model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bert-base-tweetner7-random
+  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: 0.609117361784675
+    - name: Precision
+      type: precision
+      value: 0.6011938281337988
+    - name: Recall
+      type: recall
+      value: 0.6172525439407955
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.559165089199025
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5499368578582033
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5694430718770875
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7572194954913822
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7474929577464788
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7672024979761767
+  - 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: 0.6103825136612021
+    - name: Precision
+      type: precision
+      value: 0.6445470282746683
+    - name: Recall
+      type: recall
+      value: 0.5796574987026466
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5675359874657813
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6021803835272678
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5387624182505003
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7273224043715847
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7680323139065205
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.6907109496626881
+
+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-random
+
+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_random` 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): 0.609117361784675
+- Precision (micro): 0.6011938281337988
+- Recall (micro): 0.6172525439407955
+- F1 (macro): 0.559165089199025
+- Precision (macro): 0.5499368578582033
+- Recall (macro): 0.5694430718770875
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.46514047866805414
+- creative_work: 0.3904698874917273
+- event: 0.4183066361556064
+- group: 0.5614035087719299
+- location: 0.6389645776566757
+- person: 0.8044590643274854
+- product: 0.6354114713216957 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6000414265573856, 0.6190415373631918]
+    - 95%: [0.5981509067764902, 0.6206829089362571] 
+- F1 (macro): 
+    - 90%: [0.6000414265573856, 0.6190415373631918]
+    - 95%: [0.5981509067764902, 0.6206829089362571] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-random/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bert-base-tweetner7-random/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-random")
+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_random
+ - 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: 0.15
+ - max_grad_norm: 1
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-base-tweetner7-random/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 @@
-{"random.dev": {"micro/f1": 0.6192560175054704, "micro/f1_ci": {}, "micro/recall": 0.604378003203417, "micro/precision": 0.6348850252383623, "macro/f1": 0.5735999549689017, "macro/f1_ci": {}, "macro/recall": 0.5597621939973553, "macro/precision": 0.5895208320445692, "per_entity_metric": {"corporation": {"f1": 0.5524296675191815, "f1_ci": {}, "precision": 0.5454545454545454, "recall": 0.5595854922279793}, "creative_work": {"f1": 0.445141065830721, "f1_ci": {}, "precision": 0.45222929936305734, "recall": 0.4382716049382716}, "event": {"f1": 0.3439490445859873, "f1_ci": {}, "precision": 0.3584070796460177, "recall": 0.3306122448979592}, "group": {"f1": 0.5902668759811617, "f1_ci": {}, "precision": 0.6460481099656358, "recall": 0.5433526011560693}, "location": {"f1": 0.624203821656051, "f1_ci": {}, "precision": 0.6490066225165563, "recall": 0.6012269938650306}, "person": {"f1": 0.8240740740740741, "f1_ci": {}, "precision": 0.8135283363802559, "recall": 0.8348968105065666}, "product": {"f1": 0.6351351351351351, "f1_ci": {}, "precision": 0.6619718309859155, "recall": 0.6103896103896104}}}, "2021.test": {"micro/f1": 0.609117361784675, "micro/f1_ci": {"90": [0.6000414265573856, 0.6190415373631918], "95": [0.5981509067764902, 0.6206829089362571]}, "micro/recall": 0.6172525439407955, "micro/precision": 0.6011938281337988, "macro/f1": 0.559165089199025, "macro/f1_ci": {"90": [0.5497301707952231, 0.5689166903459428], "95": [0.5476856978181925, 0.5710003811206343]}, "macro/recall": 0.5694430718770875, "macro/precision": 0.5499368578582033, "per_entity_metric": {"corporation": {"f1": 