model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-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: 0.6421184225480168
+    - name: Precision
+      type: precision
+      value: 0.6312849162011173
+    - name: Recall
+      type: recall
+      value: 0.6533302497687327
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5910653350307403
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5792905609274926
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.604510675992635
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7789270288701977
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7657838864677617
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7925292008789175
+  - 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: 0.642489851150203
+    - name: Precision
+      type: precision
+      value: 0.6713800904977375
+    - name: Recall
+      type: recall
+      value: 0.6159833938764919
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6023293888599316
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6319549874790182
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5783022171044098
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7480378890392421
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7816742081447964
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7171769590036325
+
+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
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-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): 0.6421184225480168
+- Precision (micro): 0.6312849162011173
+- Recall (micro): 0.6533302497687327
+- F1 (macro): 0.5910653350307403
+- Precision (macro): 0.5792905609274926
+- Recall (macro): 0.604510675992635
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.5075268817204301
+- creative_work: 0.4444444444444444
+- event: 0.4390243902439025
+- group: 0.5914552736982643
+- location: 0.6584415584415584
+- person: 0.8392439243924392
+- product: 0.6573208722741433 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6329963447257968, 0.6512071883878683]
+    - 95%: [0.6313662186691117, 0.6531901528182326] 
+- F1 (macro): 
+    - 90%: [0.6329963447257968, 0.6512071883878683]
+    - 95%: [0.6313662186691117, 0.6531901528182326] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-base-tweetner7-2020/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-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/roberta-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: roberta-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/roberta-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

@@ -1 +0,0 @@
-{"2020.dev": {"micro/f1": 0.63264192139738, "micro/f1_ci": {}, "micro/recall": 0.6055381400208987, "micro/precision": 0.6622857142857143, "macro/f1": 0.574634430106692, "macro/f1_ci": {}, "macro/recall": 0.5490536498812068, "macro/precision": 0.6046855072673193, "per_entity_metric": {"corporation": {"f1": 0.4741144414168937, "f1_ci": {}, "precision": 0.5304878048780488, "recall": 0.42857142857142855}, "creative_work": {"f1": 0.49738219895287955, "f1_ci": {}, "precision": 0.5459770114942529, "recall": 0.4567307692307692}, "event": {"f1": 0.39363817097415504, "f1_ci": {}, "precision": 0.4008097165991903, "recall": 0.38671875}, "group": {"f1": 0.5103448275862068, "f1_ci": {}, "precision": 0.5336538461538461, "recall": 0.4889867841409692}, "location": {"f1": 0.6284153005464481, "f1_ci": {}, "precision": 0.6216216216216216, "recall": 0.6353591160220995}, "person": {"f1": 0.8713550600343053, "f1_ci": {}, "precision": 0.8943661971830986, "recall": 0.8494983277591973}, "product": {"f1": 0.647191011235955, "f1_ci": {}, "precision": 0.7058823529411765, "recall": 0.5975103734439834}}}, "2021.test": {"micro/f1": 0.6421184225480168, "micro/f1_ci": {"90": [0.6329963447257968, 0.6512071883878683], "95": [0.6313662186691117, 0.6531901528182326]}, "micro/recall": 0.6533302497687327, "micro/precision": 0.6312849162011173, "macro/f1": 0.5910653350307403, "macro/f1_ci": {"90": [0.580811083611411, 0.6006051849025007], "95": [0.5786926369174525, 0.6019537723463372]}, "macro/recall": 0.604510675992635, "macro/precision": 0.5792905609274926, "per_entity_metric": {"corporation": {"f1": 0.5075268817204301, "f1_ci": {"90": [0.4824596104045469, 