Updated model with better training and evaluation. Test and val data included as pickle files. Older Legacy files were removed to avoid confusion.

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

	---

	# {MODEL_NAME}

	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 : hiiamsid/sentence_similarity_spanish_es. It is pretrained for the language : - es.


	This model was trained on a dataset prepared by linking product classifications from [UN stats](https://unstats.un.org/unsd/classifications/Econ).
	This model is designed to link different products together - trained on variation brought on by product level correspondance. It was trained for 100 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json


	## 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 86 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": 43,
	"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
	"max_grad_norm": 1,
	"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
	"optimizer_params": {
	"lr": 2e-05
	},
	"scheduler": "WarmupLinear",
	"steps_per_epoch": null,
	"warmup_steps": 8600,
	"weight_decay": 0.01
	}
	```




	LinkTransformer(
	(0): Transformer({'max_seq_length': 512, '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
	)
	```

	## 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}
	}

	```