model update

README.md

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+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bert-large-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: 0.6238958623895862
+    - name: Precision
+      type: precision
+      value: 0.6271028037383177
+    - name: Recall
+      type: recall
+      value: 0.6207215541165587
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5754103658637805
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5760445653768616
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5751041088351385
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7648665930360984
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7689340813464236
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.760841910489187
+  - 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: 0.6154274575327208
+    - name: Precision
+      type: precision
+      value: 0.6640625
+    - name: Recall
+      type: recall
+      value: 0.5734302023871303
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5709159092071027
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6168953196783556
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5321784485961766
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.732943469785575
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7908653846153846
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.6829268292682927
+
+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-large-tweetner7-random
+
+This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-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): 0.6238958623895862
+- Precision (micro): 0.6271028037383177
+- Recall (micro): 0.6207215541165587
+- F1 (macro): 0.5754103658637805
+- Precision (macro): 0.5760445653768616
+- Recall (macro): 0.5751041088351385
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.49146005509641877
+- creative_work: 0.3972413793103448
+- event: 0.44788732394366193
+- group: 0.5767073573078192
+- location: 0.6721649484536083
+- person: 0.8116810183451891
+- product: 0.6307304785894207 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6141711573096726, 0.6329835528622229]
+    - 95%: [0.6127880547187768, 0.6345997209553179] 
+- F1 (macro): 
+    - 90%: [0.6141711573096726, 0.6329835528622229]
+    - 95%: [0.6127880547187768, 0.6345997209553179] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-large-tweetner7-random/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bert-large-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-large-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-large-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-large-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

