model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-large-tweetner7-selflabel2021
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: tner/tweetner7
+      type: tner/tweetner7
+      args: tner/tweetner7
+    metrics:
+    - name: F1 (test_2021)
+      type: f1
+      value: 0.6460286973223365
+    - name: Precision (test_2021)
+      type: precision
+      value: 0.6315440689198144
+    - name: Recall (test_2021)
+      type: recall
+      value: 0.6611933395004626
+    - name: Macro F1 (test_2021)
+      type: f1_macro
+      value: 0.5944660768713126
+    - name: Macro Precision (test_2021)
+      type: precision_macro
+      value: 0.5801646971717881
+    - name: Macro Recall (test_2021)
+      type: recall_macro
+      value: 0.6174983598336771
+    - name: Entity Span F1 (test_2021)
+      type: f1_entity_span
+      value: 0.7857183209988137
+    - name: Entity Span Precision (test_2020)
+      type: precision_entity_span
+      value: 0.7680583167660703
+    - name: Entity Span Recall (test_2021)
+      type: recall_entity_span
+      value: 0.8042095524459351
+    - name: F1 (test_2020)
+      type: f1
+      value: 0.6475365457498646
+    - name: Precision (test_2020)
+      type: precision
+      value: 0.6768534238822863
+    - name: Recall (test_2020)
+      type: recall
+      value: 0.6206538661131292
+    - name: Macro F1 (test_2020)
+      type: f1_macro
+      value: 0.6064934754479069
+    - name: Macro Precision (test_2020)
+      type: precision_macro
+      value: 0.63365172906493
+    - name: Macro Recall (test_2020)
+      type: recall_macro
+      value: 0.5889063993107413
+    - name: Entity Span F1 (test_2020)
+      type: f1_entity_span
+      value: 0.7663146493365827
+    - name: Entity Span Precision (test_2020)
+      type: precision_entity_span
+      value: 0.8012457531143827
+    - name: Entity Span Recall (test_2020)
+      type: recall_entity_span
+      value: 0.7343020238713025
+
+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-large-tweetner7-selflabel2021
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2021` split of the [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) annotated by [tner/roberta-large](https://huggingface.co/tner/tner/roberta-large-tweetner7-2020)). Please check [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling) for more detail of reproducing the model. 
+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): 0.6460286973223365
+- Precision (micro): 0.6315440689198144
+- Recall (micro): 0.6611933395004626
+- F1 (macro): 0.5944660768713126
+- Precision (macro): 0.5801646971717881
+- Recall (macro): 0.6174983598336771
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.5021008403361344
+- creative_work: 0.4589000591366056
+- event: 0.45184799583550234
+- group: 0.602966540186271
+- location: 0.667091836734694
+- person: 0.8345784418356457
+- product: 0.6437768240343348 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.63733724830433, 0.6556095472315113]
+    - 95%: [0.6353273787551952, 0.6574352280031737] 
+- F1 (macro): 
+    - 90%: [0.63733724830433, 0.6556095472315113]
+    - 95%: [0.6353273787551952, 0.6574352280031737] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021/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-large-tweetner7-selflabel2021")
+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
+ - dataset_name: None
+ - local_dataset: {'train': 'tweet_ner/2021.extra.tner/roberta-large-2020.txt', 'validation': 'tweet_ner/2020.dev.txt'}
+ - model: roberta-large
+ - crf: True
+ - max_length: 128
+ - epoch: 30
