Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:29547
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
Instructions to use BelisaDi/stella-tuned-rirag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BelisaDi/stella-tuned-rirag with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BelisaDi/stella-tuned-rirag", trust_remote_code=True) sentences = [ "When calculating regulatory capital, which guidance note outlines the potential for an increased valuation adjustment for less liquid positions that may surpass the adjustments made for financial reporting purposes?", "REGULATORY REQUIREMENTS - SPOT COMMODITY ACTIVITIES\nSpot Commodities and Accepted Spot Commodities\nAuthorised Persons will need to submit the details of how each Accepted Spot Commodity that is proposed to be used meets the requirements for the purposes of COBS Rule 22.2.2 and paragraphs 25 and 26 above. The use of each Accepted Spot Commodity will be approved as part of the formal application process for review and approval of an FSP. Though an Authorised Person may, for example, propose to admit to trading a commonly traded Spot Commodity, the Authorised Person’s controls relating to responsible and sustainable sourcing, and sound delivery mechanisms may not yet be fully developed. In such circumstances, the FSRA may require the Authorised Person to delay the commencement of trading until such time that suitable controls have been developed and implemented.\n", "Adjustment to the current valuation of less liquid positions for regulatory capital purposes. The adjustment to the current valuation of less liquid positions made under Guidance note 11 is likely to impact minimum Capital Requirements and may exceed those valuation adjustments made under the International Financial Reporting Standards and Guidance notes 8 and 9.\n\n", "REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS\nAnti-Money Laundering and Countering Financing of Terrorism\nIn order to develop a robust and sustainable regulatory framework for Virtual Assets, FSRA is of the view that a comprehensive application of its AML/CFT framework should be in place, including full compliance with, among other things, the:\n\na)\tUAE AML/CFT Federal Laws, including the UAE Cabinet Resolution No. (10) of 2019 Concerning the Executive Regulation of the Federal Law No. 20 of 2018 concerning Anti-Money Laundering and Combating Terrorism Financing;\n\nb)\tUAE Cabinet Resolution 20 of 2019 concerning the procedures of dealing with those listed under the UN sanctions list and UAE/local terrorist lists issued by the Cabinet, including the FSRA AML and Sanctions Rules and Guidance (“AML Rules”) or such other AML rules as may be applicable in ADGM from time to time; and\n\nc)\tadoption of international best practices (including the FATF Recommendations).\n" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| # coding=utf-8 | |
| # Copyright 2024 The GTE Team Authors and Alibaba Group. | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ NEW model configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class NewConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to | |
| instantiate a NEW model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration with the defaults will yield a similar configuration to that of the NEW | |
| [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"silu"` and `"gelu_new"` are supported. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (`int`, *optional*, defaults to 512): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| type_vocab_size (`int`, *optional*, defaults to 2): | |
| The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`]. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| position_embedding_type (`str`, *optional*, defaults to `"rope"`): | |
| Type of position embedding. Choose one of `"absolute"`, `"rope"`. | |
| rope_theta (`float`, *optional*, defaults to 10000.0): | |
| The base period of the RoPE embeddings. | |
| rope_scaling (`Dict`, *optional*): | |
| Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling | |
| strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is | |
| `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update | |
| `max_position_embeddings` to the expected new maximum. See the following thread for more information on how | |
| these scaling strategies behave: | |
| https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an | |
| experimental feature, subject to breaking API changes in future versions. | |
| classifier_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| Examples: | |
| ```python | |
| >>> from transformers import NewConfig, NewModel | |
| >>> # Initializing a NEW izhx/new-base-en style configuration | |
| >>> configuration = NewConfig() | |
| >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration | |
| >>> model = NewModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "new" | |
| def __init__( | |
| self, | |
| vocab_size=30528, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.0, | |
| max_position_embeddings=2048, | |
| type_vocab_size=1, | |
| initializer_range=0.02, | |
| layer_norm_type='layer_norm', | |
| layer_norm_eps=1e-12, | |
| # pad_token_id=0, | |
| position_embedding_type="rope", | |
| rope_theta=10000.0, | |
| rope_scaling=None, | |
| classifier_dropout=None, | |
| pack_qkv=True, | |
| unpad_inputs=False, | |
| use_memory_efficient_attention=False, | |
| logn_attention_scale=False, | |
| logn_attention_clip1=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_type = layer_norm_type | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.rope_theta = rope_theta | |
| self.rope_scaling = rope_scaling | |
| self.classifier_dropout = classifier_dropout | |
| self.pack_qkv = pack_qkv | |
| self.unpad_inputs = unpad_inputs | |
| self.use_memory_efficient_attention = use_memory_efficient_attention | |
| self.logn_attention_scale = logn_attention_scale | |
| self.logn_attention_clip1 = logn_attention_clip1 | |