0.46514047866805414, "f1_ci": {"90": [0.4422362753387115, 0.48909159792525936], "95": [0.43623003635525726, 0.49374912858221975]}, "precision": 0.43737769080234834, "recall": 0.49666666666666665}, "creative_work": {"f1": 0.3904698874917273, "f1_ci": {"90": [0.36092434680684116, 0.4211238997968856], "95": [0.35687967318425945, 0.42683661009613894]}, "precision": 0.3782051282051282, "recall": 0.40355677154582764}, "event": {"f1": 0.4183066361556064, "f1_ci": {"90": [0.39546956111373294, 0.4397802739202638], "95": [0.39134522763851565, 0.44413293286890765]}, "precision": 0.4208103130755064, "recall": 0.41583257506824384}, "group": {"f1": 0.5614035087719299, "f1_ci": {"90": [0.5405763591247462, 0.5834728306426968], "95": [0.5359571482288267, 0.5869609145475759]}, "precision": 0.5753803596127247, "recall": 0.5480895915678524}, "location": {"f1": 0.6389645776566757, "f1_ci": {"90": [0.6109104048765331, 0.6688442881364794], "95": [0.605244670105696, 0.6721568117825428]}, "precision": 0.6236702127659575, "recall": 0.6550279329608939}, "person": {"f1": 0.8044590643274854, "f1_ci": {"90": [0.7932438610200235, 0.8160130648695689], "95": [0.7909297225622194, 0.8184663212435235]}, "precision": 0.797463768115942, "recall": 0.8115781710914455}, "product": {"f1": 0.6354114713216957, "f1_ci": {"90": [0.6130714623048149, 0.6565474489604781], "95": [0.6085662982071728, 0.6597266285956008]}, "precision": 0.616650532429816, "recall": 0.6553497942386831}}}, "2020.test": {"micro/f1": 0.6103825136612021, "micro/f1_ci": {"90": [0.5886597809593919, 0.630754232709721], "95": [0.5853510478005706, 0.6344224019458733]}, "micro/recall": 0.5796574987026466, "micro/precision": 0.6445470282746683, "macro/f1": 0.5675359874657813, "macro/f1_ci": {"90": [0.5455416290059489, 0.5872544909762225], "95": [0.5421124912878689, 0.5910870158401571]}, "macro/recall": 0.5387624182505003, "macro/precision": 0.6021803835272678, "per_entity_metric": {"corporation": {"f1": 0.539440203562341, "f1_ci": {"90": [0.48223350253807107, 0.5925925925925926], "95": [0.47027027027027024, 0.6024160642570282]}, "precision": 0.5247524752475248, "recall": 0.5549738219895288}, "creative_work": {"f1": 0.42771084337349397, "f1_ci": {"90": [0.3701389008713657, 0.48001129943502824], "95": [0.35945076955513383, 0.4898017326588755]}, "precision": 0.46405228758169936, "recall": 0.39664804469273746}, "event": {"f1": 0.39147286821705424, "f1_ci": {"90": [0.3423689033075527, 0.4384615384615384], "95": [0.33401924920284043, 0.4479486677526936]}, "precision": 0.40239043824701193, "recall": 0.38113207547169814}, "group": {"f1": 0.5306859205776173, "f1_ci": {"90": [0.4740687545714476, 0.5835123669931646], "95": [0.4626316755585294, 0.5926018983807928]}, "precision": 0.6049382716049383, "recall": 0.47266881028938906}, "location": {"f1": 0.6266666666666666, "f1_ci": {"90": [0.5584730144800351, 0.689055929352397], "95": [0.5435147751150357, 0.7016515521450994]}, "precision": 0.6962962962962963, "recall": 0.5696969696969697}, "person": {"f1": 0.805944055944056, "f1_ci": {"90": [0.7759176252073368, 0.8316523801385778], "95": [0.7702132619352088, 0.8373703976596187]}, "precision": 0.8412408759124088, "recall": 0.7734899328859061}, "product": {"f1": 0.6508313539192399, "f1_ci": {"90": [0.6010349610090543, 0.6958744811793329], "95": [0.5892310901468917, 0.7061625360343002]}, "precision": 0.681592039800995, "recall": 0.6227272727272727}}}, "2021.test (span detection)": {"micro/f1": 0.7572194954913822, "micro/f1_ci": {}, "micro/recall": 0.7672024979761767, "micro/precision": 0.7474929577464788, "macro/f1": 0.7572194954913822, "macro/f1_ci": {}, "macro/recall": 0.7672024979761767, "macro/precision": 0.7474929577464788}, "2020.test (span detection)": {"micro/f1": 0.7273224043715847, "micro/f1_ci": {}, "micro/recall": 0.6907109496626881, "micro/precision": 0.7680323139065205, "macro/f1": 0.7273224043715847, "macro/f1_ci": {}, "macro/recall": 0.6907109496626881, "macro/precision": 0.7680323139065205}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6103825136612021, "micro/f1_ci": {"90": [0.5886597809593919, 0.630754232709721], "95": [0.5853510478005706, 0.6344224019458733]}, "micro/recall": 0.5796574987026466, "micro/precision": 0.6445470282746683, "macro/f1": 0.5675359874657813, "macro/f1_ci": {"90": [0.5455416290059489, 0.5872544909762225], "95": [0.5421124912878689, 0.5910870158401571]}, "macro/recall": 