Upload 4 files

README.md

@@ -1,3 +1,118 @@
 ---
 license: apache-2.0
 ---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+industry-bert-sec-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.   
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+BERT-based 768-parameter drop-in substitute for non-industry-specific embeddings model.   This model was trained on a wide range of 
+publicly available U.S. Securities and Exchange Commission (SEC) regulatory filings and related documents.
+
+- **Developed by:** llmware
+- **Shared by [optional]:** Darren Oberst
+- **Model type:** BERT-based Industry domain fine-tuned Sentence Transformer architecture
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** BERT-based model, fine-tuning methodology described below.
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+This model is intended to be used as a sentence embedding model, specifically for financial services and use cases involving regulatory and financial filing documents.
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+This model was fine-tuned using a custom self-supervised procedure that combined contrastive techniques with stochastic injections of 
+distortions in the samples.  The methodology was derived, adapted and inspired primarily from three research papers cited below:
+TSDAE (Reimers), DeClutr (Giorgi), and Contrastive Tension (Carlsson).
+
+
+## Citation [optional]
+
+Custom training protocol used to train the model, which was derived and inspired by the following papers:
+
+@article{wang-2021-TSDAE,
+    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
+    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna", 
+    journal= "arXiv preprint arXiv:2104.06979",
+    month = "4",
+    year = "2021",
+    url = "https://arxiv.org/abs/2104.06979",
+}
+
+@inproceedings{giorgi-etal-2021-declutr,
+    title        = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations},
+    author       = {Giorgi, John  and Nitski, Osvald  and Wang, Bo  and Bader, Gary},
+    year         = 2021,
+    month        = aug,
+    booktitle    = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
+    publisher    = {Association for Computational Linguistics},
+    address      = {Online},
+    pages        = {879--895},
+    doi          = {10.18653/v1/2021.acl-long.72},
+    url          = {https://aclanthology.org/2021.acl-long.72}
+}
+
+@article{Carlsson-2021-CT,
+      title =  {Semantic Re-tuning with Contrastive Tension},
+      author=  {Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, Magnus Sahlgren},
+        year=  {2021},
+        month= {"January"}
+    Published: 12 Jan 2021, Last Modified: 05 May 2023
+}
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

@@ -0,0 +1,27 @@
+{
+  "model_name": "industry-bert-sec-v0.1",
+  "description": "Sentence transformer embedding model finetuned on wide range of public SEC filing and regulatory documents",
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9e8f6a4cf3ec548c258b2718ba53be1f1e396749ea5b0db2402303c0d78edf1
+size 438000173