--- pipeline_tag: sentence-similarity language: - en tags: - linktransformer - sentence-transformers - sentence-similarity - tabular-classification --- # dell-research-harvard/lt-un-data-fine-coarse-en 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 : multi-qa-mpnet-base-dot-v1. It is pretrained for the language : - en. 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 to their coarse product classification - 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 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 126 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": 1260, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "lr": 2e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 12600, "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}) ) ``` ## Citing & Authors