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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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- word-similarity |
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- transformers |
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widget: |
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- source_sentence: "Provide a large table; this is a horizontal <t>plane</t>, and will represent the ground plane, viz." |
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sentences: |
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- "The President's <t>plane</t> landed at Goose Bay at 9:03 p.m." |
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- "any line joining two points on a <t>plane</t> lies wholly on that plane" |
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- "the flight was delayed due to trouble with the <t>plane</t>" |
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example_title: "plane" |
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--- |
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# pierluigic/xl-lexeme |
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This is model is based on [sentence-transformers](https://www.SBERT.net): It maps target word in sentences to a 1024 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 (WordTransformer) |
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Install the library: |
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``` |
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git clone [email protected]:pierluigic/xl-lexeme.git |
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cd xl-lexeme |
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pip3 install . |
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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 WordTransformer import WordTransformer, InputExample |
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model = WordTransformer('pierluigic/xl-lexeme') |
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examples = InputExample(texts="the quick fox jumps over the lazy dog", positions=[10,13]) |
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fox_embedding = model.encode(examples) #The embedding of the target word "fox" |
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``` |
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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 16531 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.ContrastiveLoss.ContrastiveLoss` with parameters: |
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``` |
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{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 10, |
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"evaluation_steps": 4132, |
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'transformers.optimization.AdamW'>", |
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"optimizer_params": { |
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"lr": 1e-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": 16531.0, |
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"weight_decay": 0.0 |
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} |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformerTarget( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, '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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) |
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``` |
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## Citing & Authors |
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``` |
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@inproceedings{cassotti-etal-2023-xl, |
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title = "{XL}-{LEXEME}: {W}i{C} Pretrained Model for Cross-Lingual {LEX}ical s{EM}antic chang{E}", |
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author = "Cassotti, Pierluigi and |
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Siciliani, Lucia and |
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DeGemmis, Marco and |
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Semeraro, Giovanni and |
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Basile, Pierpaolo", |
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booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", |
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month = jul, |
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year = "2023", |
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address = "Toronto, Canada", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.acl-short.135", |
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pages = "1577--1585" |
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} |
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``` |
