model update

README.md

@@ -0,0 +1,124 @@
+---
+datasets:
+- conll2003
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-large-conll2003
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.924769027716674
+    - name: Precision
+      type: precision
+      value: 0.9191883855168795
+    - name: Recall
+      type: recall
+      value: 0.9304178470254958
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.9110950780089749
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.9030546238754271
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.9197126371122274
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.9619852164730729
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.9562631210636809
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.9677762039660056
+
+pipeline_tag: token-classification
+widget:
+- text: "Jacob Collier is a Grammy awarded artist from England."
+  example_title: "NER Example 1"
+---
+# tner/roberta-large-conll2003
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
+[tner/conll2003](https://huggingface.co/datasets/tner/conll2003) 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): 0.924769027716674
+- Precision (micro): 0.9191883855168795
+- Recall (micro): 0.9304178470254958
+- F1 (macro): 0.9110950780089749
+- Precision (macro): 0.9030546238754271
+- Recall (macro): 0.9197126371122274
+
+The per-entity breakdown of the F1 score on the test set are below:
+- location: 0.9390573401380967
+- organization: 0.9107142857142857
+- other: 0.8247422680412372
+- person: 0.9698664181422801 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.9185189408755685, 0.9309806929048586]
+    - 95%: [0.9174010190551032, 0.9318590917100465] 
+- F1 (macro): 
+    - 90%: [0.9185189408755685, 0.9309806929048586]
+    - 95%: [0.9174010190551032, 0.9318590917100465] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-conll2003/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-large-conll2003/raw/main/eval/metric_span.json).
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+ - dataset: ['tner/conll2003']
+ - dataset_split: train
+ - dataset_name: None
+ - local_dataset: None
+ - model: roberta-large
+ - crf: True
+ - max_length: 128
+ - epoch: 17
+ - batch_size: 64
+ - lr: 1e-05
+ - random_seed: 42
+ - gradient_accumulation_steps: 1
+ - weight_decay: None
+ - 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/roberta-large-conll2003/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/conll2003_roberta_large/best_model",
+  "_name_or_path": "tner_ckpt/conll2003_roberta_large/model_rcsnba/epoch_16",
   "architectures": [
     "RobertaForTokenClassification"
   ],

eval/metric.json

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+{"micro/f1": 0.924769027716674, "micro/f1_ci": {"90": [0.9185189408755685, 0.9309806929048586], "95": [0.9174010190551032, 0.9318590917100465]}, "micro/recall": 0.9304178470254958, "micro/precision": 0.9191883855168795, "macro/f1": 0.9110950780089749, "macro/f1_ci": {"90": [0.9037658968420902, 0.9180195035939583], "95": [0.9025770150365444, 0.9194742929481238]}, "macro/recall": 0.9197126371122274, "macro/precision": 0.9030546238754271, "per_entity_metric": {"location": {"f1": 0.9390573401380967, "f1_ci": {"90": [0.9313965104630062, 0.9466090331717206], "95": [0.9300843094139872, 0.9479665926179084]}, "precision": 0.9404690318701142, "recall": 0.9376498800959233}, "organization": {"f1": 0.9107142857142857, "f1_ci": {"90": [0.9007880638693908, 0.9204616266778444], "95": [0.8990486734098716, 0.9225422754373195]}, "precision": 0.9005297233666862, "recall": 0.9211318482841662}, "other": {"f1": 0.8247422680412372, "f1_ci": {"90": [0.805305076465003, 0.8442077230359522], "95": [0.8014114890584008, 0.8477429372112399]}, "precision": 0.796812749003984, "recall": 0.8547008547008547}, "person": {"f1": 0.9698664181422801, "f1_ci": {"90": [0.9624360457807172, 0.9764181563647193], "95": [0.9610383119046899, 0.9773602066916893]}, "precision": 0.9744069912609239, "recall": 0.9653679653679653}}}

eval/metric_span.json

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+{"micro/f1": 0.9619852164730729, "micro/f1_ci": {"90": [0.9577645343622905, 0.9662569291727845], "95": [0.9566146856431471, 0.9669190537417556]}, "micro/recall": 0.9677762039660056, "micro/precision": 0.9562631210636809, "macro/f1": 0.9619852164730729, "macro/f1_ci": {"90": [0.9577645343622905, 0.9662569291727845], "95": [0.9566146856431471, 0.9669190537417556]}, "macro/recall": 0.9677762039660056, "macro/precision": 0.9562631210636809}

pytorch_model.bin

@@ -1,3 +1,3 @@
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-size 1417408561
+oid sha256:25de4402a9051fc455e281bac160fd20d206f45fc6b48f16ce0c802e8c81a085
+size 1417414001

tokenizer_config.json

@@ -6,7 +6,7 @@
   "errors": "replace",
   "mask_token": "<mask>",
   "model_max_length": 512,
-  "name_or_path": "tner_ckpt/conll2003_roberta_large/best_model",
+  "name_or_path": "tner_ckpt/conll2003_roberta_large/model_rcsnba/epoch_16",
   "pad_token": "<pad>",
   "sep_token": "</s>",
   "special_tokens_map_file": null,

trainer_config.json

@@ -0,0 +1 @@
+{"dataset": ["tner/conll2003"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "roberta-large", "crf": true, "max_length": 128, "epoch": 17, "batch_size": 64, "lr": 1e-05, "random_seed": 42, "gradient_accumulation_steps": 1, "weight_decay": null, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0}