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-{"random.dev": {"micro/f1": 0.619047619047619, "micro/f1_ci": {}, "micro/recall": 0.596903363587827, "micro/precision": 0.6428982173663025, "macro/f1": 0.5686540193943024, "macro/f1_ci": {}, "macro/recall": 0.547210195927133, "macro/precision": 0.5921601321577861, "per_entity_metric": {"corporation": {"f1": 0.5444743935309972, "f1_ci": {}, "precision": 0.5674157303370787, "recall": 0.5233160621761658}, "creative_work": {"f1": 0.4155844155844156, "f1_ci": {}, "precision": 0.4383561643835616, "recall": 0.3950617283950617}, "event": {"f1": 0.3593073593073593, "f1_ci": {}, "precision": 0.3824884792626728, "recall": 0.33877551020408164}, "group": {"f1": 0.6086956521739131, "f1_ci": {}, "precision": 0.632398753894081, "recall": 0.5867052023121387}, "location": {"f1": 0.5993485342019543, "f1_ci": {}, "precision": 0.6388888888888888, "recall": 0.5644171779141104}, "person": {"f1": 0.830945558739255, "f1_ci": {}, "precision": 0.8463035019455253, "recall": 0.8161350844277674}, "product": {"f1": 0.6222222222222221, "f1_ci": {}, "precision": 0.639269406392694, "recall": 0.6060606060606061}}}, "2021.test": {"micro/f1": 0.6238958623895862, "micro/f1_ci": {"90": [0.6141711573096726, 0.6329835528622229], "95": [0.6127880547187768, 0.6345997209553179]}, "micro/recall": 0.6207215541165587, "micro/precision": 0.6271028037383177, "macro/f1": 0.5754103658637805, "macro/f1_ci": {"90": [0.565187314674676, 0.5855711118073963], "95": [0.563829302210402, 0.5869723763958868]}, "macro/recall": 0.5751041088351385, "macro/precision": 0.5760445653768616, "per_entity_metric": {"corporation": {"f1": 0.49146005509641877, "f1_ci": {"90": [0.46653944279830356, 0.5156783797722426], "95": [0.4614505665499631, 0.5195247572390868]}, "precision": 0.48743169398907105, "recall": 0.4955555555555556}, "creative_work": {"f1": 0.3972413793103448, "f1_ci": {"90": [0.3643388832934659, 0.431475468975469], "95": [0.357765951797727, 0.4368833727344366]}, "precision": 0.40055632823365783, "recall": 0.39398084815321477}, "event": {"f1": 0.44788732394366193, "f1_ci": {"90": [0.4252921892767356, 0.4712090747178809], "95": [0.42052975286951233, 0.4741356529398811]}, "precision": 0.4626576139670223, "recall": 0.4340309372156506}, "group": {"f1": 0.5767073573078192, "f1_ci": {"90": [0.5553909855557407, 0.5986221015634983], "95": [0.5507048431429983, 0.6039963050868477]}, "precision": 0.5776602775941837, "recall": 0.5757575757575758}, "location": {"f1": 0.6721649484536083, "f1_ci": {"90": [0.6437046042939083, 0.6989743577005144], "95": [0.6382294156836619, 0.7034515355421547]}, "precision": 0.6617050067658998, "recall": 0.6829608938547486}, "person": {"f1": 0.8116810183451891, "f1_ci": {"90": [0.800425398613887, 0.8226651182198794], "95": [0.7974815277796216, 0.8242620017446516]}, "precision": 0.8243346007604563, "recall": 0.799410029498525}, "product": {"f1": 0.6307304785894207, "f1_ci": {"90": [0.6096523222472238, 0.6528996533628258], "95": [0.6046932305919911, 0.6557772793770326]}, "precision": 0.6179664363277394, "recall": 0.6440329218106996}}}, "2020.test": {"micro/f1": 0.6154274575327208, "micro/f1_ci": {"90": [0.5947241888019572, 0.6356762437073024], "95": [0.5903024143830322, 0.6396733847327379]}, "micro/recall": 0.5734302023871303, "micro/precision": 0.6640625, "macro/f1": 0.5709159092071027, "macro/f1_ci": {"90": [0.5485436010515674, 0.5911412312900881], "95": [0.54348814668388, 0.5952871224560826]}, "macro/recall": 0.5321784485961766, "macro/precision": 0.6168953196783556, "per_entity_metric": {"corporation": {"f1": 0.5633802816901409, "f1_ci": {"90": [0.5108901703453305, 0.6114260304694146], "95": [0.5013283378746595, 0.6201379487863938]}, "precision": 0.6097560975609756, "recall": 0.5235602094240838}, "creative_work": {"f1": 0.4281150159744409, "f1_ci": {"90": [0.3629106221339231, 0.4872107567229519], "95": [0.35123805851861367, 0.49881329442767974]}, "precision": 0.5, "recall": 0.3743016759776536}, "event": {"f1": 0.4268774703557312, "f1_ci": {"90": [0.3761945562305827, 0.47865295050923795], "95": [0.36749324466274025, 0.48812845288166423]}, "precision": 0.44813278008298757, "recall": 0.4075471698113208}, "group": {"f1": 0.5335689045936396, "f1_ci": {"90": [0.4815280555531712, 0.5886398331255172], "95": [0.47204810764639515, 0.5993434343434344]}, "precision": 0.592156862745098, "recall": 0.4855305466237942}, "location": {"f1": 0.6198083067092652, "f1_ci": {"90": [0.5512898975732075, 0.6781922675026124], "95": [0.534796626984127, 0.6899004797898276]}, "precision": 0.6554054054054054, "recall": 0.5878787878787879}, "person": {"f1": 0.810375670840787, "f1_ci": {"90": [0.7806688121728075, 0.8346625323604978], "95": [0.7761355449156406, 0.8400027100271004]}, "precision": 0.867816091954023, "recall": 0.7600671140939598}, "product": {"f1": 0.6142857142857142, "f1_ci": {"90": [0.5632209365970393, 0.6650651474676988], "95": [0.5506497316430371, 0.6756231123983224]}, "precision": 0.645, "recall": 0.5863636363636363}}}, "2021.test (span detection)": {"micro/f1": 0.7648665930360984, "micro/f1_ci": {}, "micro/recall": 0.760841910489187, "micro/precision": 0.7689340813464236, "macro/f1": 0.7648665930360984, "macro/f1_ci": {}, "macro/recall": 0.760841910489187, "macro/precision": 0.7689340813464236}, "2020.test (span detection)": {"micro/f1": 0.732943469785575, "micro/f1_ci": {}, "micro/recall": 0.6829268292682927, "micro/precision": 0.7908653846153846, "macro/f1": 0.732943469785575, "macro/f1_ci": {}, "macro/recall": 0.6829268292682927, "macro/precision": 0.7908653846153846}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6154274575327208, "micro/f1_ci": {"90": [0.5947241888019572, 0.6356762437073024], "95": [0.5903024143830322, 0.6396733847327379]}, "micro/recall": 0.5734302023871303, "micro/precision": 0.6640625, "macro/f1": 0.5709159092071027, "macro/f1_ci": {"90": [0.5485436010515674, 0.5911412312900881], "95": [0.54348814668388, 0.5952871224560826]}, "macro/recall": 0.5321784485961766, "macro/precision": 0.6168953196783556, "per_entity_metric": {"corporation": {"f1": 0.5633802816901409, "f1_ci": {"90": [0.5108901703453305, 0.6114260304694146], "95": [0.5013283378746595, 0.6201379487863938]}, "precision": 0.6097560975609756, "recall": 0.5235602094240838}, "creative_work": {"f1": 0.4281150159744409, "f1_ci": {"90": [0.3629106221339231, 0.4872107567229519], "95": [0.35123805851861367, 0.49881329442767974]}, "precision": 0.5, "recall": 0.3743016759776536}, "event": {"f1": 0.4268774703557312, "f1_ci": {"90": [0.3761945562305827, 0.47865295050923795], "95": [0.36749324466274025, 0.48812845288166423]}, "precision": 0.44813278008298757, "recall": 0.4075471698113208}, "group": {"f1": 0.5335689045936396, "f1_ci": {"90": [0.4815280555531712, 0.5886398331255172], "95": [0.47204810764639515, 0.5993434343434344]}, "precision": 0.592156862745098, "recall": 0.4855305466237942}, "location": {"f1": 0.6198083067092652, "f1_ci": {"90": [0.5512898975732075, 0.6781922675026124], "95": [0.534796626984127, 0.6899004797898276]}, "precision": 0.6554054054054054, "recall": 0.5878787878787879}, "person": {"f1": 0.810375670840787, "f1_ci": {"90": [0.7806688121728075, 0.8346625323604978], "95": [0.7761355449156406, 0.8400027100271004]}, "precision": 0.867816091954023, "recall": 0.7600671140939598}, "product": {"f1": 0.6142857142857142, "f1_ci": {"90": [0.5632209365970393, 0.6650651474676988], "95": [0.5506497316430371, 0.6756231123983224]}, "precision": 0.645, "recall": 0.5863636363636363}}}

