Update README.md

README.md

@@ -12,17 +12,10 @@ tags:
 
 # gbpatentdata/lt-patent-inventor-linking
 
-This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model- it just wraps around the class. 
-It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
-Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. 
-It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model - it just wraps around the class. 
 Take a look at the documentation of [sentence-transformers](https://www.sbert.net/index.html) if you want to use this model for more than what we support in our applications. 
 
-
-This model has been fine-tuned on the model : sentence-transformers/all-mpnet-base-v2. It is pretrained for the language : - en.
-
-
-test
+This model has been fine-tuned on the model: `sentence-transformers/all-mpnet-base-v2`. It is pretrained for the language: `en`.
 
 ## Usage (LinkTransformer)
 
@@ -38,56 +31,18 @@ Then you can use the model like this:
 import linktransformer as lt
 import pandas as pd
 
-##Load the two dataframes that you want to link. For example, 2 dataframes with company names that are written differently
-df1=pd.read_csv("data/df1.csv") ###This is the left dataframe with key CompanyName for instance
-df2=pd.read_csv("data/df2.csv") ###This is the right dataframe with key CompanyName for instance
-
-###Merge the two dataframes on the key column!
-df_merged = lt.merge(df1, df2, on="CompanyName", how="inner")
-
-##Done! The merged dataframe has a column called "score" that contains the similarity score between the two company names
-
-```
-
-
-## Training your own LinkTransformer model
-Any Sentence Transformers can be used as a backbone by simply adding a pooling layer.  Any other transformer on HuggingFace can also be used by specifying the option add_pooling_layer==True
-The model was trained using SupCon loss. 
-Usage can be found in the package docs. 
-The training config can be found in the repo with the name LT_training_config.json
-To replicate the training, you can download the file and specify the path in the config_path argument of the training function. You can also override the config by specifying the training_args argument.
-Here is an example. 
-
-
-```python
-
-##Consider the example in the paper that has a dataset of Mexican products and their tariff codes from 1947 and 1948 and we want train a model to link the two tariff codes.
-saved_model_path = train_model(
-        model_path="hiiamsid/sentence_similarity_spanish_es",
-        dataset_path=dataset_path,
-        left_col_names=["description47"],
-        right_col_names=['description48'],
-        left_id_name=['tariffcode47'],
-        right_id_name=['tariffcode48'],
-        log_wandb=False,
-        config_path=LINKAGE_CONFIG_PATH,
-        training_args={"num_epochs": 1}
-    )
+df_lm_matched = lt.cluster_rows(df,
+                                model='gbpatentdata/lt-patent-inventor-linking',
+                                on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'],
+                                cluster_type='SLINK',
+                                cluster_params={'threshold': 0.1, 'min cluster size': 1, 'metric': 'cosine'}
+)
 
 ```
 
+## Evaluation
 
-You can also use this package for deduplication (clusters a df on the supplied key column). Merging a fine class (like product) to a coarse class (like HS code) is also possible.
-Read our paper and the documentation for more!
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-You can evaluate the model using the [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) package's inference functions.
-We have provided a few datasets in the package for you to try out. We plan to host more datasets on Huggingface and our website (Coming soon) that you can take a look at.
+We evaluate using the standard [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) information retrieval metrics. Our test set evaluations are available [here](https://huggingface.co/gbpatentdata/lt-patent-inventor-linking/blob/main/Information-Retrieval_evaluation_test_results.csv).
 
 
 ## Training
@@ -121,10 +76,7 @@ Parameters of the fit()-Method:
     "weight_decay": 0.01
 }
 ```
-
-
-
-
+```
 LinkTransformer(
   (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
@@ -132,7 +84,21 @@ LinkTransformer(
 )
 ```
 
-## Citing & Authors
+## Citation 
+
+If you use our model or custom training/evaluation data in your research, please cite our accompanying paper as follows:
+
+```
+@article{bct2025,
+  title = {300 Years of British Patents},
+  author = {Enrico Berkes and Matthew Lee Chen and Matteo Tranchero},
+  journal = {arXiv preprint arXiv:2401.12345},
+  year = {2025},
+  url = {https://arxiv.org/abs/2401.12345}
+}
+```
+
+Please also cite the original LinkTransformer authors:
 
 ```
 @misc{arora2023linktransformer,