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model update

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README.md ADDED
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+ ---
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+ datasets:
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+ - tner/tweetner7
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+ metrics:
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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: tner/tweetner7/test_2021
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+ type: tner/tweetner7/test_2021
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+ args: tner/tweetner7/test_2021
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.6447001005249637
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+ - name: Precision
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+ type: precision
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+ value: 0.6234607906675308
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+ - name: Recall
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+ type: recall
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+ value: 0.6674375578168362
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.5982200308213212
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+ - name: Precision (macro)
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+ type: precision_macro
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+ value: 0.576608821080324
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+ - name: Recall (macro)
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+ type: recall_macro
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+ value: 0.622268182336741
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+ - name: F1 (entity span)
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+ type: f1_entity_span
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+ value: 0.7793353811784417
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+ - name: Precision (entity span)
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+ type: precision_entity_span
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+ value: 0.7536184921149276
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+ - name: Recall (entity span)
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+ type: recall_entity_span
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+ value: 0.8068694344859488
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: tner/tweetner7/test_2020
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+ type: tner/tweetner7/test_2020
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+ args: tner/tweetner7/test_2020
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.6582010582010582
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+ - name: Precision
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+ type: precision
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+ value: 0.671343766864544
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+ - name: Recall
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+ type: recall
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+ value: 0.6455630513751947
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.619090119256277
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+ - name: Precision (macro)
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+ type: precision_macro
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+ value: 0.6309214005692869
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+ - name: Recall (macro)
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+ type: recall_macro
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+ value: 0.6088158080350003
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+ - name: F1 (entity span)
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+ type: f1_entity_span
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+ value: 0.7647525800476317
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+ - name: Precision (entity span)
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+ type: precision_entity_span
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+ value: 0.7802375809935205
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+ - name: Recall (entity span)
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+ type: recall_entity_span
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+ value: 0.7498702646600934
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+
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+ pipeline_tag: token-classification
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+ widget:
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+ - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
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+ example_title: "NER Example 1"
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+ ---
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+ # tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat
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+
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+ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the
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+ [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
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+ Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
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+ for more detail). It achieves the following results on the test set of 2021:
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+ - F1 (micro): 0.6447001005249637
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+ - Precision (micro): 0.6234607906675308
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+ - Recall (micro): 0.6674375578168362
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+ - F1 (macro): 0.5982200308213212
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+ - Precision (macro): 0.576608821080324
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+ - Recall (macro): 0.622268182336741
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+
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+
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+
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+ The per-entity breakdown of the F1 score on the test set are below:
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+ - corporation: 0.5048128342245989
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+ - creative_work: 0.45297029702970293
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+ - event: 0.46761313220940554
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+ - group: 0.6009661835748793
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+ - location: 0.6592252133946159
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+ - person: 0.8302430243024302
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+ - product: 0.6717095310136157
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+
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+ For F1 scores, the confidence interval is obtained by bootstrap as below:
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+ - F1 (micro):
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+ - 90%: [0.6358921767926183, 0.6542958612061787]
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+ - 95%: [0.6341987223616053, 0.6560992650244356]
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+ - F1 (macro):
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+ - 90%: [0.6358921767926183, 0.6542958612061787]
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+ - 95%: [0.6341987223616053, 0.6560992650244356]
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+
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+ Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat/raw/main/eval/metric.json)
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+ and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat/raw/main/eval/metric_span.json).
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+
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+ ### Usage
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+ This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip
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+ ```shell
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+ pip install tner
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+ ```
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+ and activate model as below.
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+ ```python
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+ from tner import TransformersNER
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+ model = TransformersNER("tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat")
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+ model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
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+ ```
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+ It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - dataset: ['tner/tweetner7']
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+ - dataset_split: train_all
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+ - dataset_name: None
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+ - local_dataset: None
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+ - model: cardiffnlp/twitter-roberta-base-dec2021
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+ - crf: True
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+ - max_length: 128
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+ - epoch: 30
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+ - batch_size: 32
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+ - lr: 1e-05
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+ - random_seed: 0
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+ - gradient_accumulation_steps: 1
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+ - weight_decay: 1e-07
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+ - lr_warmup_step_ratio: 0.3
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+ - max_grad_norm: 1
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+
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+ The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020-2021-concat/raw/main/trainer_config.json).
