final

.gitignore

@@ -0,0 +1,5 @@
+*.pyc
+*39.pyc
+myenv/
+build/
+dist/

Kubernetes/responsible-ai-moderation-model.yaml

@@ -0,0 +1,62 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: responsible-ai-moderation-model
+  namespace: irai-toolkit-test
+  labels:
+    app: responsible-ai-moderation-model
+spec:
+  type: ClusterIP
+  ports:
+  - port: 8000
+  selector:
+    app: responsible-ai-moderation-model
+---
+apiVersion: apps/v1
+kind: Deployment
+seccompProfile:
+type: "RuntimeDefault"
+automountServiceAccountToken: false
+metadata:
+  name: responsible-ai-moderation-model
+  namespace: irai-toolkit-test
+  labels:
+    app: responsible-ai-moderation-model
+    version: v1
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: responsible-ai-moderation-model
+      version: v1
+  template:
+    metadata:
+      labels:
+        app: responsible-ai-moderation-model
+        version: v1
+    spec:
+      automountServiceAccountToken: false # Disable token mounting
+      imagePullSecrets:
+         - name: docker-secret
+      containers:
+        - envFrom:
+          - configMapRef:
+              name: privacy-config
+          image: <Image Name>
+          imagePullPolicy: Always
+          name: responsible-ai-privacy
+          ports:
+            - containerPort: 8000
+          securityContext:
+            runAsUser: 1000  # Non-root user
+            runAsGroup: 1000
+            capabilities:
+              drop:
+                - ALL  # Drop all capabilities
+            seccompProfile:
+              type: RuntimeDefault
+          resources:
+            limits:
+                #cpu: '2'
+                memory: '8Gi'
+                nvidia.com/gpu: 1

