model update

README.md

@@ -0,0 +1,126 @@
+---
+datasets:
+- wnut2017
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/bertweet-large-wnut2017
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut2017
+      type: wnut2017
+      args: wnut2017
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.5302273987798114
+    - name: Precision
+      type: precision
+      value: 0.6602209944751382
+    - name: Recall
+      type: recall
+      value: 0.44300278035217794
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.4643459997680019
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5792841925426832
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.3973128655628379
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.6142697881828317
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7706293706293706
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.5106580166821131
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/bertweet-large-wnut2017
+
+This model is a fine-tuned version of [vinai/bertweet-large](https://huggingface.co/vinai/bertweet-large) on the 
+[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) 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): 0.5302273987798114
+- Precision (micro): 0.6602209944751382
+- Recall (micro): 0.44300278035217794
+- F1 (macro): 0.4643459997680019
+- Precision (macro): 0.5792841925426832
+- Recall (macro): 0.3973128655628379
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.3902439024390244
+- group: 0.37130801687763715
+- location: 0.6595744680851063
+- person: 0.65474552957359
+- product: 0.2857142857142857
+- work_of_art: 0.4244897959183674 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.5002577319587629, 0.5587481638299118]
+    - 95%: [0.4947163587619384, 0.5629013150503995] 
+- F1 (macro): 
+    - 90%: [0.5002577319587629, 0.5587481638299118]
+    - 95%: [0.4947163587619384, 0.5629013150503995] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/bertweet-large-wnut2017/raw/main/eval/metric_span.json).
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/wnut2017']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: vinai/bertweet-large
+ - crf: False
+ - 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/bertweet-large-wnut2017/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/wnut2017_bertweet_large/best_model",
+  "_name_or_path": "tner_ckpt/wnut2017_bertweet_large/model_ulfllg/epoch_5",
   "architectures": [
     "RobertaForTokenClassification"
   ],

eval/metric.json

@@ -0,0 +1 @@
+{"micro/f1": 0.5302273987798114, "micro/f1_ci": {"90": [0.5002577319587629, 0.5587481638299118], "95": [0.4947163587619384, 0.5629013150503995]}, "micro/recall": 0.44300278035217794, "micro/precision": 0.6602209944751382, "macro/f1": 0.4643459997680019, "macro/f1_ci": {"90": [0.4295294813187177, 0.49516911094489413], "95": [0.42541205920185476, 0.5012597092323381]}, "macro/recall": 0.3973128655628379, "macro/precision": 0.5792841925426832, "per_entity_metric": {"corporation": {"f1": 0.3902439024390244, "f1_ci": {"90": [0.28251334858886346, 0.4844618055555554], "95": [0.2615622052645115, 0.5000766871165647]}, "precision": 0.42105263157894735, "recall": 0.36363636363636365}, "group": {"f1": 0.37130801687763715, "f1_ci": {"90": [0.28571428571428575, 0.4476021935286534], "95": [0.2649412628487518, 0.45962356792144043]}, "precision": 0.6111111111111112, "recall": 0.26666666666666666}, "location": {"f1": 0.6595744680851063, "f1_ci": {"90": [0.5980565014653362, 0.7165448388917255], "95": [0.5819641909031913, 0.7272881880024737]}, "precision": 0.7045454545454546, "recall": 0.62}, "person": {"f1": 0.65474552957359, "f1_ci": {"90": [0.6147118506493507, 0.6900943074573127], "95": [0.6088246972404209, 0.6951931075460488]}, "precision": 0.7986577181208053, "recall": 0.5547785547785548}, "product": {"f1": 0.2857142857142857, "f1_ci": {"90": [0.21047120418848164, 0.35790123456790124], "95": [0.19321348940914154, 0.3714393395549175]}, "precision": 0.43548387096774194, "recall": 0.2125984251968504}, "work_of_art": {"f1": 0.4244897959183674, "f1_ci": {"90": [0.3478106691335183, 0.4930296272698311], "95": [0.33599726775956273, 0.5040740341031729]}, "precision": 0.5048543689320388, "recall": 0.36619718309859156}}}

eval/metric_span.json

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+{"micro/f1": 0.6142697881828317, "micro/f1_ci": {"90": [0.5873941890739028, 0.6394992474386686], "95": [0.5823018215160319, 0.644268545034642]}, "micro/recall": 0.5106580166821131, "micro/precision": 0.7706293706293706, "macro/f1": 0.6142697881828317, "macro/f1_ci": {"90": [0.5873941890739028, 0.6394992474386686], "95": [0.5823018215160319, 0.644268545034642]}, "macro/recall": 0.5106580166821131, "macro/precision": 0.7706293706293706}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 1417424945
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+size 1417430385

tokenizer_config.json

@@ -5,7 +5,7 @@
   "eos_token": "</s>",
   "errors": "replace",
   "mask_token": "<mask>",
-  "name_or_path": "tner_ckpt/wnut2017_bertweet_large/best_model",
+  "name_or_path": "tner_ckpt/wnut2017_bertweet_large/model_ulfllg/epoch_5",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": "tner_ckpt/wnut2017_bertweet_large/model_ulfllg/epoch_5/special_tokens_map.json",

trainer_config.json

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+{"dataset": ["tner/wnut2017"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "vinai/bertweet-large", "crf": false, "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}