+ - batch_size: 32
+ - lr: 1e-05
+ - 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-large-tweetner7-selflabel2021/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

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+{"micro/f1": 0.6475365457498646, "micro/f1_ci": {"90": [0.6278810391051123, 0.6659767107844533], "95": [0.6235838190810499, 0.6692123090249849]}, "micro/recall": 0.6206538661131292, "micro/precision": 0.6768534238822863, "macro/f1": 0.6064934754479069, "macro/f1_ci": {"90": [0.5836818822839157, 0.6251332135885596], "95": [0.5795678730454966, 0.6290985642471119]}, "macro/recall": 0.5889063993107413, "macro/precision": 0.63365172906493, "per_entity_metric": {"corporation": {"f1": 0.5618556701030927, "f1_ci": {"90": [0.5, 0.6172598627787307], "95": [0.4885981049752539, 0.6259302015554888]}, "precision": 0.5532994923857868, "recall": 0.5706806282722513}, "creative_work": {"f1": 0.5040650406504065, "f1_ci": {"90": [0.44183047910974876, 0.5584744579292135], "95": [0.4330176767676768, 0.572193256090315]}, "precision": 0.48947368421052634, "recall": 0.5195530726256983}, "event": {"f1": 0.4731182795698925, "f1_ci": {"90": [0.41807994639571167, 0.5219746433628906], "95": [0.40788644405130475, 0.5325269892043183]}, "precision": 0.55, "recall": 0.41509433962264153}, "group": {"f1": 0.5521235521235521, "f1_ci": {"90": [0.5008620689655173, 0.6033057851239669], "95": [0.48913461538461533, 0.6141652372784449]}, "precision": 0.6908212560386473, "recall": 0.45980707395498394}, "location": {"f1": 0.6746268656716418, "f1_ci": {"90": [0.610678987545199, 0.7329210191981933], "95": [0.5951452376616281, 0.741727195043794]}, "precision": 0.6647058823529411, "recall": 0.6848484848484848}, "person": {"f1": 0.8190314358538658, "f1_ci": {"90": [0.7936307012256378, 0.8422012779178834], "95": [0.787314635592392, 0.846041418767823]}, "precision": 0.8296041308089501, "recall": 0.8087248322147651}, "product": {"f1": 0.660633484162896, "f1_ci": {"90": [0.6098026856808009, 0.7045074140794477], "95": [0.6030337195196198, 0.7142857142857143]}, "precision": 0.6576576576576577, "recall": 0.6636363636363637}}}

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

@@ -1 +1 @@
-{"2020.dev": {"micro/f1": 0.648148148148148, "micro/f1_ci": {}, "micro/recall": 0.6217345872518286, "micro/precision": 0.676905574516496, "macro/f1": 0.5884762061336491, "macro/f1_ci": {}, "macro/recall": 0.5710043863284546, "macro/precision": 0.6142162065355595, "per_entity_metric": {"corporation": {"f1": 0.48947368421052634, "f1_ci": {}, "precision": 0.5254237288135594, "recall": 0.458128078817734}, "creative_work": {"f1": 0.5048076923076923, "f1_ci": {}, "precision": 0.5048076923076923, "recall": 0.5048076923076923}, "event": {"f1": 0.3665893271461717, "f1_ci": {}, "precision": 0.4514285714285714, "recall": 0.30859375}, "group": {"f1": 0.5833333333333333, "f1_ci": {}, "precision": 0.6574585635359116, "recall": 0.5242290748898678}, "location": {"f1": 0.6515151515151515, "f1_ci": {}, "precision": 0.6, "recall": 0.712707182320442}, "person": {"f1": 0.8754266211604095, "f1_ci": {}, "precision": 0.8937282229965157, "recall": 0.8578595317725752}, "product": {"f1": 0.6481876332622601, "f1_ci": {}, "precision": 0.6666666666666666, "recall": 0.6307053941908713}}}, "2021.test": {"micro/f1": 0.6460286973223365, "micro/f1_ci": {"90": [0.63733724830433, 0.6556095472315113], "95": [0.6353273787551952, 0.6574352280031737]}, "micro/recall": 0.6611933395004626, "micro/precision": 0.6315440689198144, "macro/f1": 0.5944660768713126, "macro/f1_ci": {"90": [0.5849081455127132, 