model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bertweet-base-tweetner7-2021
+  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: 0.6308962917798349
+    - name: Precision
+      type: precision
+      value: 0.6058767167039285
+    - name: Recall
+      type: recall
+      value: 0.6580712303422757
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5735468406550763
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5503198173085064
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6012922054817469
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7788214245778822
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7538694663924668
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.8054816699433329
+  - 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: 0.6205787781350482
+    - name: Precision
+      type: precision
+      value: 0.6415512465373961
+    - name: Recall
+      type: recall
+      value: 0.6009340944473275
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5723158793505982
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5910271170769507
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5568451570610017
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7595141700404859
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7913385826771654
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7301504929942917
+
+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-tweetner7-2021
+
+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_2021` 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): 0.6308962917798349
+- Precision (micro): 0.6058767167039285
+- Recall (micro): 0.6580712303422757
+- F1 (macro): 0.5735468406550763
+- Precision (macro): 0.5503198173085064
+- Recall (macro): 0.6012922054817469
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.4565701559020044
+- creative_work: 0.4098984771573604
+- event: 0.4628410159924742
+- group: 0.593177511054959
+- location: 0.6333949476278496
+- person: 0.8279457768508863
+- product: 0.631 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6218627510838193, 0.6407164862470697]
+    - 95%: [0.6201627010426306, 0.6422908401462293] 
+- F1 (macro): 
+    - 90%: [0.6218627510838193, 0.6407164862470697]
+    - 95%: [0.6201627010426306, 0.6422908401462293] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-base-tweetner7-2021/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bertweet-base-tweetner7-2021/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-tweetner7-2021")
+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: vinai/bertweet-base
+ - crf: False
+ - 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: 0.3
+ - max_grad_norm: 1
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bertweet-base-tweetner7-2021/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 @@
-{"2021.dev": {"micro/f1": 0.6220703125000001, "micro/f1_ci": {}, "micro/recall": 0.637, "micro/precision": 0.607824427480916, "macro/f1": 0.5734483206270202, "macro/f1_ci": {}, "macro/recall": 0.5876986774163436, "macro/precision": 0.5610235583875315, "per_entity_metric": {"corporation": {"f1": 0.5217391304347826, "f1_ci": {}, "precision": 0.5142857142857142, "recall": 0.5294117647058824}, "creative_work": {"f1": 0.45333333333333337, "f1_ci": {}, "precision": 0.4473684210526316, "recall": 0.4594594594594595}, "event": {"f1": 0.38247011952191234, "f1_ci": {}, "precision": 0.4, "recall": 0.366412213740458}, "group": {"f1": 0.6214442013129103, "f1_ci": {}, "precision": 0.6173913043478261, "recall": 0.6255506607929515}, "location": {"f1": 0.6184210526315789, "f1_ci": {}, "precision": 0.5875, "recall": 0.6527777777777778}, "person": {"f1": 0.8006589785831961, "f1_ci": {}, "precision": 0.75, "recall": 0.8586572438162544}, "product": {"f1": 0.6160714285714285, "f1_ci": {}, "precision": 0.6106194690265486, "recall": 0.6216216216216216}}}, "2021.test": {"micro/f1": 0.6308962917798349, "micro/f1_ci": {"90": [0.6218627510838193, 0.6407164862470697], "95": [0.6201627010426306, 0.6422908401462293]}, "micro/recall": 0.6580712303422757, "micro/precision": 0.6058767167039285, "macro/f1": 0.5735468406550763, "macro/f1_ci": {"90": [0.5633518872919319, 0.5834141537051213], "95": [0.5619155878559741, 0.585359923196132]}, "macro/recall": 0.6012922054817469, "macro/precision": 0.5503198173085064, "per_entity_metric": {"corporation": {"f1": 