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.gitattributes

@@ -33,3 +33,4 @@ 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
+model.safetensors filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,9 @@
+{
+  "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
+}

Information-Retrieval_evaluation_eval_results.csv

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Information-Retrieval_evaluation_test_results.csv

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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

@@ -0,0 +1,30 @@
+{
+  "model_save_dir": "models",
+  "model_save_name": "check5",
+  "opt_model_description": "test",
+  "opt_model_lang": "en",
+  "train_batch_size": 64,
+  "num_epochs": 100,
+  "warm_up_perc": 1,
+  "learning_rate": 2e-05,
+  "loss_type": "supcon",
+  "val_perc": 0.2,
+  "wandb_names": {
+    "project": "linktransformer",
+    "id": "your-id",
+    "run": "run-name",
+    "entity": "your-id"
+  },
+  "add_pooling_layer": false,
+  "large_val": true,
+  "eval_steps_perc": 0.5,
+  "test_at_end": true,
+  "save_val_test_pickles": true,
+  "val_query_prop": 0.5,
+  "loss_params": {},
+  "batch_size": 128,
+  "eval_type": "retrieval",
+  "training_dataset": "../../../labelled_data.csv",
+  "base_model_path": "sentence-transformers/all-mpnet-base-v2",
+  "best_model_path": "models/check5"
+}

README.md

@@ -0,0 +1,121 @@
+---
+pipeline_tag: sentence-similarity
+language: 
+- en
+tags:
+- linktransformer
+- sentence-transformers
+- sentence-similarity
+- tabular-classification
+
+---
+
+# gbpatentdata/lt-patent-inventor-linking
+
+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. 
+This model has been fine-tuned on the model: `sentence-transformers/all-mpnet-base-v2`. It is pretrained for the language: `en`.
+
+## Usage (Sentence-Transformers)
+
+To use this model using sentence-transformers:
+
+```python 
+from sentence_transformers import SentenceTransformer
+
+# load 
+model = SentenceTransformer("matthewleechen/lt-patent-inventor-linking")
+```
+
+## Usage (LinkTransformer)
+
+To use this model for clustering with [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
+
+```python
+import linktransformer as lt
+import pandas as pd
+
+df_lm_matched = lt.cluster_rows(df, # df should be a dataset of unique patent-inventors 
+                                model='matthewleechen/lt-patent-inventor-linking',
+                                on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'], # cluster on these variables 
+                                cluster_type='SLINK', # use SLINK algorithm 
+                                cluster_params={ # default params 
+                                    'threshold': 0.1,
+                                    'min cluster size': 1,
+                                    'metric': 'cosine'
+                                }
+    )
+)
+
+```
+
+## Evaluation
+
+We evaluate using the standard [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) information retrieval metrics. Our test set evaluations are available [here](https://huggingface.co/gbpatentdata/lt-patent-inventor-linking/blob/main/Information-Retrieval_evaluation_test_results.csv).
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 31 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": 16,
+    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 3100,
+    "weight_decay": 0.01
+}
+```
+```
+LinkTransformer(
+  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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})
+  (2): Normalize()
+)
+```
+
+## Citation 
+
+If you use our model or custom training/evaluation data in your research, please cite our accompanying paper as follows:
+
+```
+@article{bct2025,
+  title = {300 Years of British Patents},
+  author = {Enrico Berkes and Matthew Lee Chen and Matteo Tranchero},
+  journal = {arXiv preprint arXiv:2401.12345},
+  year = {2025},
+  url = {https://arxiv.org/abs/2401.12345}
+}
+```
+
+Please also cite the original LinkTransformer authors:
+
+```
+@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

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "models/check5",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "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": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5786a63dc05544771f8690ae44d4cfcd2828ce95030f75992891ce51bf98d6bc
+size 437967672

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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

@@ -0,0 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "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
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}