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+
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+ ### Reference
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+ If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
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+
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+ ```
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+
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+ @inproceedings{ushio-camacho-collados-2021-ner,
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+ title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
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+ author = "Ushio, Asahi and
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+ Camacho-Collados, Jose",
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+ booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
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+ month = apr,
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+ year = "2021",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2021.eacl-demos.7",
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+ doi = "10.18653/v1/2021.eacl-demos.7",
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+ pages = "53--62",
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+ 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.",
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+ }
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+
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+ ```
eval/metric.test_2020.json ADDED
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+ {"micro/f1": 0.6582010582010582, "micro/f1_ci": {"90": [0.6379167890637253, 0.6767511315048438], "95": [0.6339207548025675, 0.6807947362204927]}, "micro/recall": 0.6455630513751947, "micro/precision": 0.671343766864544, "macro/f1": 0.619090119256277, "macro/f1_ci": {"90": [0.5972479198841049, 0.6386559386417274], "95": [0.5937401018540227, 0.6428463929597004]}, "macro/recall": 0.6088158080350003, "macro/precision": 0.6309214005692869, "per_entity_metric": {"corporation": {"f1": 0.5796344647519582, "f1_ci": {"90": [0.5234079697610352, 0.6277959240647681], "95": [0.5141152231379257, 0.6394375]}, "precision": 0.578125, "recall": 0.581151832460733}, "creative_work": {"f1": 0.553072625698324, "f1_ci": {"90": [0.49853196304809205, 0.6028253100693715], "95": [0.4876406133927624, 0.6114264896373057]}, "precision": 0.553072625698324, "recall": 0.553072625698324}, "event": {"f1": 0.444022770398482, "f1_ci": {"90": [0.3922253196193406, 0.4971993370807634], "95": [0.38228807543114435, 0.5075202210070632]}, "precision": 0.44656488549618323, "recall": 0.44150943396226416}, "group": {"f1": 0.5749128919860629, "f1_ci": {"90": [0.5205170817406994, 0.625659134872618], "95": [0.5104305259005177, 0.6358512874408828]}, "precision": 0.6273764258555133, "recall": 0.5305466237942122}, "location": {"f1": 0.6646706586826348, "f1_ci": {"90": [0.5974730765917118, 0.7278115556520639], "95": [0.5810510954741723, 0.7381043956043957]}, "precision": 0.6568047337278107, "recall": 0.6727272727272727}, "person": {"f1": 0.844331641285956, "f1_ci": {"90": [0.8183284457478005, 0.8676071424722781], "95": [0.8145682012390513, 0.8709331756339546]}, "precision": 0.8515358361774744, "recall": 0.837248322147651}, "product": {"f1": 0.6729857819905214, "f1_ci": {"90": [0.6212986836419078, 0.7170731707317073], "95": [0.6111074847693646, 0.725641560972215]}, "precision": 0.7029702970297029, "recall": 0.6454545454545455}}}
eval/{metric.json → metric.test_2021.json} RENAMED
@@ -1 +1 @@
