model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bert-base-tweetner7-2020-2021-concat
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: tner/tweetner7/test_2021
+      type: tner/tweetner7/test_2021
+      args: tner/tweetner7/test_2021
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.6230258640421148
+    - name: Precision
+      type: precision
+      value: 0.6166742183960127
+    - name: Recall
+      type: recall
+      value: 0.6295097132284921
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5758556427048315
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5715554663683273
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5821234872899773
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7661839619941617
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7584995466908432
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7740256736440384
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: tner/tweetner7/test_2020
+      type: tner/tweetner7/test_2020
+      args: tner/tweetner7/test_2020
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.6210070384407147
+    - name: Precision
+      type: precision
+      value: 0.6491228070175439
+    - name: Recall
+      type: recall
+      value: 0.5952257394914374
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.577436139660066
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6119340101835135
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.549500601374034
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7298321602598808
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7628749292586304
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.6995329527763363
+
+pipeline_tag: token-classification
+widget:
+- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
+  example_title: "NER Example 1"
+---
+# tner/bert-base-tweetner7-2020-2021-concat
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
+Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
+for more detail). It achieves the following results on the test set of 2021:
+- F1 (micro): 0.6230258640421148
+- Precision (micro): 0.6166742183960127
+- Recall (micro): 0.6295097132284921
+- F1 (macro): 0.5758556427048315
+- Precision (macro): 0.5715554663683273
+- Recall (macro): 0.5821234872899773
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.5141176470588235
+- creative_work: 0.3886075949367089
+- event: 0.4580617122990004
+- group: 0.5660613650594865
+- location: 0.6264564770390679
+- person: 0.8196536144578314
+- product: 0.6580310880829014 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6139925448708724, 0.632549139769655]
+    - 95%: [0.612303125388328, 0.6336744975616968] 
+- F1 (macro): 
+    - 90%: [0.6139925448708724, 0.632549139769655]
+    - 95%: [0.612303125388328, 0.6336744975616968] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/raw/main/eval/metric_span.json).
+
+### Usage
+This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip   
+```shell
+pip install tner
+```
+and activate model as below.
+```python
+from tner import TransformersNER
+model = TransformersNER("tner/bert-base-tweetner7-2020-2021-concat")
+model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
+```
+It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/tweetner7']
+ - dataset_split: train_all
+ - dataset_name: None
+ - local_dataset: None
+ - model: bert-base-cased
+ - crf: True
+ - max_length: 128
+ - epoch: 30
+ - batch_size: 32
+ - lr: 0.0001
+ - random_seed: 0
+ - gradient_accumulation_steps: 1
+ - weight_decay: 1e-07
+ - lr_warmup_step_ratio: 0.3
+ - max_grad_norm: 1
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/raw/main/trainer_config.json).
