | 160,000 | | |
| | **local_attention_rope_theta** | 10,000 | | |
| --- | |
| ## **ライセンス / License** | |
| **Apache-2.0** |