model update

README.md

@@ -0,0 +1,150 @@
+---
+datasets:
+- tner/ontonotes5
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/deberta-v3-large-ontonotes5
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: tner/ontonotes5
+      type: tner/ontonotes5
+      args: tner/ontonotes5
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9069623608411381
+    - name: Precision
+      type: precision
+      value: 0.902100360312857
+    - name: Recall
+      type: recall
+      value: 0.9118770542773386
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.834586960779896
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.8237351069457466
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.8475169311172334
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.9267538434352359
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.9217857456718517
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.9317757839566492
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/deberta-v3-large-ontonotes5
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
+[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset.
+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:
+- F1 (micro): 0.9069623608411381
+- Precision (micro): 0.902100360312857
+- Recall (micro): 0.9118770542773386
+- F1 (macro): 0.834586960779896
+- Precision (macro): 0.8237351069457466
+- Recall (macro): 0.8475169311172334
+
+The per-entity breakdown of the F1 score on the test set are below:
+- cardinal_number: 0.853475935828877
+- date: 0.8815545959284392
+- event: 0.8030303030303031
+- facility: 0.7896678966789669
+- geopolitical_area: 0.9650033867690223
+- group: 0.9337209302325581
+- language: 0.8372093023255814
+- law: 0.6756756756756757
+- location: 0.7624020887728459
+- money: 0.8818897637795275
+- ordinal_number: 0.8635235732009926
+- organization: 0.914952751528627
+- percent: 0.9
+- person: 0.9609866599546942
+- product: 0.7901234567901234
+- quantity: 0.8161434977578474
+- time: 0.674364896073903
+- work_of_art: 0.7188405797101449 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.9019409960743083, 0.911751130722053]
+    - 95%: [0.9010822890967028, 0.9125611412371442] 
+- F1 (macro): 
+    - 90%: [0.9019409960743083, 0.911751130722053]
+    - 95%: [0.9010822890967028, 0.9125611412371442] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-ontonotes5/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-ontonotes5/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/deberta-v3-large-ontonotes5")
+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/ontonotes5']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: microsoft/deberta-v3-large
+ - crf: True
+ - max_length: 128
+ - epoch: 15
+ - batch_size: 16
+ - lr: 1e-05
+ - random_seed: 42
+ - gradient_accumulation_steps: 4
+ - weight_decay: 1e-07
+ - lr_warmup_step_ratio: 0.1
+ - max_grad_norm: 10.0
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-ontonotes5/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.",
+}
+
+```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "tner_ckpt/ontonotes5_deberta_v3_large/best_model",
+  "_name_or_path": "tner_ckpt/ontonotes5_deberta_v3_large/model_ayzfwn/epoch_5",
   "architectures": [
     "DebertaV2ForTokenClassification"
   ],

