dell-research-harvard/lt-un-data-fine-fine-es

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers 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 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. 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 installed:

pip install -U linktransformer

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.

##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

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You can evaluate the model using the 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": 860,
    "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": 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}) )

## Citing & Authors

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