0.5387624182505003, "macro/precision": 0.6021803835272678, "per_entity_metric": {"corporation": {"f1": 0.539440203562341, "f1_ci": {"90": [0.48223350253807107, 0.5925925925925926], "95": [0.47027027027027024, 0.6024160642570282]}, "precision": 0.5247524752475248, "recall": 0.5549738219895288}, "creative_work": {"f1": 0.42771084337349397, "f1_ci": {"90": [0.3701389008713657, 0.48001129943502824], "95": [0.35945076955513383, 0.4898017326588755]}, "precision": 0.46405228758169936, "recall": 0.39664804469273746}, "event": {"f1": 0.39147286821705424, "f1_ci": {"90": [0.3423689033075527, 0.4384615384615384], "95": [0.33401924920284043, 0.4479486677526936]}, "precision": 0.40239043824701193, "recall": 0.38113207547169814}, "group": {"f1": 0.5306859205776173, "f1_ci": {"90": [0.4740687545714476, 0.5835123669931646], "95": [0.4626316755585294, 0.5926018983807928]}, "precision": 0.6049382716049383, "recall": 0.47266881028938906}, "location": {"f1": 0.6266666666666666, "f1_ci": {"90": [0.5584730144800351, 0.689055929352397], "95": [0.5435147751150357, 0.7016515521450994]}, "precision": 0.6962962962962963, "recall": 0.5696969696969697}, "person": {"f1": 0.805944055944056, "f1_ci": {"90": [0.7759176252073368, 0.8316523801385778], "95": [0.7702132619352088, 0.8373703976596187]}, "precision": 0.8412408759124088, "recall": 0.7734899328859061}, "product": {"f1": 0.6508313539192399, "f1_ci": {"90": [0.6010349610090543, 0.6958744811793329], "95": [0.5892310901468917, 0.7061625360343002]}, "precision": 0.681592039800995, "recall": 0.6227272727272727}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.609117361784675, "micro/f1_ci": {"90": [0.6000414265573856, 0.6190415373631918], "95": [0.5981509067764902, 0.6206829089362571]}, "micro/recall": 0.6172525439407955, "micro/precision": 0.6011938281337988, "macro/f1": 0.559165089199025, "macro/f1_ci": {"90": [0.5497301707952231, 0.5689166903459428], "95": [0.5476856978181925, 0.5710003811206343]}, "macro/recall": 0.5694430718770875, "macro/precision": 0.5499368578582033, "per_entity_metric": {"corporation": {"f1": 0.46514047866805414, "f1_ci": {"90": [0.4422362753387115, 0.48909159792525936], "95": [0.43623003635525726, 0.49374912858221975]}, "precision": 0.43737769080234834, "recall": 0.49666666666666665}, "creative_work": {"f1": 0.3904698874917273, "f1_ci": {"90": [0.36092434680684116, 0.4211238997968856], "95": [0.35687967318425945, 0.42683661009613894]}, "precision": 0.3782051282051282, "recall": 0.40355677154582764}, "event": {"f1": 0.4183066361556064, "f1_ci": {"90": [0.39546956111373294, 0.4397802739202638], "95": [0.39134522763851565, 0.44413293286890765]}, "precision": 0.4208103130755064, "recall": 0.41583257506824384}, "group": {"f1": 0.5614035087719299, "f1_ci": {"90": [0.5405763591247462, 0.5834728306426968], "95": [0.5359571482288267, 0.5869609145475759]}, "precision": 0.5753803596127247, "recall": 0.5480895915678524}, "location": {"f1": 0.6389645776566757, "f1_ci": {"90": [0.6109104048765331, 0.6688442881364794], "95": [0.605244670105696, 0.6721568117825428]}, "precision": 0.6236702127659575, "recall": 0.6550279329608939}, "person": {"f1": 0.8044590643274854, "f1_ci": {"90": [0.7932438610200235, 0.8160130648695689], "95": [0.7909297225622194, 0.8184663212435235]}, "precision": 0.797463768115942, "recall": 0.8115781710914455}, "product": {"f1": 0.6354114713216957, "f1_ci": {"90": [0.6130714623048149, 0.6565474489604781], "95": [0.6085662982071728, 0.6597266285956008]}, "precision": 0.616650532429816, "recall": 0.6553497942386831}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7273224043715847, "micro/f1_ci": {}, "micro/recall": 0.6907109496626881, "micro/precision": 0.7680323139065205, "macro/f1": 0.7273224043715847, "macro/f1_ci": {}, "macro/recall": 0.6907109496626881, "macro/precision": 0.7680323139065205}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7572194954913822, "micro/f1_ci": {}, "micro/recall": 0.7672024979761767, "micro/precision": 0.7474929577464788, "macro/f1": 0.7572194954913822, "macro/f1_ci": {}, "macro/recall": 0.7672024979761767, "macro/precision": 0.7474929577464788}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "random.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": 0.15, "max_grad_norm": 1}
+{"dataset": ["tner/tweetner7"], "dataset_split": "train_random", "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": 0.15, "max_grad_norm": 1}