96abhishekarora commited on
Commit
50657a8
1 Parent(s): 5e817f8

Modified validation and training for linktransformer model

Browse files
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LT_training_config.json CHANGED
@@ -1,12 +1,13 @@
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  {
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  "model_save_dir": "models",
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  "model_save_name": "linkage_un_data_fr_fine_fine",
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- "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 together - 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 ",
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@@ -16,10 +17,11 @@
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+ "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 together - trained on variation brought on by product level correspondance. It was trained for 50 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json \n ",
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README.md CHANGED
@@ -12,7 +12,7 @@ tags:
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  # dell-research-harvard/lt-un-data-fine-fine-fr
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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.
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 1024 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 : dangvantuan/sentence-camembert-lar
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24
 
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  This model was trained on a dataset prepared by linking product classifications from [UN stats](https://unstats.un.org/unsd/classifications/Econ).
26
- This model is designed to link different products together - 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
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  ## Usage (LinkTransformer)
@@ -109,17 +109,17 @@ The model was trained with the parameters:
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  Parameters of the fit()-Method:
110
  ```
111
  {
112
- "epochs": 100,
113
- "evaluation_steps": 660,
114
  "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
115
  "max_grad_norm": 1,
116
  "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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119
  },
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  "scheduler": "WarmupLinear",
121
  "steps_per_epoch": null,
122
- "warmup_steps": 6600,
123
  "weight_decay": 0.01
124
  }
125
  ```
@@ -129,10 +129,20 @@ Parameters of the fit()-Method:
129
 
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  LinkTransformer(
131
  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: CamembertModel
132
- (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
133
  )
134
  ```
135
 
136
  ## Citing & Authors
137
 
138
- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
12
 
13
  # dell-research-harvard/lt-un-data-fine-fine-fr
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.
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 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
23
 
24
 
25
  This model was trained on a dataset prepared by linking product classifications from [UN stats](https://unstats.un.org/unsd/classifications/Econ).
26
+ This model is designed to link different products together - trained on variation brought on by product level correspondance. It was trained for 50 epochs using other defaults that can be found in the repo's LinkTransformer config file - LT_training_config.json
27
 
28
 
29
  ## Usage (LinkTransformer)
 
109
  Parameters of the fit()-Method:
110
  ```
111
  {
112
+ "epochs": 50,
113
+ "evaluation_steps": 33,
114
  "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
115
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116
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117
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118
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120
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121
  "steps_per_epoch": null,
122
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123
  "weight_decay": 0.01
124
  }
125
  ```
 
129
 
130
  LinkTransformer(
131
  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: CamembertModel
132
+ (1): Pooling({'word_embedding_dimension': 1024, '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})
133
  )
134
  ```
135
 
136
  ## Citing & Authors
137
 
138
+ ```
139
+ @misc{arora2023linktransformer,
140
+ title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
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+ author={Abhishek Arora and Melissa Dell},
142
+ year={2023},
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+ eprint={2309.00789},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
146
+ }
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+
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+ ```
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