Modified validation and training for linktransformer model

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

LT_training_config.json

@@ -0,0 +1,31 @@
+{
+  "model_save_dir": "models",
+  "model_save_name": "linkage_un_data_multi_fine_coarse",
+  "opt_model_description": "This model was trained on a dataset prepared by linking product classifications from [UN stats](https://unstats.un.org/unsd/classifications/Econ). \n                                 This model is designed to link different products to their industrial classification (ISIC) - 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 \n  ",
+  "opt_model_lang": [
+    "en",
+    "fr",
+    "es"
+  ],
+  "train_batch_size": 64,
+  "num_epochs": 100,
+  "warm_up_perc": 1,
+  "learning_rate": 2e-06,
+  "val_perc": 0.2,
+  "wandb_names": {
+    "project": "linkage",
+    "id": "econabhishek",
+    "run": "linkage_un_data_multi_fine_coarse",
+    "entity": "econabhishek"
+  },
+  "add_pooling_layer": false,
+  "large_val": true,
+  "eval_steps_perc": 0.1,
+  "test_at_end": true,
+  "save_val_test_pickles": true,
+  "val_query_prop": 0.5,
+  "eval_type": "retrieval",
+  "training_dataset": "dataframe",
+  "base_model_path": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+  "best_model_path": "models/linkage_un_data_multi_fine_coarse"
+}

README.md

@@ -0,0 +1,142 @@
+---
+pipeline_tag: sentence-similarity
+language: 
+- en
+- fr
+- es
+tags:
+- linktransformer
+- sentence-transformers
+- sentence-similarity
+- tabular-classification
+
+---
+
+# dell-research-harvard/lt-un-data-fine-coarse-multi
+
+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 : sentence-transformers/paraphrase-multilingual-mpnet-base-v2. It is pretrained for the language : - en
+- fr
+- 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 to their industrial classification (ISIC) - 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 51 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": 510,
+    "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": 5100,
+    "weight_decay": 0.01
+}
+```
+
+
+
+
+LinkTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (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 -->

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "models/linkage_un_data_multi_fine_coarse/",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

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sentence_bert_config.json

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+  "do_lower_case": false
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special_tokens_map.json

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tokenizer_config.json

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