Commit
•
2de46f4
1
Parent(s):
c8548ba
Updated model with better training and evaluation. Test and val data included as pickle files. Older Legacy files were removed to avoid confusion.
Browse files- .gitattributes +5 -0
- 0/1_Pooling/config.json +7 -0
- 0/README.md +136 -0
- 0/config.json +27 -0
- 0/config_sentence_transformers.json +7 -0
- 0/modules.json +14 -0
- 0/pytorch_model.bin +3 -0
- 0/sentence_bert_config.json +4 -0
- 0/special_tokens_map.json +7 -0
- 0/tokenizer.json +0 -0
- 0/tokenizer_config.json +15 -0
- 0/vocab.txt +0 -0
- 1_Pooling/config.json +0 -0
- 50/1_Pooling/config.json +7 -0
- 50/README.md +136 -0
- 50/config.json +27 -0
- 50/config_sentence_transformers.json +7 -0
- 50/modules.json +14 -0
- 50/pytorch_model.bin +3 -0
- 50/sentence_bert_config.json +4 -0
- 50/special_tokens_map.json +7 -0
- 50/tokenizer.json +0 -0
- 50/tokenizer_config.json +15 -0
- 50/vocab.txt +0 -0
- Information-Retrieval_evaluation_eval_results.csv +23 -0
- Information-Retrieval_evaluation_results.csv +5 -0
- Information-Retrieval_evaluation_test_results.csv +8 -0
- LT_training_config.json +0 -0
- README.md +1 -1
- added_tokens.json +0 -0
- binary_classification_evaluation_eval_results.csv +145 -0
- binary_classification_evaluation_test_results.csv +140 -0
- config.json +1 -1
- config_sentence_transformers.json +0 -0
- entity_vocab.json +0 -0
- eval/Information-Retrieval_evaluation_eval_results.csv +0 -0
- eval/Information-Retrieval_evaluation_results.csv +2 -0
- eval/binary_classification_evaluation_eval_results.csv +0 -0
- modules.json +0 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +0 -0
- sentencepiece.bpe.model +0 -0
- special_tokens_map.json +48 -50
- test_data.pickle +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -2
- val_data.pickle +3 -0
- vocab.txt +0 -0
.gitattributes
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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.git/lfs/objects/ff/ff/ffff8b1e1118917383c8481ca5631e9b5a05616039a6f0039ad5d0fae975d7ed filter=lfs diff=lfs merge=lfs -text
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sentencepiece.bpe.model filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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model.safetensors filter=lfs diff=lfs merge=lfs -text
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50/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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0/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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.git/lfs/objects/ff/ff/ffff8b1e1118917383c8481ca5631e9b5a05616039a6f0039ad5d0fae975d7ed filter=lfs diff=lfs merge=lfs -text
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test_data.pickle filter=lfs diff=lfs merge=lfs -text
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val_data.pickle filter=lfs diff=lfs merge=lfs -text
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sentencepiece.bpe.model filter=lfs diff=lfs merge=lfs -text
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0/1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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0/README.md
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---
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pipeline_tag: sentence-similarity
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language:
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- es
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tags:
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- linktransformer
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- sentence-transformers
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- sentence-similarity
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- tabular-classification
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---
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# {MODEL_NAME}
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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.
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It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
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Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well.
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It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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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.
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This model has been fine-tuned on the model : hiiamsid/sentence_similarity_spanish_es. It is pretrained for the language : - es.
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check
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## Usage (LinkTransformer)
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Using this model becomes easy when you have [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
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```
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pip install -U linktransformer
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```
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Then you can use the model like this:
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```python
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import linktransformer as lt
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import pandas as pd
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##Load the two dataframes that you want to link. For example, 2 dataframes with company names that are written differently
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df1=pd.read_csv("data/df1.csv") ###This is the left dataframe with key CompanyName for instance
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df2=pd.read_csv("data/df2.csv") ###This is the right dataframe with key CompanyName for instance
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###Merge the two dataframes on the key column!
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df_merged = lt.merge(df1, df2, on="CompanyName", how="inner")
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##Done! The merged dataframe has a column called "score" that contains the similarity score between the two company names
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```
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## Training your own LinkTransformer model
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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
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The model was trained using SupCon loss.
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Usage can be found in the package docs.
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The training config can be found in the repo with the name LT_training_config.json
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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.
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Here is an example.
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```python
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##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.
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saved_model_path = train_model(
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model_path="hiiamsid/sentence_similarity_spanish_es",
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dataset_path=dataset_path,
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left_col_names=["description47"],
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right_col_names=['description48'],
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left_id_name=['tariffcode47'],
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right_id_name=['tariffcode48'],
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log_wandb=False,
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config_path=LINKAGE_CONFIG_PATH,
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training_args={"num_epochs": 1}
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)
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```
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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.
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Read our paper and the documentation for more!
