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--- |
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library_name: transformers |
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language: |
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- ru |
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pipeline_tag: feature-extraction |
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datasets: |
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- uonlp/CulturaX |
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--- |
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# ruRoPEBert Classic Model for Russian language |
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This is an encoder model from **Tochka AI** based on the **RoPEBert** architecture, using the cloning method described in [our article on Habr](https://habr.com/ru/companies/tochka/articles/797561/). |
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[CulturaX](https://huggingface.co/papers/2309.09400) dataset was used for model training. The **hivaze/ru-e5-base** (only english and russian embeddings of **intfloat/multilingual-e5-base**) model was used as the original; this model surpasses it and all other models in quality (at the time of creation), according to the `S+W` score of [encodechka](https://github.com/avidale/encodechka) benchmark. |
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The model source code is available in the file [modeling_rope_bert.py](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-2k/blob/main/modeling_rope_bert.py) |
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The model is trained on contexts **up to 2048 tokens** in length, but can be used on larger contexts. |
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## Usage |
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**Important**: To load the model correctly, you must enable dowloading code from the model's repository: `trust_remote_code=True`, this will download the **modeling_rope_bert.py** script and load the weights into the correct architecture. |
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Otherwise, you can download this script manually and use classes from it directly to load the model. |
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### Basic usage (no efficient attention) |
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```python |
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model_name = 'Tochka-AI/ruRoPEBert-e5-base-2k' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True) |
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``` |
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### With SDPA (efficient attention) |
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```python |
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation='sdpa') |
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``` |
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### Getting embeddings |
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The correct pooler (`mean`) is already **built into the model architecture**, which averages embeddings based on the attention mask. You can also select the pooler type (`first_token_transform`), which performs a learnable linear transformation on the first token. |
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To change built-in pooler implementation use `pooler_type` parameter in `AutoModel.from_pretrained` function |
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```python |
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test_batch = tokenizer.batch_encode_plus(["Привет, чем занят?", "Здравствуйте, чем вы занимаетесь?"], return_tensors='pt', padding=True) |
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with torch.inference_mode(): |
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pooled_output = model(**test_batch).pooler_output |
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``` |
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In addition, you can calculate cosine similarities between texts in batch using normalization and matrix multiplication: |
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```python |
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import torch.nn.functional as F |
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F.normalize(pooled_output, dim=1) @ F.normalize(pooled_output, dim=1).T |
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``` |
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### Using as classifier |
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To load the model with trainable classification head on top (change `num_labels` parameter): |
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```python |
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model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, attn_implementation='sdpa', num_labels=4) |
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``` |
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### With RoPE scaling |
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Allowed types for RoPE scaling are: `linear` and `dynamic`. To extend the model's context window you need to change tokenizer max length and add `rope_scaling` parameter. |
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If you want to scale your model context by 2x: |
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```python |
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tokenizer.model_max_length = 4096 |
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model = AutoModel.from_pretrained(model_name, |
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trust_remote_code=True, |
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attn_implementation='sdpa', |
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rope_scaling={'type': 'dynamic','factor': 2.0} |
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) # 2.0 for x2 scaling, 4.0 for x4, etc.. |
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``` |
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P.S. Don't forget to specify the dtype and device you need to use resources efficiently. |
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## Metrics |
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Evaluation of this model on encodechka benchmark: |
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| Model name | STS | PI | NLI | SA | TI | IA | IC | ICX | NE1 | NE2 | Avg S (no NE) | Avg S+W (with NE) | |
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|---------------------|-----|------|-----|-----|-----|-----|-----|-----|-----|-----|---------------|-------------------| |
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| ruRoPEBert-e5-base-512 | 0.793 | 0.704 | 0.457 | 0.803 | 0.970 | 0.788 | 0.802 | 0.749 | 0.328 | 0.396 | 0.758 | 0.679 | |
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| **ruRoPEBert-e5-base-2k** | 0.787 | 0.708 | 0.460 | 0.804 | 0.970 | 0.792 | 0.803 | 0.749 | 0.402 | 0.423 | 0.759 | 0.689 | |
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| intfloat/multilingual-e5-base | 0.834 | 0.704 | 0.458 | 0.795 | 0.964 | 0.782 | 0.803 | 0.740 | 0.234 | 0.373 | 0.76 | 0.668 | |
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## Authors |
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- Sergei Bratchikov (Tochka AI Team, [HF](https://huggingface.co/hivaze), [GitHub](https://huggingface.co/hivaze)) |
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- Maxim Afanasiev (Tochka AI Team, [HF](https://huggingface.co/mrapplexz), [GitHub](https://github.com/mrapplexz)) |
