model/README.md

---
pipeline_tag: sentence-similarity
language: 
- en
tags:
- linktransformer
- sentence-transformers
- sentence-similarity
- tabular-classification

---

# {MODEL_NAME}

This is a [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) 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 : dell-research-harvard/lt-wikidata-comp-zh. 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 98 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": 100,
    "evaluation_steps": 980,
    "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": 9800,
    "weight_decay": 0.01
}
```




LinkTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->