Initial Commit

README.md

@@ -1,38 +1,38 @@
----
-language: 
-  - en
-tags:
-- masked-lm
-- pytorch
-pipeline-tag: 
-  - "fill-mask"
-mask-token: 
-  - "[MASK]"
-widget:
-- text: "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."
-- text: "The present invention relates to [MASK] accessories and pertains particularly to a brake light unit for bicycles."
-- text: "The present invention discloses a space-bound-free [MASK] and its coordinate determining circuit for determining a coordinate of a stylus pen."
-- text: "The illuminated [MASK] includes a substantially translucent canopy supported by a plurality of ribs pivotally swingable towards and away from a shaft."
-license: apache-2.0
-metrics:
-- perplexity
-
----
-
-# Motivation
-
-
-This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite a lot of memory/storage. So have reduced the embedding dimension to 64 i.e 1/16th of 1024 using Principle Component Analysis (PCA) and it still gives a comparable performance. Yes! PCA gives better performance than NMF. Note: This process neither improves the runtime, nor the memory requirement for running the model. It only reduces the needed space to store embeddings, for example, for semantic search using vector databases.
-
-
-
-# BERT for Patents
-
-BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). 
-
-If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper](https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf) and [GitHub page](https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md) containing the original TensorFlow checkpoint.
-
----
-
-### Projects using this model (or variants of it):
-- [Patents4IPPC](https://github.com/ec-jrc/Patents4IPPC) (carried out by [Pi School](https://picampus-school.com/) and commissioned by the [Joint Research Centre (JRC)](https://ec.europa.eu/jrc/en) of the European Commission)
+---
+language: 
+  - en
+tags:
+- masked-lm
+- pytorch
+pipeline-tag: 
+  - "fill-mask"
+mask-token: 
+  - "[MASK]"
+widget:
+- text: "The present [MASK] provides a torque sensor that is small and highly rigid and for which high production efficiency is possible."
+- text: "The present invention relates to [MASK] accessories and pertains particularly to a brake light unit for bicycles."
+- text: "The present invention discloses a space-bound-free [MASK] and its coordinate determining circuit for determining a coordinate of a stylus pen."
+- text: "The illuminated [MASK] includes a substantially translucent canopy supported by a plurality of ribs pivotally swingable towards and away from a shaft."
+license: apache-2.0
+metrics:
+- perplexity
+
+---
+
+# Motivation
+
+
+This model is based on anferico/bert-for-patents - a BERT<sub>LARGE</sub> model (See details below). By default, the pre-trained model's output embeddings with size 768 (base-models) or with size 1024 (large-models). However, when you store Millions of embeddings, this can require quite a lot of memory/storage. So have reduced the embedding dimension to 64 i.e 1/16th of 1024 using Principle Component Analysis (PCA) and it still gives a comparable performance. Yes! PCA gives better performance than NMF. Note: This process neither improves the runtime, nor the memory requirement for running the model. It only reduces the needed space to store embeddings, for example, for semantic search using vector databases.
+
+
+
+# BERT for Patents
+
+BERT for Patents is a model trained by Google on 100M+ patents (not just US patents). 
+
+If you want to learn more about the model, check out the [blog post](https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis), [white paper](https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf) and [GitHub page](https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md) containing the original TensorFlow checkpoint.
+
+---
+
+### Projects using this model (or variants of it):
+- [Patents4IPPC](https://github.com/ec-jrc/Patents4IPPC) (carried out by [Pi School](https://picampus-school.com/) and commissioned by the [Joint Research Centre (JRC)](https://ec.europa.eu/jrc/en) of the European Commission)

bert-for-patents-64d/README.md

@@ -1,55 +0,0 @@
----
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
----
-
-# {MODEL_NAME}
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 64 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
-
-## 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
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-  (dense): Dense({'in_features': 1024, 'out_features': 64, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->