model update

README.md

@@ -0,0 +1,176 @@
+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-base-tweetner7-2020-2021-continuous
+  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: 0.6547126972113873
+    - name: Precision
+      type: precision
+      value: 0.6592801031773947
+    - name: Recall
+      type: recall
+      value: 0.6502081406105458
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6000787312274737
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.603865779286349
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5992466120658141
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7809734513274336
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7863758940086762
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7756447322770903
+  - 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: 0.651460361613352
+    - name: Precision
+      type: precision
+      value: 0.7020383693045563
+    - name: Recall
+      type: recall
+      value: 0.6076803321224702
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6081745135588633
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6574828031156369
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5706180236424009
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7504867872044506
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.8087529976019184
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7000518941359626
+
+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/roberta-base-tweetner7-2020-2021-continuous
+
+This model is a fine-tuned version of [tner/roberta-base-tweetner-2020](https://huggingface.co/tner/roberta-base-tweetner-2020) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on `train_2021`. 
+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): 0.6547126972113873
+- Precision (micro): 0.6592801031773947
+- Recall (micro): 0.6502081406105458
+- F1 (macro): 0.6000787312274737
+- Precision (macro): 0.603865779286349
+- Recall (macro): 0.5992466120658141
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.509673852957435
+- creative_work: 0.41677588466579296
+- event: 0.4675062972292191
+- group: 0.6152256286600069
+- location: 0.6798159105851413
+- person: 0.8448868778280542
+- product: 0.6666666666666667 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6458722707634147, 0.6637540527089854]
+    - 95%: [0.6443720180740024, 0.6654476640585366] 
+- F1 (macro): 
+    - 90%: [0.6458722707634147, 0.6637540527089854]
+    - 95%: [0.6443720180740024, 0.6654476640585366] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-base-tweetner7-2020-2021-continuous/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-base-tweetner7-2020-2021-continuous/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/roberta-base-tweetner7-2020-2021-continuous")
+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_2021
+ - dataset_name: None
+ - local_dataset: None
+ - model: tner/roberta-base-tweetner-2020
+ - crf: True
+ - max_length: 128
+ - epoch: 30
+ - batch_size: 32
+ - lr: 1e-06
+ - random_seed: 0
+ - gradient_accumulation_steps: 1
+ - weight_decay: 1e-07
+ - lr_warmup_step_ratio: 0.15
+ - max_grad_norm: 1
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-base-tweetner7-2020-2021-continuous/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.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.651460361613352, "micro/f1_ci": {"90": [0.6305997364833857, 0.6695794736812504], "95": [0.6269839088527567, 0.6749042831834553]}, "micro/recall": 0.6076803321224702, "micro/precision": 0.7020383693045563, "macro/f1": 0.6081745135588633, "macro/f1_ci": {"90": [0.5854739740621966, 0.6281268906531955], "95": [0.581483892413636, 0.6343555010851516]}, "macro/recall": 0.5706180236424009, "macro/precision": 0.6574828031156369, "per_entity_metric": {"corporation": {"f1": 0.5804749340369394, "f1_ci": {"90": [0.5156140581232493, 0.6325686506372356], "95": [0.5058044742163802, 0.6415385361594802]}, "precision": 0.5851063829787234, "recall": 0.5759162303664922}, "creative_work": {"f1": 0.49853372434017595, "f1_ci": {"90": [0.4344938384299407, 0.5531050547402748], "95": [0.42452631578947353, 0.5626611469938013]}, "precision": 0.5246913580246914, "recall": 0.4748603351955307}, "event": {"f1": 0.4473684210526316, "f1_ci": {"90": [0.39322736633500616, 0.5023485303606792], "95": [0.38337182448036955, 0.511726656523523]}, "precision": 0.5340314136125655, "recall": 0.3849056603773585}, "group": {"f1": 0.560460652591171, "f1_ci": {"90": [0.5110098560700875, 0.608893264449275], "95": [0.5010438972162741, 0.6203138617664004]}, "precision": 0.6952380952380952, "recall": 0.4694533762057878}, "location": {"f1": 0.6707692307692309, "f1_ci": {"90": [0.6067931688804554, 0.7277823315118398], "95": [0.5954828423929547, 0.7365481158894334]}, "precision": 0.68125, "recall": 0.6606060606060606}, "person": {"f1": 0.8263473053892216, "f1_ci": {"90": [0.7996677782129142, 0.8497937470551579], "95": [0.7938117295076058, 0.856165669034084]}, "precision": 0.8429319371727748, "recall": 0.8104026845637584}, "product": {"f1": 0.6732673267326733, "f1_ci": {"90": [0.6195010523709298, 0.7234149470758046], "95": [0.6084558684914979, 0.7323297582567245]}, "precision": 0.7391304347826086, "recall": 0.6181818181818182}}}