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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You can evaluate the model using the [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) package's inference functions.
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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.
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 50 with parameters:
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```
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{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`linktransformer.modified_sbert.losses.SupConLoss_wandb`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 0,
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"evaluation_steps": 500,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"lr": 2e-06
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 0,
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"weight_decay": 0.01
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}
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```
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LinkTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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0/config.json
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{
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"_name_or_path": "/home/abhishekarora/.cache/torch/sentence_transformers/hiiamsid_sentence_similarity_spanish_es/",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.31.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31002
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}
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0/config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.10.2",
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"pytorch": "1.9.0+cu102"
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}
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}
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0/modules.json
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[
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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}
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]
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0/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:27cedbcf35b15b343168c274181ed80f78982b1dbc33a354dae7556731ffabc9
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size 439470249
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0/sentence_bert_config.json
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{
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"max_seq_length": 512,
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"do_lower_case": false
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}
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{
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"mask_token": "[MASK]",
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}
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0/tokenizer.json
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0/tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"strip_accents": false,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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0/vocab.txt
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1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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50/README.md
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|
1 |
+
---
|
2 |
+
pipeline_tag: sentence-similarity
|
3 |
+
language:
|
4 |
+
- es
|
5 |
+
tags:
|
6 |
+
- linktransformer
|
7 |
+
- sentence-transformers
|
8 |
+
- sentence-similarity
|
9 |
+
- tabular-classification
|
10 |
+
|
11 |
+
---
|
12 |
+
|
13 |
+
# {MODEL_NAME}
|
14 |
+
|
15 |
+
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.
|
16 |
+
It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
|
17 |
+
Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well.
|
18 |
+
It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
|
19 |
+
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.
|
20 |
+
|
21 |
+
|
22 |
+
This model has been fine-tuned on the model : hiiamsid/sentence_similarity_spanish_es. It is pretrained for the language : - es.
|
23 |
+
|
24 |
+
|
25 |
+
check
|
26 |
+
|
27 |
+
## Usage (LinkTransformer)
|
28 |
+
|
29 |
+
Using this model becomes easy when you have [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
|
30 |
+
|
31 |
+
```
|
32 |
+
pip install -U linktransformer
|
33 |
+
```
|
34 |
+
|
35 |
+
Then you can use the model like this:
|
36 |
+
|
37 |
+
```python
|
38 |
+
import linktransformer as lt
|
39 |
+
import pandas as pd
|
40 |
+
|
41 |
+
##Load the two dataframes that you want to link. For example, 2 dataframes with company names that are written differently
|
42 |
+
df1=pd.read_csv("data/df1.csv") ###This is the left dataframe with key CompanyName for instance
|
43 |
+
df2=pd.read_csv("data/df2.csv") ###This is the right dataframe with key CompanyName for instance
|
44 |
+
|
45 |
+
###Merge the two dataframes on the key column!
|
46 |
+
df_merged = lt.merge(df1, df2, on="CompanyName", how="inner")
|
47 |
+
|
48 |
+
##Done! The merged dataframe has a column called "score" that contains the similarity score between the two company names
|
49 |
+
|
50 |
+
```
|
51 |
+
|
52 |
+
|
53 |
+
## Training your own LinkTransformer model
|
54 |
+
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
|
55 |
+
The model was trained using SupCon loss.
|
56 |
+
Usage can be found in the package docs.
|
57 |
+
The training config can be found in the repo with the name LT_training_config.json
|
58 |
+
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.
|
59 |
+
Here is an example.
|
60 |
+
|
61 |
+
|
62 |
+
```python
|
63 |
+
|
64 |
+
##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.
|
65 |
+
saved_model_path = train_model(
|
66 |
+
model_path="hiiamsid/sentence_similarity_spanish_es",
|
67 |
+
dataset_path=dataset_path,
|
68 |
+
left_col_names=["description47"],
|
69 |
+
right_col_names=['description48'],
|
70 |
+
left_id_name=['tariffcode47'],
|
71 |
+
right_id_name=['tariffcode48'],
|
72 |
+
log_wandb=False,
|
73 |
+
config_path=LINKAGE_CONFIG_PATH,
|
74 |
+
training_args={"num_epochs": 1}
|
75 |
+
)
|
76 |
+
|
77 |
+
```
|
78 |
+
|
79 |
+
|
80 |
+
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.
|
81 |
+
Read our paper and the documentation for more!
|
82 |
+
|
83 |
+
|
84 |
+
|
85 |
+
## Evaluation Results
|
86 |
+
|
87 |
+
<!--- Describe how your model was evaluated -->
|
88 |
+
|
89 |
+
You can evaluate the model using the [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) package's inference functions.
|
90 |
+
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.