LICENSE.txt

@@ -0,0 +1,9 @@
+The MIT License (MIT)
+
+Copyright 2024-2025 Infosys Ltd.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -0,0 +1,166 @@
+# Responsible-AI-Moderation Model 
+
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Features](#features)
+- [Installation](#installation)
+- [Set Configuration Variables](#set-configuration-variables)
+- [Models Required](#models-required)
+- [Running the Application](#running-the-application)
+- [Docker Image](#Docker-Image) 
+- [License](#license)
+- [Contact](#contact)
+  
+## Introduction
+The **Moderation Model** module acts as a central hub for machine learning models for prompt injection, toxicity, jailbreak, restricted topic, custom theme and refusal checks. It provides the endpoints to utilize the response generated by these models.
+
+## Features
+The **Moderation Model** module acts as a wrapper for the traditional AI models we are using for various checks like prompt injection, jailbreak, toxicity etc. 
+
+## Installation
+To run the application, first we need to install Python and the necessary packages:
+
+1. Install Python (version 3.11.x) from the [official website](https://www.python.org/downloads/) and ensure it is added to your system PATH.
+
+2. Clone the repository : responsible-ai-mm-flask:
+    ```sh
+    git clone <repository-url>
+    ```
+
+3. Navigate to the `responsible-ai-mm-flask` directory:
+    ```sh
+    cd responsible-ai-mm-flask
+    ```
+
+4. Create a virtual environment:
+    ```sh
+    python -m venv venv
+    ```
+
+5. Activate the virtual environment:
+    - On Windows:
+        ```sh
+        .\venv\Scripts\activate
+         ```
+
+6. Go to the `requirements` directory where the `requirement.txt` file is present.
+    In the `requirement.txt` file comment the 
+    ```sh
+    lib/torch-2.2.0+cu118-cp39-cp39-linux_x86_64.whl  
+    ```
+    **Note:** Download appropriate torch version supporting python version which is installed [i.e if Python version is 3.10 use torch-2.2.0+cu118-**cp310**-**cp310**-**linux**_x86_64.whl, where cp310 denotes python version 3.10 and linux denotes OS which can be linux/win and **_not applicable for Mac_**]
+   
+    **Note:** If working in windows as this is for linux and replace 
+    ```sh
+    lib/
+    ```
+    with 
+    ```sh
+    ../lib/
+    ```
+  **Note:** If working in Mac Os, run the below command after running requirement.txt
+     ```sh
+   pip install --pre torch torchvision torchaudio \--extra-index-url https://download.pytorch.org/whl/nightly/cpu
+    ```
+  
+  Download and place the en_core_web_lg-3.5.0-py3-none-any.whl inside the lib folder.
+    [en_core_web_lg](https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.5.0/en_core_web_lg-3.5.0-py3-none-any.whl) and install the requirements:
+    
+    ```sh
+    pip install -r requirement.txt
+    ```
+    
+  **Note:** when running requirement.txt, if getting error related to "cuda-python" then comment cuda-python from 
+          requirement.txt file and run pip install again
+    Install the fastapi library as well, use the following command:
+    ```sh
+    pip install fastapi
+    ```
+## Set Configuration Variables
+After installing all the required packages, configure the variables necessary to run the APIs.
+
+1. Navigate to the `src` directory:
+    ```sh
+    cd ..
+    ```
+
+2. Locate the `.env` file, which contains keys like the following:
+
+  ```sh
+  workers=1
+  WORKERS="${workers}"
+  # DB_NAME="${dbname}"
+  # DB_USERNAME="${username}"
+  # DB_PWD="${password}"
+  # DB_IP="${ipaddress}"
+  # DB_PORT="${port}"
+  # MONGO_PATH="mongodb://${DB_USERNAME}:${DB_PWD}@${DB_IP}:${DB_PORT}/"
+  # MONGO_PATH= "mongodb://localhost:27017/"
+  ```
+
+3. Replace the placeholders with your actual values.
+
+## Models Required
+The following models are required to run the application. Download all the model files from the links provided, and place it in the folder name provided.
+
+1. [Prompt Injection](https://huggingface.co/deepset/deberta-v3-base-injection/tree/main)
+Files required to download here are : model.safetensors, config.json, tokenizer_config.json, tokenizer.json, special_tokens_map.json.
+Name the folder as 'dbertaInjection'.
+
+2. [Restricted Topic](https://huggingface.co/MoritzLaurer/deberta-v3-base-zeroshot-v2.0/tree/main)
+Files required to download here are : model.safetensors, added_tokens.json, config.json, special_tokens_map.json, spm.model, tokenizer.json, tokenizer_config.json.
+Name the folder as 'restricted-dberta-base-zeroshot-v2'.
+
+3. [Sentence Transformer Model](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1/tree/main)
+Files required to download here are : 1_Pooling folder, pytorch_model.bin, vocab.txt, tokenizer.json, tokenizer_config.json, special_tokens_map.json, sentence_bert_config.json, modules.json, config.json, config_sentence_transformers.json.
+Name the folder as 'multi-qa-mpnet-base-dot-v1'.
+
+4. [Detoxify](https://huggingface.co/FacebookAI/roberta-base/tree/main)
+Files required to download here are : vocab.json, tokenizer.json, merges.txt, config.json.
+Now download the model checkpoint file from this url and keep it under this folder -
+[toxic_model_ckpt_file](https://github.com/unitaryai/detoxify/releases/download/v0.3-alpha/toxic_debiased-c7548aa0.ckpt)
+Name the folder as 'detoxify'.
+
+5. [Gibberish](https://huggingface.co/madhurjindal/autonlp-Gibberish-Detector-492513457)
+Files required to download here are : vocab.json, tokenizer.json,config.json,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
+Name the folder as 'gibberish'.
+
+6. [Bancode](https://huggingface.co/vishnun/codenlbert-tiny)
+Files required to download here are : vocab.txt, tokenizer.json,config.json,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
+Name the folder as 'bancode'.
+
+7. [Restricted Topic](https://huggingface.co/cross-encoder/nli-MiniLM2-L6-H768)
+Files required to download here are : vocab.json, tokenizer.json,config.json,merges.txt,pytorch_model.bin, tokenizer_config.json,special_tokens_map.json.
+Name the folder as 'nli-MiniLM2-L6-H768'.
+
+Place the above folders in a folder named 'models' in the following way: 'responsible-ai-mm-flask/models'.
+
+## Running the Application
+Once we have completed all the aforementioned steps, we can start the service.
+
+1. Navigate to the `src` directory:
+
+2. Run `main_MM.py` file:
+    ```sh
+    python main_MM.py
+     ```
+
+3. PORT_NO : Use the Port No that is configured in .env file.
+
+   Open the following URL in your browser:
+  `http://localhost:8000/rai/v1/raimoderationmodels/docs`
+
+**Note:** :
+1. To address the issue where the Passport Number is not recognized in Privacy, modify the "piiEntitiesToBeRedacted" field in the privacy() under service.py file (line no: 98) from None to an empty list []. This adjustment ensures that the Passport Number is correctly identified.
+
+2. Do not use this Moderation Model repository as a standalone repository. It serves as the base or dependency for the Moderation Layer repository, which provides the 'Guardrail' functionality, so access this repository APIs through Moderation layer.
+
+## Docker Image
+The Docker image for the ModerationModel module has been published on Docker Hub. You can access it here: [ModerationModel image](https://hub.docker.com/repository/docker/infosysresponsibleaitoolkit/responsible-ai-moderationmodel)
+
+## License
+The source code for the project is licensed under the MIT license, which you can find in the [LICENSE.txt](LICENSE.txt) file.
+
+## Contact
+If you have more questions or need further insights please feel free to connect with us @ Infosysraitoolkit@infosys.com

