model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bertweet-base-tweetner7-2020
+  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: 0.6405933917734322
+    - name: Precision
+      type: precision
+      value: 0.6230870135548754
+    - name: Recall
+      type: recall
+      value: 0.6591119333950046
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5944351160023098
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5741906773305591
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6180335313677364
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7790952514751335
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7577612592916484
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.8016653174511391
+  - 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: 0.6638023630504833
+    - name: Precision
+      type: precision
+      value: 0.6878130217028381
+    - name: Recall
+      type: recall
+      value: 0.6414115204981837
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6240651348448572
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6445261805050383
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6088211190017436
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7773301101262422
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.8056792873051225
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7509081473793461
+
+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/bertweet-base-tweetner7-2020
+
+This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` 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): 0.6405933917734322
+- Precision (micro): 0.6230870135548754
+- Recall (micro): 0.6591119333950046
+- F1 (macro): 0.5944351160023098
+- Precision (macro): 0.5741906773305591
+- Recall (macro): 0.6180335313677364
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.5161987041036716
+- creative_work: 0.4572468162522741
+- event: 0.4586907449209932
+- group: 0.5974197816738339
+- location: 0.6470211402946828
+- person: 0.8270704122582998
+- product: 0.6573982125124131 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.631778937808505, 0.6496155272705774]
+    - 95%: [0.6304400232899813, 0.6514114604724054] 
+- F1 (macro): 
+    - 90%: [0.631778937808505, 0.6496155272705774]
+    - 95%: [0.6304400232899813, 0.6514114604724054] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-base-tweetner7-2020/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bertweet-base-tweetner7-2020/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/bertweet-base-tweetner7-2020")
+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_2020
+ - dataset_name: None
+ - local_dataset: None
+ - model: vinai/bertweet-base
+ - 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/bertweet-base-tweetner7-2020/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

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-{"2020.dev": {"micro/f1": 0.6461870115871734, "micro/f1_ci": {}, "micro/recall": 0.6264367816091954, "micro/precision": 0.6672231496939344, "macro/f1": 0.5926978856418852, "macro/f1_ci": {}, "macro/recall": 0.5761073706964339, "macro/precision": 0.61172737557305, "per_entity_metric": {"corporation": {"f1": 0.48186528497409326, "f1_ci": {}, "precision": 0.5081967213114754, "recall": 0.458128078817734}, "creative_work": {"f1": 0.5530864197530865, "f1_ci": {}, "precision": 0.5685279187817259, "recall": 0.5384615384615384}, "event": {"f1": 0.40490797546012275, "f1_ci": {}, "precision": 0.4248927038626609, "recall": 0.38671875}, "group": {"f1": 0.5560747663551402, "f1_ci": {}, "precision": 0.5920398009950248, "recall": 0.5242290748898678}, "location": {"f1": 0.6296296296296297, "f1_ci": {}, "precision": 0.6040609137055838, "recall": 0.6574585635359116}, "person": {"f1": 0.8683760683760683, "f1_ci": {}, "precision": 0.8881118881118881, "recall": 0.8494983277591973}, "product": {"f1": 0.654945054945055, "f1_ci": {}, "precision": 0.6962616822429907, "recall": 0.6182572614107884}}}, "2021.test": {"micro/f1": 0.6405933917734322, "micro/f1_ci": {"90": [0.631778937808505, 0.6496155272705774], "95": [0.6304400232899813, 0.6514114604724054]}, "micro/recall": 0.6591119333950046, "micro/precision": 0.6230870135548754, "macro/f1": 0.5944351160023098, "macro/f1_ci": {"90": [0.5847208708923466, 0.603808377698836], "95": [0.5828546488018534, 0.6052274662051181]}, "macro/recall": 0.6180335313677364, "macro/precision": 