+
+### Reference
+If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
+
+```
+
+@inproceedings{ushio-camacho-collados-2021-ner,
+    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
+    author = "Ushio, Asahi  and
+      Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
+    month = apr,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.eacl-demos.7",
+    doi = "10.18653/v1/2021.eacl-demos.7",
+    pages = "53--62",
+    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
+}
+
+```

eval/metric.json

@@ -1 +0,0 @@
-{"2021.dev": {"micro/f1": 0.6272365805168986, "micro/f1_ci": {}, "micro/recall": 0.631, "micro/precision": 0.6235177865612648, "macro/f1": 0.578141938973107, "macro/f1_ci": {}, "macro/recall": 0.577392851398218, "macro/precision": 0.5799685464596722, "per_entity_metric": {"corporation": {"f1": 0.5918367346938775, "f1_ci": {}, "precision": 0.6170212765957447, "recall": 0.5686274509803921}, "creative_work": {"f1": 0.3815789473684211, "f1_ci": {}, "precision": 0.3717948717948718, "recall": 0.3918918918918919}, "event": {"f1": 0.41947565543071164, "f1_ci": {}, "precision": 0.4117647058823529, "recall": 0.42748091603053434}, "group": {"f1": 0.6105263157894737, "f1_ci": {}, "precision": 0.5846774193548387, "recall": 0.6387665198237885}, "location": {"f1": 0.5797101449275361, "f1_ci": {}, "precision": 0.6060606060606061, "recall": 0.5555555555555556}, "person": {"f1": 0.8270944741532976, "f1_ci": {}, "precision": 0.8345323741007195, "recall": 0.8197879858657244}, "product": {"f1": 0.6367713004484306, "f1_ci": {}, "precision": 0.6339285714285714, "recall": 0.6396396396396397}}}, "2021.test": {"micro/f1": 0.6230258640421148, "micro/f1_ci": {"90": [0.6139925448708724, 0.632549139769655], "95": [0.612303125388328, 0.6336744975616968]}, "micro/recall": 0.6295097132284921, "micro/precision": 0.6166742183960127, "macro/f1": 0.5758556427048315, "macro/f1_ci": {"90": [0.5656519179998177, 0.585371041220358], "95": [0.5639180067469939, 0.586921603961901]}, "macro/recall": 0.5821234872899773, "macro/precision": 0.5715554663683273, "per_entity_metric": {"corporation": {"f1": 0.5141176470588235, "f1_ci": {"90": [0.4878216484874022, 0.5397454481031415], "95": [0.48169465214919754, 0.5451600061071579]}, "precision": 0.54625, "recall": 0.4855555555555556}, "creative_work": {"f1": 0.3886075949367089, "f1_ci": {"90": [0.3597256986658403, 0.4188124874941639], "95": [0.3535082020207142, 0.42354681306371]}, "precision": 0.36160188457008247, "recall": 0.41997264021887826}, "event": {"f1": 0.4580617122990004, "f1_ci": {"90": [0.4348966918412935, 0.4793315743183817], "95": [0.43126880173262044, 0.48449054654247115]}, "precision": 0.4384359400998336, "recall": 0.47952684258416745}, "group": {"f1": 0.5660613650594865, "f1_ci": {"90": [0.5454485939592322, 0.5872104279571561], "95": [0.5417478676490005, 0.5923902852388108]}, "precision": 0.5393794749403341, "recall": 0.5955204216073782}, "location": {"f1": 0.6264564770390679, "f1_ci": {"90": [0.597997138769671, 0.6547394206191423], "95": [0.5912249546857395, 0.6582113395040892]}, "precision": 0.6150740242261103, "recall": 0.638268156424581}, "person": {"f1": 0.8196536144578314, "f1_ci": {"90": [0.8086892670302934, 0.8320832481435211], "95": [0.8064352860749925, 0.8343931055298054]}, "precision": 0.8373076923076923, "recall": 0.8027286135693216}, "product": {"f1": 0.6580310880829014, "f1_ci": {"90": [0.6350598078981826, 0.6793531734242207], "95": [0.6308296803788606, 0.6823582405935348]}, "precision": 0.662839248434238, "recall": 0.6532921810699589}}}, "2020.test": {"micro/f1": 0.6210070384407147, "micro/f1_ci": {"90": [0.598677414852038, 0.6408161660194358], "95": [0.5946501055829765, 