0.5321255741220724], "95": [0.4786678573496566, 0.5359074129334884]}, "precision": 0.49166666666666664, "recall": 0.5244444444444445}, "creative_work": {"f1": 0.4444444444444444, "f1_ci": {"90": [0.41206219991189397, 0.472399743395062], "95": [0.40429689645945094, 0.4781029004648966]}, "precision": 0.42105263157894735, "recall": 0.47058823529411764}, "event": {"f1": 0.4390243902439025, "f1_ci": {"90": [0.41618532179505363, 0.4613249997500841], "95": [0.4127254270752854, 0.46538424790791016]}, "precision": 0.4441340782122905, "recall": 0.4340309372156506}, "group": {"f1": 0.5914552736982643, "f1_ci": {"90": [0.5707880300178324, 0.6130815064249576], "95": [0.5670469135971191, 0.6169643378760042]}, "precision": 0.5994587280108254, "recall": 0.5836627140974967}, "location": {"f1": 0.6584415584415584, "f1_ci": {"90": [0.6291422043205595, 0.6852636263678726], "95": [0.6243413788002835, 0.6901146371455337]}, "precision": 0.6152912621359223, "recall": 0.7081005586592178}, "person": {"f1": 0.8392439243924392, "f1_ci": {"90": [0.8284389763971283, 0.8491030026023837], "95": [0.8267645014381084, 0.8509174642295299]}, "precision": 0.8199085473091804, "recall": 0.8595132743362832}, "product": {"f1": 0.6573208722741433, "f1_ci": {"90": [0.6353671840482944, 0.6793638106087693], "95": [0.6315494736842105, 0.682884859188648]}, "precision": 0.6635220125786163, "recall": 0.6512345679012346}}}, "2020.test": {"micro/f1": 0.642489851150203, "micro/f1_ci": {"90": [0.6231728409986848, 0.6615969581749049], "95": [0.6200368620037806, 0.665245816863449]}, "micro/recall": 0.6159833938764919, "micro/precision": 0.6713800904977375, "macro/f1": 0.6023293888599316, "macro/f1_ci": {"90": [0.5807455360504852, 0.6223504555748728], "95": [0.5765329255864473, 0.6263256800647661]}, "macro/recall": 0.5783022171044098, "macro/precision": 0.6319549874790182, "per_entity_metric": {"corporation": {"f1": 0.5858585858585857, "f1_ci": {"90": [0.5271310068649885, 0.6411705551086083], "95": [0.5164291386299996, 0.6514806378132117]}, "precision": 0.5658536585365853, "recall": 0.6073298429319371}, "creative_work": {"f1": 0.4894259818731118, "f1_ci": {"90": [0.4285714285714286, 0.5459332207429503], "95": [0.4135536723163841, 0.5538461538461538]}, "precision": 0.5328947368421053, "recall": 0.45251396648044695}, "event": {"f1": 0.4383561643835617, "f1_ci": {"90": [0.3850460933957987, 0.4915793621013133], "95": [0.37837565936157486, 0.500023020257827]}, "precision": 0.45528455284552843, "recall": 0.4226415094339623}, "group": {"f1": 0.5531135531135531, "f1_ci": {"90": [0.5047318665914491, 0.6014011350967873], "95": [0.49806744046427853, 0.6106442412597326]}, "precision": 0.6425531914893617, "recall": 0.4855305466237942}, "location": {"f1": 0.6563467492260061, "f1_ci": {"90": [0.5910379918588874, 0.7164217571934143], "95": [0.5790909090909092, 0.729731611834676]}, "precision": 0.6708860759493671, "recall": 0.6424242424242425}, "person": {"f1": 0.8200339558573854, "f1_ci": {"90": [0.7919374706548458, 0.8439227194913183], "95": [0.7872795232324625, 0.8492311695040038]}, "precision": 0.8298969072164949, "recall": 0.8104026845637584}, "product": {"f1": 0.673170731707317, "f1_ci": {"90": [0.617711019074841, 0.7227918554567322], "95": [0.6066718407764846, 0.7343802362600538]}, "precision": 0.7263157894736842, "recall": 0.6272727272727273}}}, "2021.test (span detection)": {"micro/f1": 0.7789270288701977, "micro/f1_ci": {}, "micro/recall": 0.7925292008789175, "micro/precision": 0.7657838864677617, "macro/f1": 0.7789270288701977, "macro/f1_ci": {}, "macro/recall": 0.7925292008789175, "macro/precision": 0.7657838864677617}, "2020.test (span detection)": {"micro/f1": 0.7480378890392421, "micro/f1_ci": {}, "micro/recall": 0.7171769590036325, "micro/precision": 0.7816742081447964, "macro/f1": 0.7480378890392421, "macro/f1_ci": {}, "macro/recall": 0.7171769590036325, "macro/precision": 0.7816742081447964}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.642489851150203, "micro/f1_ci": {"90": [0.6231728409986848, 0.6615969581749049], "95": [0.6200368620037806, 0.665245816863449]}, "micro/recall": 0.6159833938764919, "micro/precision": 0.6713800904977375, "macro/f1": 0.6023293888599316, "macro/f1_ci": {"90": [0.5807455360504852, 0.6223504555748728], "95": [0.5765329255864473, 0.6263256800647661]}, "macro/recall": 