eval/{metric.json → metric.test_2021.json}

@@ -1 +1 @@
-{"2021.dev": {"micro/f1": 0.6488706365503081, "micro/f1_ci": {}, "micro/recall": 0.632, "micro/precision": 0.6666666666666666, "macro/f1": 0.5997203254958251, "macro/f1_ci": {}, "macro/recall": 0.5869343642573417, "macro/precision": 0.618503242002026, "per_entity_metric": {"corporation": {"f1": 0.5645933014354066, "f1_ci": {}, "precision": 0.5514018691588785, "recall": 0.5784313725490197}, "creative_work": {"f1": 0.44295302013422816, "f1_ci": {}, "precision": 0.44, "recall": 0.44594594594594594}, "event": {"f1": 0.415929203539823, "f1_ci": {}, "precision": 0.49473684210526314, "recall": 0.35877862595419846}, "group": {"f1": 0.6372093023255814, "f1_ci": {}, "precision": 0.6748768472906403, "recall": 0.6035242290748899}, "location": {"f1": 0.6486486486486486, "f1_ci": {}, "precision": 0.631578947368421, "recall": 0.6666666666666666}, "person": {"f1": 0.8253424657534247, "f1_ci": {}, "precision": 0.8006644518272426, "recall": 0.8515901060070671}, "product": {"f1": 0.6633663366336634, "f1_ci": {}, "precision": 0.7362637362637363, "recall": 0.6036036036036037}}}, "2021.test": {"micro/f1": 0.6547126972113873, "micro/f1_ci": {"90": [0.6458722707634147, 0.6637540527089854], "95": [0.6443720180740024, 0.6654476640585366]}, "micro/recall": 0.6502081406105458, "micro/precision": 0.6592801031773947, "macro/f1": 0.6000787312274737, "macro/f1_ci": {"90": [0.5905237241709625, 0.6095507771622684], "95": [0.5885881380317086, 0.6113013659787363]}, "macro/recall": 0.5992466120658141, "macro/precision": 0.603865779286349, "per_entity_metric": {"corporation": {"f1": 0.509673852957435, "f1_ci": {"90": [0.484179870421451, 0.5353061422313796], "95": [0.47824814173709396, 0.5405723393787932]}, "precision": 0.5071507150715071, "recall": 0.5122222222222222}, "creative_work": {"f1": 0.41677588466579296, "f1_ci": {"90": [0.3865633906408017, 0.44653977444332704], "95": [0.37910324725354655, 0.4510138162184456]}, "precision": 0.4, "recall": 0.43502051983584133}, "event": {"f1": 0.4675062972292191, "f1_ci": {"90": [0.4432285719295966, 0.49052772395711874], "95": [0.4381138582657509, 0.49466101670892687]}, "precision": 0.5237020316027088, "recall": 0.4222020018198362}, "group": {"f1": 0.6152256286600069, "f1_ci": {"90": [0.5945103130246991, 0.6374704452133914], "95": [0.5902147083464299, 0.6407439785680712]}, "precision": 0.6447653429602888, "recall": 0.5882740447957839}, "location": {"f1": 0.6798159105851413, "f1_ci": {"90": [0.6515954195494981, 0.7065228684260313], "95": [0.646596286962582, 0.7124493642560418]}, "precision": 0.6422360248447205, "recall": 0.7220670391061452}, "person": {"f1": 0.8448868778280542, "f1_ci": {"90": [0.8343973181095194, 0.8549887043226098], "95": [0.8319527490533347, 0.8570455559167381]}, "precision": 0.8297191610380377, "recall": 0.8606194690265486}, "product": {"f1": 0.6666666666666667, "f1_ci": {"90": [0.6452512118485499, 0.6877656402687181], "95": [0.6405735428551591, 0.6908174750739359]}, "precision": 0.6794871794871795, "recall": 0.654320987654321}}}, "2020.test": {"micro/f1": 0.651460361613352, "micro/f1_ci": {"90": [0.6305997364833857, 0.6695794736812504], "95": [0.6269839088527567, 0.6749042831834553]}, "micro/recall": 0.6076803321224702, "micro/precision": 0.7020383693045563, "macro/f1": 0.6081745135588633, "macro/f1_ci": {"90": [0.5854739740621966, 0.6281268906531955], "95": [0.581483892413636, 0.6343555010851516]}, "macro/recall": 0.5706180236424009, "macro/precision": 0.6574828031156369, "per_entity_metric": {"corporation": {"f1": 0.5804749340369394, "f1_ci": {"90": [0.5156140581232493, 0.6325686506372356], "95": [0.5058044742163802, 0.6415385361594802]}, "precision": 0.5851063829787234, "recall": 0.5759162303664922}, "creative_work": {"f1": 0.49853372434017595, "f1_ci": {"90": [0.4344938384299407, 0.5531050547402748], "95": [0.42452631578947353, 0.5626611469938013]}, "precision": 0.5246913580246914, "recall": 0.4748603351955307}, "event": {"f1": 