Modified validation and training for linktransformer model

.gitattributes

@@ -34,3 +34,5 @@ saved_model/**/* 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
+model.safetensors filter=lfs diff=lfs merge=lfs -text
+.git/lfs/objects/af/c3/afc308c30b585f63df930d326b7d368bd14b8099d8440c3165dc0568c17e891a filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -3,5 +3,7 @@
   "pooling_mode_cls_token": false,
   "pooling_mode_mean_tokens": true,
   "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
 }

LT_training_config.json

@@ -1,12 +1,13 @@
 {
   "model_save_dir": "models",
   "model_save_name": "linkage_un_data_es_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 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 \n  ",
+  "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 coarse product classification - trained on variation brought on by product level correspondance. It was trained for 70 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json \n  ",
   "opt_model_lang": "es",
   "train_batch_size": 64,
-  "num_epochs": 100,
+  "num_epochs": 70,
   "warm_up_perc": 1,
-  "learning_rate": 2e-06,
+  "learning_rate": 2e-05,
+  "loss_type": "supcon",
   "val_perc": 0.2,
   "wandb_names": {
     "project": "linkage",
@@ -16,10 +17,11 @@
   },
   "add_pooling_layer": false,
   "large_val": true,
-  "eval_steps_perc": 0.1,
+  "eval_steps_perc": 0.5,
   "test_at_end": true,
   "save_val_test_pickles": true,
   "val_query_prop": 0.5,
+  "loss_params": {},
   "eval_type": "retrieval",
   "training_dataset": "dataframe",
   "base_model_path": "hiiamsid/sentence_similarity_spanish_es",

README.md

@@ -12,7 +12,7 @@ tags:
 
 # dell-research-harvard/lt-un-data-fine-coarse-es
 
-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. 
+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 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
@@ -23,7 +23,7 @@ This model has been fine-tuned on the model : hiiamsid/sentence_similarity_spani
 
 
 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 
+                                 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 70 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json 
   
 
 ## Usage (LinkTransformer)
@@ -97,7 +97,7 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 68 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 75 with parameters:
 ```
 {'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
@@ -109,17 +109,17 @@ The model was trained with the parameters:
 Parameters of the fit()-Method:
 ```
 {
-    "epochs": 100,
-    "evaluation_steps": 680,
+    "epochs": 70,
+    "evaluation_steps": 38,
     "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
     "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
     "optimizer_params": {
-        "lr": 2e-06
+        "lr": 2e-05
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 6800,
+    "warmup_steps": 5250,
     "weight_decay": 0.01
 }
 ```
@@ -129,10 +129,20 @@ Parameters of the fit()-Method:
 
 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})
+  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
 )
 ```
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+```
+@misc{arora2023linktransformer,
+                  title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
+                  author={Abhishek Arora and Melissa Dell},
+                  year={2023},
+                  eprint={2309.00789},
+                  archivePrefix={arXiv},
+                  primaryClass={cs.CL}
+                }
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "models/linkage_un_data_es_fine_coarse/",
+  "_name_or_path": "models/linkage_un_data_es_fine_coarse",
   "architectures": [
     "BertModel"
   ],
@@ -20,7 +20,7 @@
   "pad_token_id": 1,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.31.0",
+  "transformers_version": "4.35.1",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 31002

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17884b92bb03fce9b0e3de84052aa11df38e25aec5a1e7be3fd563d047fcaa1c
+size 439425888

special_tokens_map.json

@@ -1,7 +1,37 @@
 {
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }

tokenizer_config.json

@@ -1,15 +1,64 @@
 {
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
   "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
   "do_basic_tokenize": true,
   "do_lower_case": false,
   "mask_token": "[MASK]",
+  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
+  "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
   "sep_token": "[SEP]",
+  "stride": 0,
   "strip_accents": false,
   "tokenize_chinese_chars": true,
   "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"
 }