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

.gitattributes

@@ -1,40 +1,6 @@
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+val_data.pickle filter=lfs diff=lfs merge=lfs -text
+linkage_en_aliases_train.csv filter=lfs diff=lfs merge=lfs -text
 model.safetensors filter=lfs diff=lfs merge=lfs -text
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+test_data.pickle filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -1,5 +1,5 @@
 {
-  "word_embedding_dimension": 1024,
+  "word_embedding_dimension": 768,
   "pooling_mode_cls_token": true,
   "pooling_mode_mean_tokens": false,
   "pooling_mode_max_tokens": false,

Information-Retrieval_evaluation_eval_results.csv

@@ -0,0 +1,2 @@
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Information-Retrieval_evaluation_test_results.csv

@@ -0,0 +1,2 @@
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LT_training_config.json

@@ -1,10 +1,10 @@
 {
   "model_save_dir": "models",
-  "model_save_name": "linkage_en_aliases_large",
-  "opt_model_description": "This model was trained on a dataset consisting of company aliases from wiki data using the LinkTransformer framework. \n                               It was trained for 30 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json \n  ",
+  "model_save_name": "linkage_en_aliases",
+  "opt_model_description": "This model was trained on a dataset consisting of company aliases from wiki data using the LinkTransformer framework. \n                               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",
-  "train_batch_size": 256,
-  "num_epochs": 30,
+  "train_batch_size": 64,
+  "num_epochs": 100,
   "warm_up_perc": 1,
   "learning_rate": 2e-05,
   "loss_type": "supcon",
@@ -12,7 +12,7 @@
   "wandb_names": {
     "project": "linkage",
     "id": "econabhishek",
-    "run": "linkage_en_aliases_large",
+    "run": "linkage_en_aliases",
     "entity": "econabhishek"
   },
   "add_pooling_layer": false,
@@ -24,6 +24,6 @@
   "loss_params": {},
   "eval_type": "retrieval",
   "training_dataset": "dataframe",
-  "base_model_path": "BAAI/bge-large-en-v1.5",
-  "best_model_path": "models/linkage_en_aliases_large"
+  "base_model_path": "multi-qa-mpnet-base-dot-v1",
+  "best_model_path": "models/linkage_en_aliases"
 }

README.md

@@ -10,20 +10,20 @@ tags:
 
 ---
 
-# dell-research-harvard/lt-wikidata-comp-en
+# {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 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+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 : BAAI/bge-large-en-v1.5. It is pretrained for the language : - en.
+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 consisting of company aliases from wiki data using the LinkTransformer framework. 
-                               It was trained for 30 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json 
+                               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)
@@ -97,9 +97,9 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 522 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 2087 with parameters:
 ```
-{'batch_size': 256, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
 
 **Loss**:
@@ -109,8 +109,8 @@ The model was trained with the parameters:
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 30,
-    "evaluation_steps": 261,
+    "epochs": 100,
+    "evaluation_steps": 1044,
     "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
@@ -119,7 +119,7 @@ Parameters of the fit()-Method:
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 15660,
+    "warmup_steps": 208700,
     "weight_decay": 0.01
 }
 ```
@@ -128,9 +128,8 @@ Parameters of the fit()-Method:
 
 
 LinkTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
-  (2): Normalize()
+  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
 )
 ```
 

config.json

@@ -1,32 +1,24 @@
 {
-  "_name_or_path": "models/linkage_en_aliases_large",
+  "_name_or_path": "sentence-transformers/multi-qa-mpnet-base-dot-v1",
   "architectures": [
-    "BertModel"
+    "MPNetModel"
   ],
   "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "id2label": {
-    "0": "LABEL_0"
-  },
+  "hidden_size": 768,
   "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "label2id": {
-    "LABEL_0": 0
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 24,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
   "torch_dtype": "float32",
   "transformers_version": "4.35.1",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
+  "vocab_size": 30527
 }

config_sentence_transformers.json

@@ -1,7 +1,7 @@
 {
   "__version__": {
-    "sentence_transformers": "2.2.2",
-    "transformers": "4.28.1",
-    "pytorch": "1.13.0+cu117"
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
   }
 }

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pytorch_model.bin → linkage_en_aliases_train.csv

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model.safetensors

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modules.json

@@ -10,11 +10,5 @@
     "name": "1",
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 ]

sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
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-  "do_lower_case": true
+  "do_lower_case": false
 }

special_tokens_map.json

@@ -1,29 +1,43 @@
 {
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test_data.pickle

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tokenizer_config.json

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 {
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val_data.pickle

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vocab.txt

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 [PAD]
 [unused0]
 [unused1]
@@ -30520,3 +30524,4 @@ necessitated
 ##：
 ##？
 ##～
+<mask>