0.4565701559020044, "f1_ci": {"90": [0.43122213480199306, 0.4842832655545205], "95": [0.422987517373063, 0.4892101769228394]}, "precision": 0.4575892857142857, "recall": 0.45555555555555555}, "creative_work": {"f1": 0.4098984771573604, "f1_ci": {"90": [0.3791530120969373, 0.4415152462146761], "95": [0.3716394651460033, 0.4462899138884782]}, "precision": 0.38224852071005916, "recall": 0.4418604651162791}, "event": {"f1": 0.4628410159924742, "f1_ci": {"90": [0.43764436774212423, 0.4844905070216916], "95": [0.4327500906125407, 0.49103704697586353]}, "precision": 0.4790652385589094, "recall": 0.44767970882620567}, "group": {"f1": 0.593177511054959, "f1_ci": {"90": [0.5717044673748471, 0.6144093322442311], "95": [0.5676831336909995, 0.6175847266352983]}, "precision": 0.5697815533980582, "recall": 0.6185770750988142}, "location": {"f1": 0.6333949476278496, "f1_ci": {"90": [0.6061374762309342, 0.6596729625322199], "95": [0.5999818181818181, 0.6642491807610421]}, "precision": 0.5667034178610805, "recall": 0.7178770949720671}, "person": {"f1": 0.8279457768508863, "f1_ci": {"90": [0.8177849742494966, 0.8392861816686192], "95": [0.8161339468587502, 0.8409499904355973]}, "precision": 0.7830374753451677, "recall": 0.8783185840707964}, "product": {"f1": 0.631, "f1_ci": {"90": [0.6078329186866498, 0.6538099694036692], "95": [0.602711796196466, 0.6565176505572475]}, "precision": 0.6138132295719845, "recall": 0.6491769547325102}}}, "2020.test": {"micro/f1": 0.6205787781350482, "micro/f1_ci": {"90": [0.5990600374135929, 0.641028413028413], "95": [0.5956429008859256, 0.645577807233236]}, "micro/recall": 0.6009340944473275, "micro/precision": 0.6415512465373961, "macro/f1": 0.5723158793505982, "macro/f1_ci": {"90": [0.5491541277371618, 0.593824224484208], "95": [0.5446730617743423, 0.5989182517444881]}, "macro/recall": 0.5568451570610017, "macro/precision": 0.5910271170769507, "per_entity_metric": {"corporation": {"f1": 0.49867374005305043, "f1_ci": {"90": [0.4362597402597403, 0.5552962052962053], "95": [0.4225177366931115, 0.5675940646528883]}, "precision": 0.5053763440860215, "recall": 0.49214659685863876}, "creative_work": {"f1": 0.4583333333333333, "f1_ci": {"90": [0.3903903903903904, 0.516321444502628], "95": [0.3776601932639084, 0.5297937192118228]}, "precision": 0.49044585987261147, "recall": 0.4301675977653631}, "event": {"f1": 0.43892339544513453, "f1_ci": {"90": [0.38343874372946507, 0.4931036128685116], "95": [0.3732780599111177, 0.5052203054609383]}, "precision": 0.48623853211009177, "recall": 0.4}, "group": {"f1": 0.5264957264957265, "f1_ci": {"90": [0.4739659822849217, 0.5779008444686745], "95": [0.4623267883150051, 0.5874770558415155]}, "precision": 0.5620437956204379, "recall": 0.49517684887459806}, "location": {"f1": 0.6358381502890174, "f1_ci": {"90": [0.5734652877656781, 0.6918347805270832], "95": [0.5622911498701775, 0.7034883720930232]}, "precision": 0.6077348066298343, "recall": 0.6666666666666666}, "person": {"f1": 0.81787521079258, "f1_ci": {"90": [0.7927999152425893, 0.8405389863292992], "95": [0.7873498023715415, 0.844675509305848]}, "precision": 0.8220338983050848, "recall": 0.8137583892617449}, "product": {"f1": 0.630071599045346, "f1_ci": {"90": [0.5727563482336753, 0.683606306263845], "95": [0.5630568382452805, 0.6948397887323944]}, "precision": 0.6633165829145728, "recall": 0.6}}}, "2021.test (span detection)": {"micro/f1": 0.7788214245778822, "micro/f1_ci": {}, "micro/recall": 0.8054816699433329, "micro/precision": 0.7538694663924668, "macro/f1": 0.7788214245778822, "macro/f1_ci": {}, "macro/recall": 0.8054816699433329, "macro/precision": 0.7538694663924668}, "2020.test (span detection)": {"micro/f1": 0.7595141700404859, "micro/f1_ci": {}, "micro/recall": 0.7301504929942917, "micro/precision": 0.7913385826771654, "macro/f1": 0.7595141700404859, "macro/f1_ci": {}, "macro/recall": 0.7301504929942917, "macro/precision": 0.7913385826771654}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6205787781350482, "micro/f1_ci": {"90": [0.5990600374135929, 0.641028413028413], "95": [0.5956429008859256, 0.645577807233236]}, "micro/recall": 0.6009340944473275, "micro/precision": 0.6415512465373961, "macro/f1": 0.5723158793505982, "macro/f1_ci": {"90": [0.5491541277371618, 0.593824224484208], "95": [0.5446730617743423, 0.5989182517444881]}, "macro/recall": 0.5568451570610017, "macro/precision": 0.5910271170769507, "per_entity_metric": {"corporation": {"f1": 