eval/metric.json

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+{"micro/f1": 0.9069623608411381, "micro/f1_ci": {"90": [0.9019409960743083, 0.911751130722053], "95": [0.9010822890967028, 0.9125611412371442]}, "micro/recall": 0.9118770542773386, "micro/precision": 0.902100360312857, "macro/f1": 0.834586960779896, "macro/f1_ci": {"90": [0.8209804467128787, 0.8467249999163714], "95": [0.8183331796999228, 0.8488775156564468]}, "macro/recall": 0.8475169311172334, "macro/precision": 0.8237351069457466, "per_entity_metric": {"cardinal_number": {"f1": 0.853475935828877, "f1_ci": {"90": [0.8330497989081731, 0.8718031833104767], "95": [0.8292140789883788, 0.875]}, "precision": 0.853475935828877, "recall": 0.853475935828877}, "date": {"f1": 0.8815545959284392, "f1_ci": {"90": [0.8680905896697763, 0.8941108258988675], "95": [0.8660710010497302, 0.8963422156536515]}, "precision": 0.8713414634146341, "recall": 0.8920099875156055}, "event": {"f1": 0.8030303030303031, "f1_ci": {"90": [0.7258064516129032, 0.8676990493166965], "95": [0.7121147919876734, 0.8799999999999999]}, "precision": 0.7681159420289855, "recall": 0.8412698412698413}, "facility": {"f1": 0.7896678966789669, "f1_ci": {"90": [0.733052273663965, 0.8432094752717251], "95": [0.725635331440975, 0.8512490002665958]}, "precision": 0.7867647058823529, "recall": 0.7925925925925926}, "geopolitical_area": {"f1": 0.9650033867690223, "f1_ci": {"90": [0.959691207666892, 0.9698219691594531], "95": [0.9588066921705949, 0.970889877651981]}, "precision": 0.9762448606669712, "recall": 0.9540178571428571}, "group": {"f1": 0.9337209302325581, "f1_ci": {"90": [0.9186619111684506, 0.9476157489809728], "95": [0.9160821541280667, 0.9494624796759169]}, "precision": 0.9135381114903299, "recall": 0.9548156956004756}, "language": {"f1": 0.8372093023255814, "f1_ci": {"90": [0.7317073170731707, 0.9230769230769231], "95": [0.7058823529411765, 0.9411764705882353]}, "precision": 0.8571428571428571, "recall": 0.8181818181818182}, "law": {"f1": 0.6756756756756757, "f1_ci": {"90": [0.5507246376811594, 0.7858225108225106], "95": [0.5194564694564695, 0.8048859676908459]}, "precision": 0.7352941176470589, "recall": 0.625}, "location": {"f1": 0.7624020887728459, "f1_ci": {"90": [0.7159090909090909, 0.8052948402948403], "95": [0.7052152460836314, 0.8146637915859885]}, "precision": 0.7156862745098039, "recall": 0.8156424581005587}, "money": {"f1": 0.8818897637795275, "f1_ci": {"90": [0.8486692716920033, 0.9141318240683817], "95": [0.8442179400941717, 0.9222977161045344]}, "precision": 0.8722741433021807, "recall": 0.89171974522293}, "ordinal_number": {"f1": 0.8635235732009926, "f1_ci": {"90": [0.8323637555580891, 0.8932087017273362], "95": [0.824452588940783, 0.8984027892294282]}, "precision": 0.8365384615384616, "recall": 0.8923076923076924}, "organization": {"f1": 0.914952751528627, "f1_ci": {"90": [0.9040703443911496, 0.9256022910341289], "95": [0.9023951322353315, 0.9270120526085166]}, "precision": 0.9129229062673322, "recall": 0.916991643454039}, "percent": {"f1": 0.9, "f1_ci": {"90": [0.8643685509834927, 0.9322760755538995], "95": [0.858823101292919, 0.9376177922077923]}, "precision": 0.8974358974358975, "recall": 0.9025787965616046}, "person": {"f1": 0.9609866599546942, "f1_ci": {"90": [0.9549051480350558, 0.9671870030430941], "95": [0.9536727221285938, 0.9684061859032064]}, "precision": 0.961712846347607, "recall": 0.960261569416499}, "product": {"f1": 0.7901234567901234, "f1_ci": {"90": [0.7134465675866949, 0.8588271382172501], "95": [0.6945750544365813, 0.8689675697865352]}, "precision": 0.7441860465116279, "recall": 0.8421052631578947}, "quantity": {"f1": 0.8161434977578474, "f1_ci": {"90": [0.7536101904204847, 0.8716280602159256], "95": [0.7377543859649123, 0.8820588235294119]}, "precision": 0.7711864406779662, "recall": 0.8666666666666667}, "time": {"f1": 0.674364896073903, "f1_ci": {"90": [0.6221192233423761, 0.721523216148403], "95": [0.6117517618294261, 0.7317103734947124]}, "precision": 0.6606334841628959, "recall": 0.6886792452830188}, "work_of_art": {"f1": 0.7188405797101449, "f1_ci": {"90": [0.6665750915750914, 0.7664173228346457], "95": [0.6527626811594202, 0.7781568101472706]}, "precision": 0.6927374301675978, "recall": 0.7469879518072289}}}

eval/metric_span.json

@@ -0,0 +1 @@
+{"micro/f1": 0.9267538434352359, "micro/f1_ci": {"90": [0.922241598332788, 0.9310239217571861], "95": [0.9214101526505009, 0.9317962848686707]}, "micro/recall": 0.9317757839566492, "micro/precision": 0.9217857456718517, "macro/f1": 0.9267538434352359, "macro/f1_ci": {"90": [0.922241598332788, 0.9310239217571861], "95": [0.9214101526505009, 0.9317962848686707]}, "macro/recall": 0.9317757839566492, "macro/precision": 0.9217857456718517}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:6623273842f111141efed7506f70815efe36cdb90960ed934f41edbb979513fd
-size 1736332335
+oid sha256:bf457f85553cfc6301d4ec312cc1723387adcca7d8d8e001b5f52c357b4bac45
+size 1736337839

tokenizer_config.json

@@ -4,7 +4,7 @@
   "do_lower_case": false,
   "eos_token": "[SEP]",
   "mask_token": "[MASK]",
-  "name_or_path": "tner_ckpt/ontonotes5_deberta_v3_large/best_model",
+  "name_or_path": "tner_ckpt/ontonotes5_deberta_v3_large/model_ayzfwn/epoch_5",
   "pad_token": "[PAD]",
   "sep_token": "[SEP]",
   "sp_model_kwargs": {},

trainer_config.json

@@ -0,0 +1 @@
+{"dataset": ["tner/ontonotes5"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "microsoft/deberta-v3-large", "crf": true, "max_length": 128, "epoch": 15, "batch_size": 16, "lr": 1e-05, "random_seed": 42, "gradient_accumulation_steps": 4, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0}