eval/metric.test_2021.json

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+{"micro/f1": 0.6238958623895862, "micro/f1_ci": {"90": [0.6141711573096726, 0.6329835528622229], "95": [0.6127880547187768, 0.6345997209553179]}, "micro/recall": 0.6207215541165587, "micro/precision": 0.6271028037383177, "macro/f1": 0.5754103658637805, "macro/f1_ci": {"90": [0.565187314674676, 0.5855711118073963], "95": [0.563829302210402, 0.5869723763958868]}, "macro/recall": 0.5751041088351385, "macro/precision": 0.5760445653768616, "per_entity_metric": {"corporation": {"f1": 0.49146005509641877, "f1_ci": {"90": [0.46653944279830356, 0.5156783797722426], "95": [0.4614505665499631, 0.5195247572390868]}, "precision": 0.48743169398907105, "recall": 0.4955555555555556}, "creative_work": {"f1": 0.3972413793103448, "f1_ci": {"90": [0.3643388832934659, 0.431475468975469], "95": [0.357765951797727, 0.4368833727344366]}, "precision": 0.40055632823365783, "recall": 0.39398084815321477}, "event": {"f1": 0.44788732394366193, "f1_ci": {"90": [0.4252921892767356, 0.4712090747178809], "95": [0.42052975286951233, 0.4741356529398811]}, "precision": 0.4626576139670223, "recall": 0.4340309372156506}, "group": {"f1": 0.5767073573078192, "f1_ci": {"90": [0.5553909855557407, 0.5986221015634983], "95": [0.5507048431429983, 0.6039963050868477]}, "precision": 0.5776602775941837, "recall": 0.5757575757575758}, "location": {"f1": 0.6721649484536083, "f1_ci": {"90": [0.6437046042939083, 0.6989743577005144], "95": [0.6382294156836619, 0.7034515355421547]}, "precision": 0.6617050067658998, "recall": 0.6829608938547486}, "person": {"f1": 0.8116810183451891, "f1_ci": {"90": [0.800425398613887, 0.8226651182198794], "95": [0.7974815277796216, 0.8242620017446516]}, "precision": 0.8243346007604563, "recall": 0.799410029498525}, "product": {"f1": 0.6307304785894207, "f1_ci": {"90": [0.6096523222472238, 0.6528996533628258], "95": [0.6046932305919911, 0.6557772793770326]}, "precision": 0.6179664363277394, "recall": 0.6440329218106996}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.732943469785575, "micro/f1_ci": {}, "micro/recall": 0.6829268292682927, "micro/precision": 0.7908653846153846, "macro/f1": 0.732943469785575, "macro/f1_ci": {}, "macro/recall": 0.6829268292682927, "macro/precision": 0.7908653846153846}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7648665930360984, "micro/f1_ci": {}, "micro/recall": 0.760841910489187, "micro/precision": 0.7689340813464236, "macro/f1": 0.7648665930360984, "macro/f1_ci": {}, "macro/recall": 0.760841910489187, "macro/precision": 0.7689340813464236}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "random.train", "model": "bert-large-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-large-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}