0.5741906773305591, "per_entity_metric": {"corporation": {"f1": 0.5161987041036716, "f1_ci": {"90": [0.49180141685517725, 0.5412822594794634], "95": [0.4872053907697826, 0.5464205205875101]}, "precision": 0.5021008403361344, "recall": 0.5311111111111111}, "creative_work": {"f1": 0.4572468162522741, "f1_ci": {"90": [0.4263245047046743, 0.48619332184333874], "95": [0.4203299955901526, 0.49227302083088703]}, "precision": 0.4106753812636166, "recall": 0.5157318741450069}, "event": {"f1": 0.4586907449209932, "f1_ci": {"90": [0.4353521868871257, 0.48032375354584356], "95": [0.4313319263983663, 0.4842758616893962]}, "precision": 0.4551971326164875, "recall": 0.462238398544131}, "group": {"f1": 0.5974197816738339, "f1_ci": {"90": [0.5762787240136122, 0.618982083736629], "95": [0.5733679043193478, 0.62334357181611]}, "precision": 0.6, "recall": 0.5948616600790514}, "location": {"f1": 0.6470211402946828, "f1_ci": {"90": [0.6202857142857143, 0.6734338120728754], "95": [0.613022658886491, 0.6770613077175826]}, "precision": 0.5976331360946746, "recall": 0.7053072625698324}, "person": {"f1": 0.8270704122582998, "f1_ci": {"90": [0.8161884707751226, 0.8380467499055994], "95": [0.8142470822597432, 0.8396568324466097]}, "precision": 0.8184115523465704, "recall": 0.8359144542772862}, "product": {"f1": 0.6573982125124131, "f1_ci": {"90": [0.6348520899144029, 0.6785714285714285], "95": [0.6316288989365912, 0.6825163014469338]}, "precision": 0.6353166986564299, "recall": 0.6810699588477366}}}, "2020.test": {"micro/f1": 0.6638023630504833, "micro/f1_ci": {"90": [0.6418572016184402, 0.683072873635275], "95": [0.6397231030341602, 0.6878762682463384]}, "micro/recall": 0.6414115204981837, "micro/precision": 0.6878130217028381, "macro/f1": 0.6240651348448572, "macro/f1_ci": {"90": [0.6000726487941666, 0.6450820854370489], "95": [0.5959923653749377, 0.6483570192589765]}, "macro/recall": 0.6088211190017436, "macro/precision": 0.6445261805050383, "per_entity_metric": {"corporation": {"f1": 0.5804749340369394, "f1_ci": {"90": [0.5186940057264247, 0.634421505376344], "95": [0.511232833957553, 0.6458452608142494]}, "precision": 0.5851063829787234, "recall": 0.5759162303664922}, "creative_work": {"f1": 0.5494505494505495, "f1_ci": {"90": [0.49384915088405895, 0.6042839513427749], "95": [0.48414288370363484, 0.6140688259109314]}, "precision": 0.5405405405405406, "recall": 0.5586592178770949}, "event": {"f1": 0.4990403071017275, "f1_ci": {"90": [0.4465795369678865, 0.545831502200961], "95": [0.43690366732838776, 0.5538938053097346]}, "precision": 0.5078125, "recall": 0.49056603773584906}, "group": {"f1": 0.5617977528089888, "f1_ci": {"90": [0.5054113082467208, 0.6175137833864248], "95": [0.49254370765167715, 0.6298707075842308]}, "precision": 0.672645739910314, "recall": 0.48231511254019294}, "location": {"f1": 0.6744868035190615, "f1_ci": {"90": [0.611976320582878, 0.7320256856279911], "95": [0.5962226896592779, 0.740745899102445]}, "precision": 0.6534090909090909, "recall": 0.696969696969697}, "person": {"f1": 0.847545219638243, "f1_ci": {"90": [0.8229540630381673, 0.8695689462301439], "95": [0.818900462962963, 0.874592017212771]}, "precision": 0.8707964601769912, "recall": 0.825503355704698}, "product": {"f1": 0.6556603773584906, "f1_ci": {"90": [0.6023278867102397, 0.7027096864306166], "95": [0.5898495068901802, 0.7139374819254937]}, "precision": 0.6813725490196079, "recall": 0.6318181818181818}}}, "2021.test (span detection)": {"micro/f1": 0.7790952514751335, "micro/f1_ci": {}, "micro/recall": 0.8016653174511391, "micro/precision": 0.7577612592916484, "macro/f1": 0.7790952514751335, "macro/f1_ci": {}, "macro/recall": 0.8016653174511391, "macro/precision": 0.7577612592916484}, "2020.test (span detection)": {"micro/f1": 0.7773301101262422, "micro/f1_ci": {}, "micro/recall": 0.7509081473793461, "micro/precision": 0.8056792873051225, "macro/f1": 0.7773301101262422, "macro/f1_ci": {}, "macro/recall": 0.7509081473793461, "macro/precision": 0.8056792873051225}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6638023630504833, "micro/f1_ci": {"90": [0.6418572016184402, 0.683072873635275], "95": [0.6397231030341602, 0.6878762682463384]}, "micro/recall": 0.6414115204981837, "micro/precision": 0.6878130217028381, "macro/f1": 0.6240651348448572, "macro/f1_ci": {"90": [0.6000726487941666, 0.6450820854370489], "95": [0.5959923653749377, 0.6483570192589765]}, "macro/recall": 