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- {"2021.dev": {"micro/f1": 0.642156862745098, "micro/f1_ci": {}, "micro/recall": 0.655, "micro/precision": 0.6298076923076923, "macro/f1": 0.6004942384479613, "macro/f1_ci": {}, "macro/recall": 0.6146549344794797, "macro/precision": 0.5895253900625403, "per_entity_metric": {"corporation": {"f1": 0.5970149253731343, "f1_ci": {}, "precision": 0.6060606060606061, "recall": 0.5882352941176471}, "creative_work": {"f1": 0.42774566473988446, "f1_ci": {}, "precision": 0.37373737373737376, "recall": 0.5}, "event": {"f1": 0.38345864661654133, "f1_ci": {}, "precision": 0.37777777777777777, "recall": 0.3893129770992366}, "group": {"f1": 0.6458797327394209, "f1_ci": {}, "precision": 0.6531531531531531, "recall": 0.6387665198237885}, "location": {"f1": 0.6577181208053691, "f1_ci": {}, "precision": 0.6363636363636364, "recall": 0.6805555555555556}, "person": {"f1": 0.821917808219178, "f1_ci": {}, "precision": 0.7973421926910299, "recall": 0.8480565371024735}, "product": {"f1": 0.6697247706422017, "f1_ci": {}, "precision": 0.6822429906542056, "recall": 0.6576576576576577}}}, "2021.test": {"micro/f1": 0.6447001005249637, "micro/f1_ci": {"90": [0.6358921767926183, 0.6542958612061787], "95": [0.6341987223616053, 0.6560992650244356]}, "micro/recall": 0.6674375578168362, "micro/precision": 0.6234607906675308, "macro/f1": 0.5982200308213212, "macro/f1_ci": {"90": [0.5881550153814866, 0.6085554142266025], "95": [0.5868087805464741, 0.6101643811579637]}, "macro/recall": 0.622268182336741, "macro/precision": 0.576608821080324, "per_entity_metric": {"corporation": {"f1": 0.5048128342245989, "f1_ci": {"90": [0.479765110450545, 0.5306144595657036], "95": [0.47517387506462216, 0.5351675634581814]}, "precision": 0.4865979381443299, "recall": 0.5244444444444445}, "creative_work": {"f1": 0.45297029702970293, "f1_ci": {"90": [0.42319336176888755, 0.48488758755117906], "95": [0.4162047502047502, 0.4893898449722657]}, "precision": 0.4135593220338983, "recall": 0.5006839945280438}, "event": {"f1": 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"recall": 0.6851851851851852}}}, "2020.test": {"micro/f1": 0.6582010582010582, "micro/f1_ci": {"90": [0.6379167890637253, 0.6767511315048438], "95": [0.6339207548025675, 0.6807947362204927]}, "micro/recall": 0.6455630513751947, "micro/precision": 0.671343766864544, "macro/f1": 0.619090119256277, "macro/f1_ci": {"90": [0.5972479198841049, 0.6386559386417274], "95": [0.5937401018540227, 0.6428463929597004]}, "macro/recall": 0.6088158080350003, "macro/precision": 0.6309214005692869, "per_entity_metric": {"corporation": {"f1": 0.5796344647519582, "f1_ci": {"90": [0.5234079697610352, 0.6277959240647681], "95": [0.5141152231379257, 0.6394375]}, "precision": 0.578125, "recall": 0.581151832460733}, "creative_work": {"f1": 0.553072625698324, "f1_ci": {"90": [0.49853196304809205, 0.6028253100693715], "95": [0.4876406133927624, 0.6114264896373057]}, "precision": 0.553072625698324, "recall": 0.553072625698324}, "event": {"f1": 0.444022770398482, "f1_ci": {"90": [0.3922253196193406, 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eval/metric_span.test_2020.json ADDED
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+ {"micro/f1": 0.7647525800476317, "micro/f1_ci": {}, "micro/recall": 0.7498702646600934, "micro/precision": 0.7802375809935205, "macro/f1": 0.7647525800476317, "macro/f1_ci": {}, "macro/recall": 0.7498702646600934, "macro/precision": 0.7802375809935205}
eval/metric_span.test_2021.json ADDED
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+ {"micro/f1": 0.7793353811784417, "micro/f1_ci": {}, "micro/recall": 0.8068694344859488, "micro/precision": 0.7536184921149276, "macro/f1": 0.7793353811784417, "macro/f1_ci": {}, "macro/recall": 0.8068694344859488, "macro/precision": 0.7536184921149276}
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trainer_config.json CHANGED
@@ -1 +1 @@
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- {"data_split": "2020_2021.train", "model": "cardiffnlp/twitter-roberta-base-dec2021", "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": 0.3, "max_grad_norm": 1}
 
1
+ {"dataset": ["tner/tweetner7"], "dataset_split": "train_all", "dataset_name": null, "local_dataset": null, "model": "cardiffnlp/twitter-roberta-base-dec2021", "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": 0.3, "max_grad_norm": 1}