0.6451765433928058]}, "micro/recall": 0.5952257394914374, "micro/precision": 0.6491228070175439, "macro/f1": 0.577436139660066, "macro/f1_ci": {"90": [0.553295935898165, 0.5979250785226665], "95": [0.5491543194937442, 0.6020438981681012]}, "macro/recall": 0.549500601374034, "macro/precision": 0.6119340101835135, "per_entity_metric": {"corporation": {"f1": 0.5654761904761905, "f1_ci": {"90": [0.5016501650165016, 0.616316099488875], "95": [0.4921110450408684, 0.62540329151051]}, "precision": 0.6551724137931034, "recall": 0.4973821989528796}, "creative_work": {"f1": 0.4152046783625731, "f1_ci": {"90": [0.353622668579627, 0.4713502460914881], "95": [0.3405208523037697, 0.4846713528693937]}, "precision": 0.43558282208588955, "recall": 0.39664804469273746}, "event": {"f1": 0.450354609929078, "f1_ci": {"90": [0.40072106996250306, 0.4983622553239021], "95": [0.39129403514561506, 0.5080412036406674]}, "precision": 0.42474916387959866, "recall": 0.47924528301886793}, "group": {"f1": 0.5423143350604491, "f1_ci": {"90": [0.48928618711385696, 0.5931441022144487], "95": [0.47662690763762394, 0.6043063856034276]}, "precision": 0.585820895522388, "recall": 0.5048231511254019}, "location": {"f1": 0.605263157894737, "f1_ci": {"90": [0.5276388094586867, 0.6763224071389902], "95": [0.5114728539985327, 0.6883279763714547]}, "precision": 0.6618705035971223, "recall": 0.5575757575757576}, "person": {"f1": 0.8185801928133217, "f1_ci": {"90": [0.7927262257895654, 0.8431634280358857], "95": [0.7871878287083727, 0.8474604661726711]}, "precision": 0.8568807339449541, "recall": 0.7835570469798657}, "product": {"f1": 0.6448598130841121, "f1_ci": {"90": [0.590887445887446, 0.6986353389977458], "95": [0.5809278885848259, 0.7056143195046112]}, "precision": 0.6634615384615384, "recall": 0.6272727272727273}}}, "2021.test (span detection)": {"micro/f1": 0.7661839619941617, "micro/f1_ci": {}, "micro/recall": 0.7740256736440384, "micro/precision": 0.7584995466908432, "macro/f1": 0.7661839619941617, "macro/f1_ci": {}, "macro/recall": 0.7740256736440384, "macro/precision": 0.7584995466908432}, "2020.test (span detection)": {"micro/f1": 0.7298321602598808, "micro/f1_ci": {}, "micro/recall": 0.6995329527763363, "micro/precision": 0.7628749292586304, "macro/f1": 0.7298321602598808, "macro/f1_ci": {}, "macro/recall": 0.6995329527763363, "macro/precision": 0.7628749292586304}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6210070384407147, "micro/f1_ci": {"90": [0.598677414852038, 0.6408161660194358], "95": [0.5946501055829765, 0.6451765433928058]}, "micro/recall": 0.5952257394914374, "micro/precision": 0.6491228070175439, "macro/f1": 0.577436139660066, "macro/f1_ci": {"90": [0.553295935898165, 0.5979250785226665], "95": [0.5491543194937442, 0.6020438981681012]}, "macro/recall": 0.549500601374034, "macro/precision": 0.6119340101835135, "per_entity_metric": {"corporation": {"f1": 0.5654761904761905, "f1_ci": {"90": [0.5016501650165016, 0.616316099488875], "95": [0.4921110450408684, 0.62540329151051]}, "precision": 0.6551724137931034, "recall": 0.4973821989528796}, "creative_work": {"f1": 0.4152046783625731, "f1_ci": {"90": [0.353622668579627, 0.4713502460914881], "95": [0.3405208523037697, 0.4846713528693937]}, "precision": 0.43558282208588955, "recall": 0.39664804469273746}, "event": {"f1": 0.450354609929078, "f1_ci": {"90": [0.40072106996250306, 0.4983622553239021], "95": [0.39129403514561506, 0.5080412036406674]}, "precision": 0.42474916387959866, "recall": 0.47924528301886793}, "group": {"f1": 0.5423143350604491, "f1_ci": {"90": [0.48928618711385696, 0.5931441022144487], "95": [0.47662690763762394, 0.6043063856034276]}, "precision": 0.585820895522388, "recall": 