execreation.bat

@@ -0,0 +1,12 @@
+@echo off
+echo started exe creation!! Please wait....It might take an hour depending on computation speed
+set /p site_packages="Enter the path to the site-packages directory: "
+cd %~dp0
+echo %~dp0
+powershell -Command "(Get-Content src\main.py) | ForEach-Object { if ($_ -match 'from routing\.safety_router import img_router|app\.register_blueprint\(img_router,url_prefix=''\/rai\/v1\/raimoderationmodels''\)') { '#'+ $_ } else { $_ } } | Set-Content src\main.py"
+pyinstaller --add-data "src\logger.ini;." --add-data "models;models" --add-data "src\static;src" --add-data "%site_packages%;." --add-data "data;." --hidden-import=transformers src\main.py
+if ERRORLEVEL 1 (
+    echo PyInstaller encountered an error. Check the output above for details.
+) else (
+    echo Exe creation was successful!
+)

lib/privacy-2.0.8-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c22356831d15244ed329527f3b2a0683e134c90796b74bf20a0151fcdb3705d
+size 129207

models/bancode/config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "prajjwal1/bert-tiny",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "CODE",
+    "1": "NL"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "CODE": 0,
+    "NL": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

models/bancode/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

models/bancode/tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

models/dbertaInjection/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

models/dbertaInjection/config.json

@@ -0,0 +1,43 @@
+{
+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LEGIT",
+    "1": "INJECTION"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "INJECTION": 1,
+    "LEGIT": 0
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

models/dbertaInjection/gitattributes.txt

@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/dbertaInjection/gitignore.txt

@@ -0,0 +1 @@
+checkpoint-*/

models/dbertaInjection/special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

models/dbertaInjection/spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

models/dbertaInjection/tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

models/dbertaInjection/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a69a93fd6f6ff40d061d2890cc2c653840203973675b926ca88eacda8a741022
+size 3963

models/detoxify/config.json

@@ -0,0 +1,21 @@
+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "type_vocab_size": 1,
+  "vocab_size": 50265
+}

models/gibberish/config.json

@@ -0,0 +1,40 @@
+{
+  "_name_or_path": "AutoNLP",
+  "_num_labels": 4,
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "clean",
+    "1": "mild gibberish",
+    "2": "noise",
+    "3": "word salad"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "clean": 0,
+    "mild gibberish": 1,
+    "noise": 2,
+    "word salad": 3
+  },
+  "max_length": 64,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "padding": "max_length",
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.15.0",
+  "vocab_size": 30522
+}

models/gibberish/special_tokens_map.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

models/gibberish/tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "AutoNLP", "tokenizer_class": "DistilBertTokenizer"}

models/multi-qa-mpnet-base-dot-v1/1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