|
91 |
+
|
92 |
+
|
93 |
+
## Training
|
94 |
+
The model was trained with the parameters:
|
95 |
+
|
96 |
+
**DataLoader**:
|
97 |
+
|
98 |
+
`torch.utils.data.dataloader.DataLoader` of length 50 with parameters:
|
99 |
+
```
|
100 |
+
{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
101 |
+
```
|
102 |
+
|
103 |
+
**Loss**:
|
104 |
+
|
105 |
+
`linktransformer.modified_sbert.losses.SupConLoss_wandb`
|
106 |
+
|
107 |
+
Parameters of the fit()-Method:
|
108 |
+
```
|
109 |
+
{
|
110 |
+
"epochs": 1,
|
111 |
+
"evaluation_steps": 500,
|
112 |
+
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
|
113 |
+
"max_grad_norm": 1,
|
114 |
+
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
|
115 |
+
"optimizer_params": {
|
116 |
+
"lr": 2e-06
|
117 |
+
},
|
118 |
+
"scheduler": "WarmupLinear",
|
119 |
+
"steps_per_epoch": null,
|
120 |
+
"warmup_steps": 50,
|
121 |
+
"weight_decay": 0.01
|
122 |
+
}
|
123 |
+
```
|
124 |
+
|
125 |
+
|
126 |
+
|
127 |
+
|
128 |
+
LinkTransformer(
|
129 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
130 |
+
(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})
|
131 |
+
)
|
132 |
+
```
|
133 |
+
|
134 |
+
## Citing & Authors
|
135 |
+
|
136 |
+
<!--- Describe where people can find more information -->
|
50/config.json
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/home/abhishekarora/.cache/torch/sentence_transformers/hiiamsid_sentence_similarity_spanish_es/",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
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|
7 |
+
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|
8 |
+
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|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 768,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 3072,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 12,
|
18 |
+
"num_hidden_layers": 12,
|
19 |
+
"output_past": true,
|
20 |
+
"pad_token_id": 1,
|
21 |
+
"position_embedding_type": "absolute",
|
22 |
+
"torch_dtype": "float32",
|
23 |
+
"transformers_version": "4.31.0",
|
24 |
+
"type_vocab_size": 2,
|
25 |
+
"use_cache": true,
|
26 |
+
"vocab_size": 31002
|
27 |
+
}
|
50/config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.0.0",
|
4 |
+
"transformers": "4.10.2",
|
5 |
+
"pytorch": "1.9.0+cu102"
|
6 |
+
}
|
7 |
+
}
|
50/modules.json
ADDED
@@ -0,0 +1,14 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
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"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
50/pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
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oid sha256:a4062466499f9e7dad14314f6b0ffd875bc7697f46beeb52fc5b2f1b9367ac1f
|
3 |
+
size 439470249
|
50/sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
50/special_tokens_map.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": "[CLS]",
|
3 |
+
"mask_token": "[MASK]",
|
4 |
+
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|
5 |
+
"sep_token": "[SEP]",
|
6 |
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"unk_token": "[UNK]"
|
7 |
+
}
|
50/tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
50/tokenizer_config.json
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"clean_up_tokenization_spaces": true,
|
3 |
+
"cls_token": "[CLS]",
|
4 |
+
"do_basic_tokenize": true,
|
5 |
+
"do_lower_case": false,
|
6 |
+
"mask_token": "[MASK]",
|
7 |
+
"model_max_length": 512,
|
8 |
+
"never_split": null,
|
9 |
+
"pad_token": "[PAD]",
|
10 |
+
"sep_token": "[SEP]",
|
11 |
+
"strip_accents": false,
|
12 |
+
"tokenize_chinese_chars": true,
|
13 |
+
"tokenizer_class": "BertTokenizer",
|
14 |
+
"unk_token": "[UNK]"
|
15 |
+
}
|
50/vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
Information-Retrieval_evaluation_eval_results.csv
ADDED
@@ -0,0 +1,23 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
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Information-Retrieval_evaluation_results.csv
ADDED
@@ -0,0 +1,5 @@
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|
1 |
+
epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100
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Information-Retrieval_evaluation_test_results.csv
ADDED
@@ -0,0 +1,8 @@
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|
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1 |
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epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100
|
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|
LT_training_config.json
CHANGED
File without changes
|
README.md
CHANGED
@@ -10,7 +10,7 @@ tags:
|
|
10 |
|
11 |
---
|
12 |
|
13 |
-
#
|
14 |
|
15 |
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.
|
16 |
It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
|
|
|
10 |
|
11 |
---
|
12 |
|
13 |
+
# {MODEL_NAME}
|
14 |
|
15 |
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.
|
16 |
It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more.
|
added_tokens.json
CHANGED
File without changes
|
binary_classification_evaluation_eval_results.csv
ADDED
@@ -0,0 +1,145 @@
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|
1 |
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|
binary_classification_evaluation_test_results.csv
ADDED
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1 |
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