0.6088211190017436, "macro/precision": 0.6445261805050383, "per_entity_metric": {"corporation": {"f1": 0.5804749340369394, "f1_ci": {"90": [0.5186940057264247, 0.634421505376344], "95": [0.511232833957553, 0.6458452608142494]}, "precision": 0.5851063829787234, "recall": 0.5759162303664922}, "creative_work": {"f1": 0.5494505494505495, "f1_ci": {"90": [0.49384915088405895, 0.6042839513427749], "95": [0.48414288370363484, 0.6140688259109314]}, "precision": 0.5405405405405406, "recall": 0.5586592178770949}, "event": {"f1": 0.4990403071017275, "f1_ci": {"90": [0.4465795369678865, 0.545831502200961], "95": [0.43690366732838776, 0.5538938053097346]}, "precision": 0.5078125, "recall": 0.49056603773584906}, "group": {"f1": 0.5617977528089888, "f1_ci": {"90": [0.5054113082467208, 0.6175137833864248], "95": [0.49254370765167715, 0.6298707075842308]}, "precision": 0.672645739910314, "recall": 0.48231511254019294}, "location": {"f1": 0.6744868035190615, "f1_ci": {"90": [0.611976320582878, 0.7320256856279911], "95": [0.5962226896592779, 0.740745899102445]}, "precision": 0.6534090909090909, "recall": 0.696969696969697}, "person": {"f1": 0.847545219638243, "f1_ci": {"90": [0.8229540630381673, 0.8695689462301439], "95": [0.818900462962963, 0.874592017212771]}, "precision": 0.8707964601769912, "recall": 0.825503355704698}, "product": {"f1": 0.6556603773584906, "f1_ci": {"90": [0.6023278867102397, 0.7027096864306166], "95": [0.5898495068901802, 0.7139374819254937]}, "precision": 0.6813725490196079, "recall": 0.6318181818181818}}}

eval/metric.test_2021.json

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+{"micro/f1": 0.6405933917734322, "micro/f1_ci": {"90": [0.631778937808505, 0.6496155272705774], "95": [0.6304400232899813, 0.6514114604724054]}, "micro/recall": 0.6591119333950046, "micro/precision": 0.6230870135548754, "macro/f1": 0.5944351160023098, "macro/f1_ci": {"90": [0.5847208708923466, 0.603808377698836], "95": [0.5828546488018534, 0.6052274662051181]}, "macro/recall": 0.6180335313677364, "macro/precision": 0.5741906773305591, "per_entity_metric": {"corporation": {"f1": 0.5161987041036716, "f1_ci": {"90": [0.49180141685517725, 0.5412822594794634], "95": [0.4872053907697826, 0.5464205205875101]}, "precision": 0.5021008403361344, "recall": 0.5311111111111111}, "creative_work": {"f1": 0.4572468162522741, "f1_ci": {"90": [0.4263245047046743, 0.48619332184333874], "95": [0.4203299955901526, 0.49227302083088703]}, "precision": 0.4106753812636166, "recall": 0.5157318741450069}, "event": {"f1": 0.4586907449209932, "f1_ci": {"90": [0.4353521868871257, 0.48032375354584356], "95": [0.4313319263983663, 0.4842758616893962]}, "precision": 0.4551971326164875, "recall": 0.462238398544131}, "group": {"f1": 0.5974197816738339, "f1_ci": {"90": [0.5762787240136122, 0.618982083736629], "95": [0.5733679043193478, 0.62334357181611]}, "precision": 0.6, "recall": 0.5948616600790514}, "location": {"f1": 0.6470211402946828, "f1_ci": {"90": [0.6202857142857143, 0.6734338120728754], "95": [0.613022658886491, 0.6770613077175826]}, "precision": 0.5976331360946746, "recall": 0.7053072625698324}, "person": {"f1": 0.8270704122582998, "f1_ci": {"90": [0.8161884707751226, 0.8380467499055994], "95": [0.8142470822597432, 0.8396568324466097]}, "precision": 0.8184115523465704, "recall": 0.8359144542772862}, "product": {"f1": 0.6573982125124131, "f1_ci": {"90": [0.6348520899144029, 0.6785714285714285], "95": [0.6316288989365912, 0.6825163014469338]}, "precision": 0.6353166986564299, "recall": 0.6810699588477366}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7773301101262422, "micro/f1_ci": {}, "micro/recall": 0.7509081473793461, "micro/precision": 0.8056792873051225, "macro/f1": 0.7773301101262422, "macro/f1_ci": {}, "macro/recall": 0.7509081473793461, "macro/precision": 0.8056792873051225}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7790952514751335, "micro/f1_ci": {}, "micro/recall": 0.8016653174511391, "micro/precision": 0.7577612592916484, "macro/f1": 0.7790952514751335, "macro/f1_ci": {}, "macro/recall": 0.8016653174511391, "macro/precision": 0.7577612592916484}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020.train", "model": "vinai/bertweet-base", "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_2020", "dataset_name": null, "local_dataset": null, "model": "vinai/bertweet-base", "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}