0.5783022171044098, "macro/precision": 0.6319549874790182, "per_entity_metric": {"corporation": {"f1": 0.5858585858585857, "f1_ci": {"90": [0.5271310068649885, 0.6411705551086083], "95": [0.5164291386299996, 0.6514806378132117]}, "precision": 0.5658536585365853, "recall": 0.6073298429319371}, "creative_work": {"f1": 0.4894259818731118, "f1_ci": {"90": [0.4285714285714286, 0.5459332207429503], "95": [0.4135536723163841, 0.5538461538461538]}, "precision": 0.5328947368421053, "recall": 0.45251396648044695}, "event": {"f1": 0.4383561643835617, "f1_ci": {"90": [0.3850460933957987, 0.4915793621013133], "95": [0.37837565936157486, 0.500023020257827]}, "precision": 0.45528455284552843, "recall": 0.4226415094339623}, "group": {"f1": 0.5531135531135531, "f1_ci": {"90": [0.5047318665914491, 0.6014011350967873], "95": [0.49806744046427853, 0.6106442412597326]}, "precision": 0.6425531914893617, "recall": 0.4855305466237942}, "location": {"f1": 0.6563467492260061, "f1_ci": {"90": [0.5910379918588874, 0.7164217571934143], "95": [0.5790909090909092, 0.729731611834676]}, "precision": 0.6708860759493671, "recall": 0.6424242424242425}, "person": {"f1": 0.8200339558573854, "f1_ci": {"90": [0.7919374706548458, 0.8439227194913183], "95": [0.7872795232324625, 0.8492311695040038]}, "precision": 0.8298969072164949, "recall": 0.8104026845637584}, "product": {"f1": 0.673170731707317, "f1_ci": {"90": [0.617711019074841, 0.7227918554567322], "95": [0.6066718407764846, 0.7343802362600538]}, "precision": 0.7263157894736842, "recall": 0.6272727272727273}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6421184225480168, "micro/f1_ci": {"90": [0.6329963447257968, 0.6512071883878683], "95": [0.6313662186691117, 0.6531901528182326]}, "micro/recall": 0.6533302497687327, "micro/precision": 0.6312849162011173, "macro/f1": 0.5910653350307403, "macro/f1_ci": {"90": [0.580811083611411, 0.6006051849025007], "95": [0.5786926369174525, 0.6019537723463372]}, "macro/recall": 0.604510675992635, "macro/precision": 0.5792905609274926, "per_entity_metric": {"corporation": {"f1": 0.5075268817204301, "f1_ci": {"90": [0.4824596104045469, 0.5321255741220724], "95": [0.4786678573496566, 0.5359074129334884]}, "precision": 0.49166666666666664, "recall": 0.5244444444444445}, "creative_work": {"f1": 0.4444444444444444, "f1_ci": {"90": [0.41206219991189397, 0.472399743395062], "95": [0.40429689645945094, 0.4781029004648966]}, "precision": 0.42105263157894735, "recall": 0.47058823529411764}, "event": {"f1": 0.4390243902439025, "f1_ci": {"90": [0.41618532179505363, 0.4613249997500841], "95": [0.4127254270752854, 0.46538424790791016]}, "precision": 0.4441340782122905, "recall": 0.4340309372156506}, "group": {"f1": 0.5914552736982643, "f1_ci": {"90": [0.5707880300178324, 0.6130815064249576], "95": [0.5670469135971191, 0.6169643378760042]}, "precision": 0.5994587280108254, "recall": 0.5836627140974967}, "location": {"f1": 0.6584415584415584, "f1_ci": {"90": [0.6291422043205595, 0.6852636263678726], "95": [0.6243413788002835, 0.6901146371455337]}, "precision": 0.6152912621359223, "recall": 0.7081005586592178}, "person": {"f1": 0.8392439243924392, "f1_ci": {"90": [0.8284389763971283, 0.8491030026023837], "95": [0.8267645014381084, 0.8509174642295299]}, "precision": 0.8199085473091804, "recall": 0.8595132743362832}, "product": {"f1": 0.6573208722741433, "f1_ci": {"90": [0.6353671840482944, 0.6793638106087693], "95": [0.6315494736842105, 0.682884859188648]}, "precision": 0.6635220125786163, "recall": 0.6512345679012346}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7480378890392421, "micro/f1_ci": {}, "micro/recall": 0.7171769590036325, "micro/precision": 0.7816742081447964, "macro/f1": 0.7480378890392421, "macro/f1_ci": {}, "macro/recall": 0.7171769590036325, "macro/precision": 0.7816742081447964}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7789270288701977, "micro/f1_ci": {}, "micro/recall": 0.7925292008789175, "micro/precision": 0.7657838864677617, "macro/f1": 0.7789270288701977, "macro/f1_ci": {}, "macro/recall": 0.7925292008789175, "macro/precision": 0.7657838864677617}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020.train", "model": "roberta-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": "roberta-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}