Update README.md

README.md

@@ -1,146 +1,3 @@
----
-pipeline_tag: sentence-similarity
-language: 
-- en
-tags:
-- linktransformer
-- sentence-transformers
-- sentence-similarity
-- tabular-classification
+### Model Overview
 
----
-
-# matthewleechen/lt_namesonly_science
-
-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.
-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/multi-qa-mpnet-base-dot-v1. It is pretrained for the language : - en.
-
-
-test
-
-## Usage (LinkTransformer)
-
-Using this model becomes easy when you have [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
-
-```
-pip install -U linktransformer
-```
-
-Then you can use the model like this:
-
-```python
-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}
-    )
-
-```
-
-
-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.
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 25 with parameters:
-```
-{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`linktransformer.modified_sbert.losses.SupConLoss_wandb` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 200,
-    "evaluation_steps": 3,
-    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-06
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 5000,
-    "weight_decay": 0.01
-}
-```
-
-
-
-
-LinkTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (1): Pooling({'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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## Citing & Authors
-
-```
-@misc{arora2023linktransformer,
-                  title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
-                  author={Abhishek Arora and Melissa Dell},
-                  year={2023},
-                  eprint={2309.00789},
-                  archivePrefix={arXiv},
-                  primaryClass={cs.CL}
-                }
-
-```
+This is a LinkTransformer model trained to capture similarity of names found in scientific publications and biographical databases.