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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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license: apache-2.0 |
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widget: |
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- source_sentence: Κύρωση της Συνθήκης επί των συμβατικών δυνάμεων στην Ευρώπη. |
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sentences: |
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- Κύρωση του τελικού κειμένου της Έκτακτης Διάσκεψηςτων Χωρών που μετέχουν στη Συνθήκη για τις Συμβατικές Ένοπλες Δυνάμεις στην Ευρώπη. |
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- Κύρωση της Συνθήκης επί των συμβατικών δυνάμεων στην Ευρώπη μετά των συνημμένων σ` αυτήν ( 8 ) Πρωτοκόλλων και προσαρτημάτων αυτής. |
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- Η διαδικασία ολοκληρώνεται με την υπογραφή και την κύρωση της συνθήκης ένταξης, μόνο εφόσον όλα τα μέρη είναι ικανοποιημένα. |
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datasets: |
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- greek_legal_code |
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language: |
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- el |
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--- |
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# Raptarchis Embeddings Model |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["Κύρωση της Συνθήκης επί των συμβατικών δυνάμεων στην Ευρώπη", "Οι επιθεωρητές θα είναι πολίτες του επιθεωρούντος Συμβαλλομένου ή άλλων Συμβαλλομένων Κρατών"] |
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model = SentenceTransformer('livieris/Raptarchis') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) |
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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 58398 with parameters: |
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``` |
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 1, |
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"evaluation_steps": 250, |
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"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
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"optimizer_params": { |
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"eps": 1e-06, |
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"lr": 2e-05 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 10000, |
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"weight_decay": 0.01 |
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} |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
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(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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``` |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