models/multi-qa-mpnet-base-dot-v1/README.md

@@ -0,0 +1,184 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+datasets:
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- search_qa
+- eli5
+- natural_questions
+- trivia_qa
+- embedding-data/QQP
+- embedding-data/PAQ_pairs
+- embedding-data/Amazon-QA
+- embedding-data/WikiAnswers
+
+---
+
+# multi-qa-mpnet-base-dot-v1
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
+
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer, util
+
+query = "How many people live in London?"
+docs = ["Around 9 Million people live in London", "London is known for its financial district"]
+
+#Load the model
+model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-dot-v1')
+
+#Encode query and documents
+query_emb = model.encode(query)
+doc_emb = model.encode(docs)
+
+#Compute dot score between query and all document embeddings
+scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
+
+#Combine docs & scores
+doc_score_pairs = list(zip(docs, scores))
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+#Output passages & scores
+for doc, score in doc_score_pairs:
+    print(score, doc)
+```
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+#CLS Pooling - Take output from first token
+def cls_pooling(model_output):
+    return model_output.last_hidden_state[:,0]
+
+#Encode text
+def encode(texts):
+    # Tokenize sentences
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    with torch.no_grad():
+        model_output = model(**encoded_input, return_dict=True)
+
+    # Perform pooling
+    embeddings = cls_pooling(model_output)
+
+    return embeddings
+
+
+# Sentences we want sentence embeddings for
+query = "How many people live in London?"
+docs = ["Around 9 Million people live in London", "London is known for its financial district"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
+model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-dot-v1")
+
+#Encode query and docs
+query_emb = encode(query)
+doc_emb = encode(docs)
+
+#Compute dot score between query and all document embeddings
+scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
+
+#Combine docs & scores
+doc_score_pairs = list(zip(docs, scores))
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+#Output passages & scores
+for doc, score in doc_score_pairs:
+    print(score, doc)
+```
+
+## Technical Details
+
+In the following some technical details how this model must be used:
+
+| Setting | Value |
+| --- | :---: |
+| Dimensions | 768 |
+| Produces normalized embeddings | No |
+| Pooling-Method | CLS pooling |
+| Suitable score functions | dot-product (e.g. `util.dot_score`) |
+
+----
+
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developped this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developped this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
+
+Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text. 
+
+
+
+## Training procedure
+
+The full training script is accessible in this current repository: `train_script.py`.
+
+### Pre-training 
+
+We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+#### Training
+
+We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using CLS-pooling, dot-product as similarity function, and a scale of 1.
+
+
+
+
+| Dataset                    | Number of training tuples  |
+|--------------------------------------------------------|:--------------------------:|
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers |  77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges  | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges  |  21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine |  17,579,773 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet  | 3,012,496 |
+| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839 
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
+| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset |  87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
+| **Total** | **214,988,242** |

models/multi-qa-mpnet-base-dot-v1/config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "C:\\Users\\prajwal.chatra/.cache\\torch\\sentence_transformers\\sentence-transformers_multi-qa-mpnet-base-dot-v1\\",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.1",
+  "vocab_size": 30527
+}

models/multi-qa-mpnet-base-dot-v1/config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

models/multi-qa-mpnet-base-dot-v1/modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

models/multi-qa-mpnet-base-dot-v1/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

models/multi-qa-mpnet-base-dot-v1/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "[UNK]"
+}

models/multi-qa-mpnet-base-dot-v1/tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "name_or_path": "C:\\Users\\prajwal.chatra/.cache\\torch\\sentence_transformers\\sentence-transformers_multi-qa-mpnet-base-dot-v1\\",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}

models/nli-MiniLM2-L6-H768/config.json

@@ -0,0 +1,36 @@
+{
+  "_name_or_path": "nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "contradiction",
+    "1": "entailment",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "contradiction": 0,
+    "entailment": 1,
+    "neutral": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

models/nli-MiniLM2-L6-H768/special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

models/nli-MiniLM2-L6-H768/tokenizer_config.json

@@ -0,0 +1 @@
+{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "add_prefix_space": false, "errors": "replace", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large"}

models/restricted-dberta-base-zeroshot-v2/added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "[MASK]": 128000
+}

models/restricted-dberta-base-zeroshot-v2/config.json

@@ -0,0 +1,43 @@
+{
+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "entailment",
+    "1": "not_entailment"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "entailment": 0,
+    "not_entailment": 1
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

models/restricted-dberta-base-zeroshot-v2/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/restricted-dberta-base-zeroshot-v2/spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616