0.604378357848271], "95": [0.5831045169205292, 0.6060640264322646]}, "macro/recall": 0.6174983598336771, "macro/precision": 0.5801646971717881, "per_entity_metric": {"corporation": {"f1": 0.5021008403361344, "f1_ci": {"90": [0.4789834700215685, 0.5271894733215282], "95": [0.4745525584297708, 0.5316368161943792]}, "precision": 0.4760956175298805, "recall": 0.5311111111111111}, "creative_work": {"f1": 0.4589000591366056, "f1_ci": {"90": [0.4306853034260597, 0.48736707656304246], "95": [0.4254521058174994, 0.4930922458104417]}, "precision": 0.4041666666666667, "recall": 0.53077975376197}, "event": {"f1": 0.45184799583550234, "f1_ci": {"90": [0.4270124161552733, 0.4763863451671203], "95": [0.422088474249013, 0.4810302713987474]}, "precision": 0.5279805352798054, "recall": 0.39490445859872614}, "group": {"f1": 0.602966540186271, "f1_ci": {"90": [0.5815257576832498, 0.6258527871580286], "95": [0.5783987738887143, 0.6301280206163071]}, "precision": 0.6328747284576394, "recall": 0.5757575757575758}, "location": {"f1": 0.667091836734694, "f1_ci": {"90": [0.6408738272872012, 0.6929653849735745], "95": [0.6349154946952874, 0.6970575007002522]}, "precision": 0.613849765258216, "recall": 0.7304469273743017}, "person": {"f1": 0.8345784418356457, "f1_ci": {"90": [0.8240660420354806, 0.8454018578179613], "95": [0.8221766134345567, 0.8467589019305347]}, "precision": 0.8061855670103093, "recall": 0.8650442477876106}, "product": {"f1": 0.6437768240343348, "f1_ci": {"90": [0.622562297681236, 0.6648001670663515], "95": [0.6190244802359166, 0.668536346487744]}, "precision": 0.6, "recall": 0.6944444444444444}}}, "2020.test": {"micro/f1": 0.6475365457498646, "micro/f1_ci": {"90": [0.6278810391051123, 0.6659767107844533], "95": [0.6235838190810499, 0.6692123090249849]}, "micro/recall": 0.6206538661131292, "micro/precision": 0.6768534238822863, "macro/f1": 0.6064934754479069, "macro/f1_ci": {"90": [0.5836818822839157, 0.6251332135885596], "95": [0.5795678730454966, 0.6290985642471119]}, "macro/recall": 0.5889063993107413, "macro/precision": 0.63365172906493, "per_entity_metric": {"corporation": {"f1": 0.5618556701030927, "f1_ci": {"90": [0.5, 0.6172598627787307], "95": [0.4885981049752539, 0.6259302015554888]}, "precision": 0.5532994923857868, "recall": 0.5706806282722513}, "creative_work": {"f1": 0.5040650406504065, "f1_ci": {"90": [0.44183047910974876, 0.5584744579292135], "95": [0.4330176767676768, 0.572193256090315]}, "precision": 0.48947368421052634, "recall": 0.5195530726256983}, "event": {"f1": 0.4731182795698925, "f1_ci": {"90": [0.41807994639571167, 0.5219746433628906], "95": [0.40788644405130475, 0.5325269892043183]}, "precision": 0.55, "recall": 0.41509433962264153}, "group": {"f1": 0.5521235521235521, "f1_ci": {"90": [0.5008620689655173, 0.6033057851239669], "95": [0.48913461538461533, 0.6141652372784449]}, "precision": 0.6908212560386473, "recall": 0.45980707395498394}, "location": {"f1": 0.6746268656716418, "f1_ci": {"90": [0.610678987545199, 0.7329210191981933], "95": [0.5951452376616281, 0.741727195043794]}, "precision": 0.6647058823529411, "recall": 0.6848484848484848}, "person": {"f1": 0.8190314358538658, "f1_ci": {"90": [0.7936307012256378, 0.8422012779178834], "95": [0.787314635592392, 0.846041418767823]}, "precision": 0.8296041308089501, "recall": 0.8087248322147651}, "product": {"f1": 0.660633484162896, "f1_ci": {"90": [0.6098026856808009, 0.7045074140794477], "95": [0.6030337195196198, 0.7142857142857143]}, "precision": 0.6576576576576577, "recall": 0.6636363636363637}}}, "2021.test (span detection)": {"micro/f1": 0.7857183209988137, "micro/f1_ci": {}, "micro/recall": 0.8042095524459351, "micro/precision": 0.7680583167660703, "macro/f1": 0.7857183209988137, "macro/f1_ci": {}, "macro/recall": 0.8042095524459351, "macro/precision": 0.7680583167660703}, "2020.test (span detection)": {"micro/f1": 0.7663146493365827, "micro/f1_ci": {}, "micro/recall": 0.7343020238713025, "micro/precision": 0.8012457531143827, "macro/f1": 0.7663146493365827, "macro/f1_ci": {}, "macro/recall": 0.7343020238713025, "macro/precision": 0.8012457531143827}}