0.49867374005305043, "f1_ci": {"90": [0.4362597402597403, 0.5552962052962053], "95": [0.4225177366931115, 0.5675940646528883]}, "precision": 0.5053763440860215, "recall": 0.49214659685863876}, "creative_work": {"f1": 0.4583333333333333, "f1_ci": {"90": [0.3903903903903904, 0.516321444502628], "95": [0.3776601932639084, 0.5297937192118228]}, "precision": 0.49044585987261147, "recall": 0.4301675977653631}, "event": {"f1": 0.43892339544513453, "f1_ci": {"90": [0.38343874372946507, 0.4931036128685116], "95": [0.3732780599111177, 0.5052203054609383]}, "precision": 0.48623853211009177, "recall": 0.4}, "group": {"f1": 0.5264957264957265, "f1_ci": {"90": [0.4739659822849217, 0.5779008444686745], "95": [0.4623267883150051, 0.5874770558415155]}, "precision": 0.5620437956204379, "recall": 0.49517684887459806}, "location": {"f1": 0.6358381502890174, "f1_ci": {"90": [0.5734652877656781, 0.6918347805270832], "95": [0.5622911498701775, 0.7034883720930232]}, "precision": 0.6077348066298343, "recall": 0.6666666666666666}, "person": {"f1": 0.81787521079258, "f1_ci": {"90": [0.7927999152425893, 0.8405389863292992], "95": [0.7873498023715415, 0.844675509305848]}, "precision": 0.8220338983050848, "recall": 0.8137583892617449}, "product": {"f1": 0.630071599045346, "f1_ci": {"90": [0.5727563482336753, 0.683606306263845], "95": [0.5630568382452805, 0.6948397887323944]}, "precision": 0.6633165829145728, "recall": 0.6}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6308962917798349, "micro/f1_ci": {"90": [0.6218627510838193, 0.6407164862470697], "95": [0.6201627010426306, 0.6422908401462293]}, "micro/recall": 0.6580712303422757, "micro/precision": 0.6058767167039285, "macro/f1": 0.5735468406550763, "macro/f1_ci": {"90": [0.5633518872919319, 0.5834141537051213], "95": [0.5619155878559741, 0.585359923196132]}, "macro/recall": 0.6012922054817469, "macro/precision": 0.5503198173085064, "per_entity_metric": {"corporation": {"f1": 0.4565701559020044, "f1_ci": {"90": [0.43122213480199306, 0.4842832655545205], "95": [0.422987517373063, 0.4892101769228394]}, "precision": 0.4575892857142857, "recall": 0.45555555555555555}, "creative_work": {"f1": 0.4098984771573604, "f1_ci": {"90": [0.3791530120969373, 0.4415152462146761], "95": [0.3716394651460033, 0.4462899138884782]}, "precision": 0.38224852071005916, "recall": 0.4418604651162791}, "event": {"f1": 0.4628410159924742, "f1_ci": {"90": [0.43764436774212423, 0.4844905070216916], "95": [0.4327500906125407, 0.49103704697586353]}, "precision": 0.4790652385589094, "recall": 0.44767970882620567}, "group": {"f1": 0.593177511054959, "f1_ci": {"90": [0.5717044673748471, 0.6144093322442311], "95": [0.5676831336909995, 0.6175847266352983]}, "precision": 0.5697815533980582, "recall": 0.6185770750988142}, "location": {"f1": 0.6333949476278496, "f1_ci": {"90": [0.6061374762309342, 0.6596729625322199], "95": [0.5999818181818181, 0.6642491807610421]}, "precision": 0.5667034178610805, "recall": 0.7178770949720671}, "person": {"f1": 0.8279457768508863, "f1_ci": {"90": [0.8177849742494966, 0.8392861816686192], "95": [0.8161339468587502, 0.8409499904355973]}, "precision": 0.7830374753451677, "recall": 0.8783185840707964}, "product": {"f1": 0.631, "f1_ci": {"90": [0.6078329186866498, 0.6538099694036692], "95": [0.602711796196466, 0.6565176505572475]}, "precision": 0.6138132295719845, "recall": 0.6491769547325102}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7595141700404859, "micro/f1_ci": {}, "micro/recall": 0.7301504929942917, "micro/precision": 0.7913385826771654, "macro/f1": 0.7595141700404859, "macro/f1_ci": {}, "macro/recall": 0.7301504929942917, "macro/precision": 0.7913385826771654}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7788214245778822, "micro/f1_ci": {}, "micro/recall": 0.8054816699433329, "micro/precision": 0.7538694663924668, "macro/f1": 0.7788214245778822, "macro/f1_ci": {}, "macro/recall": 0.8054816699433329, "macro/precision": 0.7538694663924668}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2021.train", "model": "vinai/bertweet-base", "crf": false, "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": 0.3, "max_grad_norm": 1}
+{"dataset": ["tner/tweetner7"], "dataset_split": "train_2021", "dataset_name": null, "local_dataset": null, "model": "vinai/bertweet-base", "crf": false, "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": 0.3, "max_grad_norm": 1}