florian-hoenicke commited on
Commit
dbd8e73
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feat: push custom model

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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - fine-tuned/jinaai_jina-embeddings-v2-base-en-11072024-bh6v-webapp
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+ - allenai/c4
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+ language:
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+ - en
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+ pipeline_tag: feature-extraction
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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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+ - mteb
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+ - Insurance
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+ - Coverage
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+ - Protection
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+ - Health
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+ - Liability
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+ ---
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+ This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
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+
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+ insurance services
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+
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+ ## How to Use
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+ This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+ from sentence_transformers.util import cos_sim
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+
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+ model = SentenceTransformer(
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+ 'fine-tuned/jinaai_jina-embeddings-v2-base-en-11072024-bh6v-webapp',
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+ trust_remote_code=True
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+ )
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+
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+ embeddings = model.encode([
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+ 'first text to embed',
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+ 'second text to embed'
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+ ])
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+ print(cos_sim(embeddings[0], embeddings[1]))
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "mytmp/finetuned_model",
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+ "architectures": [
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+ "JinaBertModel"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "attn_implementation": null,
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+ "auto_map": {
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+ "AutoConfig": "configuration_bert.JinaBertConfig",
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+ "AutoModel": "modeling_bert.JinaBertModel",
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+ "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
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+ "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
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+ },
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+ "classifier_dropout": null,
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+ "emb_pooler": "mean",
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+ "feed_forward_type": "geglu",
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 8192,
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+ "model_max_length": 8192,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "alibi",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.40.2",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30528
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+ }
config_sentence_transformers.json ADDED
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+ {
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+ "__version__": {
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+ "sentence_transformers": "2.7.0",
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+ "transformers": "4.40.2",
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+ "pytorch": "2.3.1+cu121"
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+ },
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+ "prompts": {},
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+ "default_prompt_name": null
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+ }
configuration_bert.py ADDED
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+ # coding=utf-8
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+ # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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+ # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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+ # Copyright (c) 2023 Jina AI GmbH. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ BERT model configuration"""
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+ from collections import OrderedDict
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+ from typing import Mapping
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.onnx import OnnxConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class JinaBertConfig(PretrainedConfig):
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+ r"""
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+ This is the configuration class to store the configuration of a [`JinaBertModel`]. It is used to
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+ instantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating a
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+ configuration with the defaults will yield a similar configuration to that of the BERT
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+ [bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 30522):
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+ Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
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+ hidden_size (`int`, *optional*, defaults to 768):
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+ Dimensionality of the encoder layers and the pooler layer.
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+ num_hidden_layers (`int`, *optional*, defaults to 12):
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+ Number of hidden layers in the Transformer encoder.
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+ num_attention_heads (`int`, *optional*, defaults to 12):
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+ Number of attention heads for each attention layer in the Transformer encoder.
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+ intermediate_size (`int`, *optional*, defaults to 3072):
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+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
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+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
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+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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+ The dropout ratio for the attention probabilities.
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+ max_position_embeddings (`int`, *optional*, defaults to 512):
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+ The maximum sequence length that this model might ever be used with. Typically set this to something large
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+ just in case (e.g., 512 or 1024 or 2048).
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+ type_vocab_size (`int`, *optional*, defaults to 2):
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+ The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
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+ initializer_range (`float`, *optional*, defaults to 0.02):
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+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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+ The epsilon used by the layer normalization layers.
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+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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+ positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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+ with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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+ is_decoder (`bool`, *optional*, defaults to `False`):
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+ Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models). Only
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+ relevant if `config.is_decoder=True`.
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+ classifier_dropout (`float`, *optional*):
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+ The dropout ratio for the classification head.
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+ feed_forward_type (`str`, *optional*, defaults to `"original"`):
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+ The type of feed forward layer to use in the bert layers.
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+ Can be one of GLU variants, e.g. `"reglu"`, `"geglu"`
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+ emb_pooler (`str`, *optional*, defaults to `None`):
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+ The function to use for pooling the last layer embeddings to get the sentence embeddings.
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+ Should be one of `None`, `"mean"`.
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+ attn_implementation (`str`, *optional*, defaults to `"torch"`):
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+ The implementation of the self-attention layer. Can be one of:
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+ - `None` for the original implementation,
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+ - `torch` for the PyTorch SDPA implementation,
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+
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+ Examples:
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+
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+ ```python
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+ >>> from transformers import JinaBertConfig, JinaBertModel
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+
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+ >>> # Initializing a JinaBert configuration
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+ >>> configuration = JinaBertConfig()
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+
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+ >>> # Initializing a model (with random weights) from the configuration
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+ >>> model = JinaBertModel(configuration)
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+
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+ >>> # Accessing the model configuration
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+ >>> configuration = model.config
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+
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+ >>> # Encode text inputs
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+ >>> embeddings = model.encode(text_inputs)
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+ ```"""
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+ model_type = "bert"
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+
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+ def __init__(
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+ self,
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+ vocab_size=30522,
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+ hidden_size=768,
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+ num_hidden_layers=12,
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+ num_attention_heads=12,
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+ intermediate_size=3072,
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+ hidden_act="gelu",
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+ hidden_dropout_prob=0.1,
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+ attention_probs_dropout_prob=0.1,
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+ max_position_embeddings=512,
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+ type_vocab_size=2,
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+ initializer_range=0.02,
124
+ layer_norm_eps=1e-12,
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+ pad_token_id=0,
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+ position_embedding_type="absolute",
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+ use_cache=True,
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+ classifier_dropout=None,
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+ feed_forward_type="original",
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+ emb_pooler=None,
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+ attn_implementation='torch',
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+ **kwargs,
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+ ):
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+ super().__init__(pad_token_id=pad_token_id, **kwargs)
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+
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+ self.vocab_size = vocab_size
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+ self.hidden_size = hidden_size
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+ self.num_hidden_layers = num_hidden_layers
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+ self.num_attention_heads = num_attention_heads
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+ self.hidden_act = hidden_act
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+ self.intermediate_size = intermediate_size
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+ self.hidden_dropout_prob = hidden_dropout_prob
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+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
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+ self.max_position_embeddings = max_position_embeddings
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+ self.type_vocab_size = type_vocab_size
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+ self.initializer_range = initializer_range
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+ self.layer_norm_eps = layer_norm_eps
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+ self.position_embedding_type = position_embedding_type
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+ self.use_cache = use_cache
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+ self.classifier_dropout = classifier_dropout
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+ self.feed_forward_type = feed_forward_type
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+ self.emb_pooler = emb_pooler
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+ self.attn_implementation = attn_implementation
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+
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+ class JinaBertOnnxConfig(OnnxConfig):
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+ @property
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+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
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+ if self.task == "multiple-choice":
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+ dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
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+ else:
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+ dynamic_axis = {0: "batch", 1: "sequence"}
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+ return OrderedDict(
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+ [
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+ ("input_ids", dynamic_axis),
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+ ("attention_mask", dynamic_axis),
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+ ("token_type_ids", dynamic_axis),
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+ ]
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+ )
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modeling_bert.py ADDED
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+ "name": "2",
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+ "path": "2_Normalize",
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+ "type": "sentence_transformers.models.Normalize"
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+ {
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+ "max_seq_length": 8192,
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+ "do_lower_case": false
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+ }
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