+{"micro/f1": 0.6460286973223365, "micro/f1_ci": {"90": [0.63733724830433, 0.6556095472315113], "95": [0.6353273787551952, 0.6574352280031737]}, "micro/recall": 0.6611933395004626, "micro/precision": 0.6315440689198144, "macro/f1": 0.5944660768713126, "macro/f1_ci": {"90": [0.5849081455127132, 0.604378357848271], "95": [0.5831045169205292, 0.6060640264322646]}, "macro/recall": 0.6174983598336771, "macro/precision": 0.5801646971717881, "per_entity_metric": {"corporation": {"f1": 0.5021008403361344, "f1_ci": {"90": [0.4789834700215685, 0.5271894733215282], "95": [0.4745525584297708, 0.5316368161943792]}, "precision": 0.4760956175298805, "recall": 0.5311111111111111}, "creative_work": {"f1": 0.4589000591366056, "f1_ci": {"90": [0.4306853034260597, 0.48736707656304246], "95": [0.4254521058174994, 0.4930922458104417]}, "precision": 0.4041666666666667, "recall": 0.53077975376197}, "event": {"f1": 0.45184799583550234, "f1_ci": {"90": [0.4270124161552733, 0.4763863451671203], "95": [0.422088474249013, 0.4810302713987474]}, "precision": 0.5279805352798054, "recall": 0.39490445859872614}, "group": {"f1": 0.602966540186271, "f1_ci": {"90": [0.5815257576832498, 0.6258527871580286], "95": [0.5783987738887143, 0.6301280206163071]}, "precision": 0.6328747284576394, "recall": 0.5757575757575758}, "location": {"f1": 0.667091836734694, "f1_ci": {"90": [0.6408738272872012, 0.6929653849735745], "95": [0.6349154946952874, 0.6970575007002522]}, "precision": 0.613849765258216, "recall": 0.7304469273743017}, "person": {"f1": 0.8345784418356457, "f1_ci": {"90": [0.8240660420354806, 0.8454018578179613], "95": [0.8221766134345567, 0.8467589019305347]}, "precision": 0.8061855670103093, "recall": 0.8650442477876106}, "product": {"f1": 0.6437768240343348, "f1_ci": {"90": [0.622562297681236, 0.6648001670663515], "95": [0.6190244802359166, 0.668536346487744]}, "precision": 0.6, "recall": 0.6944444444444444}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7663146493365827, "micro/f1_ci": {}, "micro/recall": 0.7343020238713025, "micro/precision": 0.8012457531143827, "macro/f1": 0.7663146493365827, "macro/f1_ci": {}, "macro/recall": 0.7343020238713025, "macro/precision": 0.8012457531143827}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7857183209988137, "micro/f1_ci": {}, "micro/recall": 0.8042095524459351, "micro/precision": 0.7680583167660703, "macro/f1": 0.7857183209988137, "macro/f1_ci": {}, "macro/recall": 0.8042095524459351, "macro/precision": 0.7680583167660703}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2021.extra.roberta-large-2020", "model": "roberta-large", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 1e-05, "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", "dataset_name": null, "local_dataset": {"train": "tweet_ner/2021.extra.tner/roberta-large-2020.txt", "validation": "tweet_ner/2020.dev.txt"}, "model": "roberta-large", "crf": true, "max_length": 128, "epoch": 30, "batch_size": 32, "lr": 1e-05, "random_seed": 0, "gradient_accumulation_steps": 1, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.15, "max_grad_norm": 1}