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Skate-16/Clause-Guard
Clause-Guard is a sentence similarity model from Skate-16. Use it when you need a score for how close two texts are. It is set up for sentence-transformers.
This is a sentence-transformers model finetuned from nlpaueb/legal-bert-base-uncased. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic seโฆ
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From the Hugging Face model README
This is a sentence-transformers model finetuned from nlpaueb/legal-bert-base-uncased. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'["THE LIABILITY OF DELTATHREE FOR DAMAGES OR ALLEGED DAMAGES HEREUNDER, WHETHER IN CONTRACT, TORT OR ANY OTHER LEGAL THEORY, IS LIMITED TO, AND WILL NOT EXCEED, PRIMECALL\'S DIRECT DAMAGES.", "THE LIABILITY OF PRIMECALL FOR DAMAGES OR ALLEGED DAMAGES HEREUNDER, WHETHER IN CONTRACT, TORT OR ANY OTHER LEGAL THEORY, IS LIMITED TO, AND WILL NOT EXCEED, DELTATHREE\'S DIRECT DAMAGES.", \'IN NO EVENT SHALL PRIMECALL BE LIABLE TO DELTATHREE FOR ANY SPECIAL, INCIDENTIAL OR CONSEQUENTIAL DAMAGES, INCLUDING, WITHOUT LIMITATION, LOSS OF PROFITS, REVENUES OR DATA WHETHER BASED ON BREACH OF CONTRACT, TORT OR OTHERWISE, WHETHER OR NOT DELTATHREE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\', \'IN NO EVENT SHALL DELTATHREE BE LIABLE TO PRIMECALL FOR ANY SPECIAL, INCIDENTIAL OR CONSEQUENTIAL DAMAGES, INCLUDING, WITHOUT LIMITATION, LOSS OF PROFITS, REVENUES OR DATA WHETHER BASED ON BREACH OF CONTRACT, TORT OR OTHERWISE, WHETHER OR NOT PRIMECALL HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\']',
'ARTICLE XIII EXCLUSION OF DAMAGES; LIMITATION OF LIABILITY (a) IN NO EVENT SHALL LICENSOR BE LIABLE TO LICENSEE OR TO ANY THIRD PARTY FOR ANY SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES (INCLUDING WITHOUT LIMITATION LOSS OF USE, DATA, BUSINESS OR PROFITS) ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE, OPERATION OR PERFORMANCE OF ANY OF THE LICENSED TECHNOLOGY, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON CONTRACT, WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY BREACH OR FAILURE OF EXPRESS OR IMPLIED WARRANTY OR CONDITION, MISREPRESENTATION OR OTHERWISE, AND WHETHER OR NOT LICENSORHAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSS OR DAMAGE (INCLUDING, BUT NOT LIMITED TO, CLAIMS FOR LOSS OF DATA, GOODWILL, USE OF MONEY OR USE OF THE LICENSED TECHNOLOGY, INTERRUPTION IN USE OR AVAILABILITY OF DATA, STOPPAGE OF OTHER WORK OR IMPAIRMENT OR OTHER ASSETS), ARISING OUT OF BREACH OR FAILURE OF EXPRESS OR IMPLIED WARRANTY OR CONDITION, BREACH OF CONTRACT, MISREPRESENTATION, NEGLIGENCE, STRICT LIABILITY IN TORT, OR OTHERWISE UNDER NO CIRCUMSTANCE SHALL LICENSOR BE LIABLE FOR ANY ACTIONS, CLAIMS OR THE LIKE BY LICENSEE OR ANY THIRD PARTY THAT THE USE OF THE LICENSED TECHNOLOGY HAS RESULTED, RESULTS OR MAY RESULT IN ANY INFRINGEMENT,',
'For purposes of this Agreement, any merger, consolidation, or change of corporate structure following which there is a Change of Control of Kitov shall be considered as an assignment by Kitov, allowing Dexcel to terminate the Agreement as heretofore provided.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
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| sentence_0 | sentence_1 | label | |
|---|---|---|---|
| type | string | string | float |
| details | <ul><li>min: 15 tokens</li><li>mean: 166.27 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 131.56 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 0.87</li><li>mean: 0.96</li><li>max: 1.0</li></ul> |
| sentence_0 | sentence_1 | label |
|---|---|---|
| <code>['Each party hereby grants to the other a non-exclusive, limited license to use its trademarks, service marks or trade names only as specifically described in this Agreement.', "Subject to the terms and conditions of this Agreement, Application Provider hereby grants to Excite@Home a royalty-free, non-exclusive, worldwide license to use, reproduce, distribute, transmit and publicly display the e-centives Content in accordance with this Agreement and to sub-license the Application Content to Excite@Home's wholly-owned subsidiaries or to joint ventures in which Excite@Home participates for the sole purpose of using, reproducing, distributing, transmitting and publicly displaying the e-centives Content in accordance with this Agreement, provided that no such sublicensing shall be to Application Provider Named Competitors."]</code> | <code>Subject to the terms and conditions of this Agreement, Application Provider hereby grants to Excite@Home a royalty-free, non-exclusive, worldwide license to use, reproduce, distribute, transmit and publicly display the e-centives Content in accordance with this Agreement and to sub-license the Application Content to Excite@Home's wholly-owned subsidiaries or to joint ventures in which Excite@Home participates for the sole purpose of using, reproducing, distributing, transmitting and publicly displaying the e-centives Content in accordance with this Agreement, provided that no such sublicensing shall be to Application Provider Named Competitors.</code> | <code>0.9886096715927124</code> |
| <code>['For clarity, ENERGOUS shall not intentionally supply Products, Product Die or comparable products or product die to customers directly or through distribution channels.']</code> | <code>Distributor shall (a) procure the Products solely from STAAR (or its affiliates) and not (b) procure, manufacture, market or sell in the Territory any implantable medical devices that compete directly or indirectly with the Products, during the term of this Agreement.</code> | <code>0.9340255260467529</code> |
| <code>['Except as may otherwise be provided in this Agreement, Consultant may not sell, assign or delegate any rights or obligations under this Agreement.']</code> | <code>Except as may otherwise be provided in this Agreement, Consultant may not sell, assign or delegate any rights or obligations under this Agreement.</code> | <code>0.9797248244285583</code> |
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
per_device_train_batch_size: 16per_device_eval_batch_size: 16multi_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: noprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 3max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}tp_size: 0fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin| Epoch | Step | Training Loss |
|---|---|---|
| 0.4340 | 500 | 0.0 |
| 0.8681 | 1000 | 0.0 |
| 1.3021 | 1500 | 0.0 |
| 1.7361 | 2000 | 0.0 |
| 2.1701 | 2500 | 0.0 |
| 2.6042 | 3000 | 0.0 |
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
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