0.4473684210526316, "f1_ci": {"90": [0.39322736633500616, 0.5023485303606792], "95": [0.38337182448036955, 0.511726656523523]}, "precision": 0.5340314136125655, "recall": 0.3849056603773585}, "group": {"f1": 0.560460652591171, "f1_ci": {"90": [0.5110098560700875, 0.608893264449275], "95": [0.5010438972162741, 0.6203138617664004]}, "precision": 0.6952380952380952, "recall": 0.4694533762057878}, "location": {"f1": 0.6707692307692309, "f1_ci": {"90": [0.6067931688804554, 0.7277823315118398], "95": [0.5954828423929547, 0.7365481158894334]}, "precision": 0.68125, "recall": 0.6606060606060606}, "person": {"f1": 0.8263473053892216, "f1_ci": {"90": [0.7996677782129142, 0.8497937470551579], "95": [0.7938117295076058, 0.856165669034084]}, "precision": 0.8429319371727748, "recall": 0.8104026845637584}, "product": {"f1": 0.6732673267326733, "f1_ci": {"90": [0.6195010523709298, 0.7234149470758046], "95": [0.6084558684914979, 0.7323297582567245]}, "precision": 0.7391304347826086, "recall": 0.6181818181818182}}}, "2021.test (span detection)": {"micro/f1": 0.7809734513274336, "micro/f1_ci": {}, "micro/recall": 0.7756447322770903, "micro/precision": 0.7863758940086762, "macro/f1": 0.7809734513274336, "macro/f1_ci": {}, "macro/recall": 0.7756447322770903, "macro/precision": 0.7863758940086762}, "2020.test (span detection)": {"micro/f1": 0.7504867872044506, "micro/f1_ci": {}, "micro/recall": 0.7000518941359626, "micro/precision": 0.8087529976019184, "macro/f1": 0.7504867872044506, "macro/f1_ci": {}, "macro/recall": 0.7000518941359626, "macro/precision": 0.8087529976019184}}
+{"micro/f1": 0.6547126972113873, "micro/f1_ci": {"90": [0.6458722707634147, 0.6637540527089854], "95": [0.6443720180740024, 0.6654476640585366]}, "micro/recall": 0.6502081406105458, "micro/precision": 0.6592801031773947, "macro/f1": 0.6000787312274737, "macro/f1_ci": {"90": [0.5905237241709625, 0.6095507771622684], "95": [0.5885881380317086, 0.6113013659787363]}, "macro/recall": 0.5992466120658141, "macro/precision": 0.603865779286349, "per_entity_metric": {"corporation": {"f1": 0.509673852957435, "f1_ci": {"90": [0.484179870421451, 0.5353061422313796], "95": [0.47824814173709396, 0.5405723393787932]}, "precision": 0.5071507150715071, "recall": 0.5122222222222222}, "creative_work": {"f1": 0.41677588466579296, "f1_ci": {"90": [0.3865633906408017, 0.44653977444332704], "95": [0.37910324725354655, 0.4510138162184456]}, "precision": 0.4, "recall": 0.43502051983584133}, "event": {"f1": 0.4675062972292191, "f1_ci": {"90": [0.4432285719295966, 0.49052772395711874], "95": [0.4381138582657509, 0.49466101670892687]}, "precision": 0.5237020316027088, "recall": 0.4222020018198362}, "group": {"f1": 0.6152256286600069, "f1_ci": {"90": [0.5945103130246991, 0.6374704452133914], "95": [0.5902147083464299, 0.6407439785680712]}, "precision": 0.6447653429602888, "recall": 0.5882740447957839}, "location": {"f1": 0.6798159105851413, "f1_ci": {"90": [0.6515954195494981, 0.7065228684260313], "95": [0.646596286962582, 0.7124493642560418]}, "precision": 0.6422360248447205, "recall": 0.7220670391061452}, "person": {"f1": 0.8448868778280542, "f1_ci": {"90": [0.8343973181095194, 0.8549887043226098], "95": [0.8319527490533347, 0.8570455559167381]}, "precision": 0.8297191610380377, "recall": 0.8606194690265486}, "product": {"f1": 0.6666666666666667, "f1_ci": {"90": [0.6452512118485499, 0.6877656402687181], "95": [0.6405735428551591, 0.6908174750739359]}, "precision": 0.6794871794871795, "recall": 0.654320987654321}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7504867872044506, "micro/f1_ci": {}, "micro/recall": 0.7000518941359626, "micro/precision": 0.8087529976019184, "macro/f1": 0.7504867872044506, "macro/f1_ci": {}, "macro/recall": 0.7000518941359626, "macro/precision": 0.8087529976019184}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7809734513274336, "micro/f1_ci": {}, "micro/recall": 0.7756447322770903, "micro/precision": 0.7863758940086762, "macro/f1": 0.7809734513274336, "macro/f1_ci": {}, "macro/recall": 0.7756447322770903, "macro/precision": 0.7863758940086762}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2021.train", "model": "tner/roberta-base-tweetner-2020", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 1e-06, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.15, "max_grad_norm": 1}
+{"dataset": ["tner/tweetner7"], "dataset_split": "train_2021", "dataset_name": null, "local_dataset": null, "model": "tner/roberta-base-tweetner-2020", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 1e-06, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.15, "max_grad_norm": 1}