0.5048231511254019}, "location": {"f1": 0.605263157894737, "f1_ci": {"90": [0.5276388094586867, 0.6763224071389902], "95": [0.5114728539985327, 0.6883279763714547]}, "precision": 0.6618705035971223, "recall": 0.5575757575757576}, "person": {"f1": 0.8185801928133217, "f1_ci": {"90": [0.7927262257895654, 0.8431634280358857], "95": [0.7871878287083727, 0.8474604661726711]}, "precision": 0.8568807339449541, "recall": 0.7835570469798657}, "product": {"f1": 0.6448598130841121, "f1_ci": {"90": [0.590887445887446, 0.6986353389977458], "95": [0.5809278885848259, 0.7056143195046112]}, "precision": 0.6634615384615384, "recall": 0.6272727272727273}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6230258640421148, "micro/f1_ci": {"90": [0.6139925448708724, 0.632549139769655], "95": [0.612303125388328, 0.6336744975616968]}, "micro/recall": 0.6295097132284921, "micro/precision": 0.6166742183960127, "macro/f1": 0.5758556427048315, "macro/f1_ci": {"90": [0.5656519179998177, 0.585371041220358], "95": [0.5639180067469939, 0.586921603961901]}, "macro/recall": 0.5821234872899773, "macro/precision": 0.5715554663683273, "per_entity_metric": {"corporation": {"f1": 0.5141176470588235, "f1_ci": {"90": [0.4878216484874022, 0.5397454481031415], "95": [0.48169465214919754, 0.5451600061071579]}, "precision": 0.54625, "recall": 0.4855555555555556}, "creative_work": {"f1": 0.3886075949367089, "f1_ci": {"90": [0.3597256986658403, 0.4188124874941639], "95": [0.3535082020207142, 0.42354681306371]}, "precision": 0.36160188457008247, "recall": 0.41997264021887826}, "event": {"f1": 0.4580617122990004, "f1_ci": {"90": [0.4348966918412935, 0.4793315743183817], "95": [0.43126880173262044, 0.48449054654247115]}, "precision": 0.4384359400998336, "recall": 0.47952684258416745}, "group": {"f1": 0.5660613650594865, "f1_ci": {"90": [0.5454485939592322, 0.5872104279571561], "95": [0.5417478676490005, 0.5923902852388108]}, "precision": 0.5393794749403341, "recall": 0.5955204216073782}, "location": {"f1": 0.6264564770390679, "f1_ci": {"90": [0.597997138769671, 0.6547394206191423], "95": [0.5912249546857395, 0.6582113395040892]}, "precision": 0.6150740242261103, "recall": 0.638268156424581}, "person": {"f1": 0.8196536144578314, "f1_ci": {"90": [0.8086892670302934, 0.8320832481435211], "95": [0.8064352860749925, 0.8343931055298054]}, "precision": 0.8373076923076923, "recall": 0.8027286135693216}, "product": {"f1": 0.6580310880829014, "f1_ci": {"90": [0.6350598078981826, 0.6793531734242207], "95": [0.6308296803788606, 0.6823582405935348]}, "precision": 0.662839248434238, "recall": 0.6532921810699589}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7298321602598808, "micro/f1_ci": {}, "micro/recall": 0.6995329527763363, "micro/precision": 0.7628749292586304, "macro/f1": 0.7298321602598808, "macro/f1_ci": {}, "macro/recall": 0.6995329527763363, "macro/precision": 0.7628749292586304}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7661839619941617, "micro/f1_ci": {}, "micro/recall": 0.7740256736440384, "micro/precision": 0.7584995466908432, "macro/f1": 0.7661839619941617, "macro/f1_ci": {}, "macro/recall": 0.7740256736440384, "macro/precision": 0.7584995466908432}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020_2021.train", "model": "bert-base-cased", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 0.0001, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.3, "max_grad_norm": 1}
+{"dataset": ["tner/tweetner7"], "dataset_split": "train_all", "dataset_name": null, "local_dataset": null, "model": "bert-base-cased", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 0.0001, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.3, "max_grad_norm": 1}