model update

README.md

@@ -0,0 +1,176 @@
+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/twitter-roberta-base-dec2020-tweetner7-2020
+  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: 0.6286510590858417
+    - name: Precision
+      type: precision
+      value: 0.6068661213947482
+    - name: Recall
+      type: recall
+      value: 0.6520582793709528
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5826193263874178
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5604713618790561
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6077023445382103
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7649255811360722
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7383770985794231
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7934543772406615
+  - 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: 0.6439333862014274
+    - name: Precision
+      type: precision
+      value: 0.65625
+    - name: Recall
+      type: recall
+      value: 0.6320705760249092
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6031387609223192
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6135739317575558
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5938222468969824
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7565424266455195
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7710129310344828
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7426050856253243
+
+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/twitter-roberta-base-dec2020-tweetner7-2020
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` 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): 0.6286510590858417
+- Precision (micro): 0.6068661213947482
+- Recall (micro): 0.6520582793709528
+- F1 (macro): 0.5826193263874178
+- Precision (macro): 0.5604713618790561
+- Recall (macro): 0.6077023445382103
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.49895397489539745
+- creative_work: 0.44903064415259536
+- event: 0.43684450524395807
+- group: 0.5762400489895897
+- location: 0.6437541308658294
+- person: 0.8228613299139035
+- product: 0.6506506506506508 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6202748254882829, 0.6378443623614226]
+    - 95%: [0.6184976815588282, 0.6399304136434694] 
+- F1 (macro): 
+    - 90%: [0.6202748254882829, 0.6378443623614226]
+    - 95%: [0.6184976815588282, 0.6399304136434694] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2020/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2020/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/twitter-roberta-base-dec2020-tweetner7-2020")
+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_2020
+ - dataset_name: None
+ - local_dataset: None
+ - model: cardiffnlp/twitter-roberta-base-dec2020
+ - 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: 0.15
+ - max_grad_norm: 1
+
+The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2020/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

@@ -1 +0,0 @@
-{"2020.dev": {"micro/f1": 0.6386958845537146, "micro/f1_ci": {}, "micro/recall": 0.6243469174503657, "micro/precision": 0.6537199124726477, "macro/f1": 0.5845688851603238, "macro/f1_ci": {}, "macro/recall": 0.5723876957337674, "macro/precision": 0.5986924134163691, "per_entity_metric": {"corporation": {"f1": 0.4895833333333333, "f1_ci": {}, "precision": 0.5193370165745856, "recall": 0.4630541871921182}, "creative_work": {"f1": 0.5089058524173028, "f1_ci": {}, "precision": 0.5405405405405406, "recall": 0.4807692307692308}, "event": {"f1": 0.39285714285714285, "f1_ci": {}, "precision": 0.39919354838709675, "recall": 0.38671875}, "group": {"f1": 0.5296610169491525, "f1_ci": {}, "precision": 0.5102040816326531, "recall": 0.5506607929515418}, "location": {"f1": 0.6413043478260869, "f1_ci": {}, "precision": 0.6310160427807486, "recall": 0.6519337016574586}, "person": {"f1": 0.8760757314974182, "f1_ci": {}, "precision": 0.9024822695035462, "recall": 0.8511705685618729}, "product": {"f1": 0.6535947712418301, "f1_ci": {}, "precision": 0.6880733944954128, "recall": 0.6224066390041494}}}, "2021.test": {"micro/f1": 0.6286510590858417, "micro/f1_ci": {"90": [0.6202748254882829, 0.6378443623614226], "95": [0.6184976815588282, 0.6399304136434694]}, "micro/recall": 0.6520582793709528, "micro/precision": 0.6068661213947482, "macro/f1": 0.5826193263874178, "macro/f1_ci": {"90": [0.5730758041885631, 0.5923948982929359], "95": [0.5710195948548461, 0.5939565674784726]}, "macro/recall": 0.6077023445382103, "macro/precision": 0.5604713618790561, "per_entity_metric": {"corporation": {"f1": 0.49895397489539745, "f1_ci": {"90": [0.4740900380273677, 0.5254155321720864], "95": [0.46839534789719484, 0.5299778434362932]}, "precision": 0.47134387351778656, "recall": 0.53}, "creative_work": {"f1": 0.44903064415259536, "f1_ci": {"90": [0.41834966834966836, 0.47882006500914226], "95": [0.4111298735015004, 0.48611544056525346]}, "precision": 0.41359447004608296, "recall": 0.4911080711354309}, "event": {"f1": 0.43684450524395807, "f1_ci": {"90": [0.4132443746794808, 0.4595091419890372], "95": [0.4083257469654529, 0.46410276457343014]}, "precision": 0.43784277879341865, "recall": 0.43585077343039125}, "group": {"f1": 0.5762400489895897, "f1_ci": {"90": [0.5565039530120158, 0.5954325184333603], "95": [0.5526282709880291, 0.5985174584532377]}, "precision": 0.5383295194508009, "recall": 0.6198945981554678}, "location": {"f1": 0.6437541308658294, "f1_ci": {"90": [0.6177487356743749, 0.6711433093555442], "95": [0.6108565669901822, 0.6758728290717165]}, "precision": 0.6110414052697616, "recall": 0.6801675977653632}, "person": {"f1": 0.8228613299139035, "f1_ci": {"90": [0.8118870888802372, 0.8341448223449718], "95": [0.8093167258284707, 0.8359517294083364]}, "precision": 0.8176192209683291, "recall": 0.8281710914454278}, "product": {"f1": 0.6506506506506508, "f1_ci": {"90": [0.628450730150472, 0.672123577203644], "95": [0.6258410385414003, 0.6746884809110153]}, "precision": 0.6335282651072125, "recall": 0.668724279835391}}}, "2020.test": {"micro/f1": 0.6439333862014274, "micro/f1_ci": {"90": [0.6234163633400337, 0.6617054879836103], "95": [0.620296601140631, 0.6652159767398678]}, "micro/recall": 0.6320705760249092, "micro/precision": 0.65625, "macro/f1": 0.6031387609223192, "macro/f1_ci": {"90": [0.5814650043179064, 0.6214737405579287], "95": [0.5769478753146529, 0.6247167595269991]}, "macro/recall": 0.5938222468969824, "macro/precision": 0.6135739317575558, "per_entity_metric": {"corporation": {"f1": 0.5518987341772151, "f1_ci": {"90": [0.495774647887324, 0.6018518518518519], "95": [0.481054054054054, 0.6117767106842738]}, "precision": 0.5343137254901961, "recall": 0.5706806282722513}, "creative_work": {"f1": 0.5172413793103449, "f1_ci": {"90": [0.4618975618975619, 0.5738577425556122], "95": [0.44866459269501097, 0.5829040366996946]}, "precision": 0.5325443786982249, "recall": 0.5027932960893855}, "event": {"f1": 0.42911877394636017, "f1_ci": {"90": [0.38280066287878795, 0.4780351622131284], "95": [0.3752165216808781, 0.4866137266023824]}, "precision": 0.4357976653696498, "recall": 0.4226415094339623}, "group": {"f1": 0.559463986599665, "f1_ci": {"90": [0.5110015268737078, 0.6065222988556122], "95": [0.5, 0.6178964376394552]}, "precision": 0.583916083916084, "recall": 0.5369774919614148}, "location": {"f1": 0.6546546546546548, "f1_ci": {"90": [0.5867749707685221, 0.7157319269359402], "95": [0.5732812621501827, 0.7224163619125017]}, "precision": 0.6488095238095238, "recall": 0.6606060606060606}, "person": {"f1": 0.8397600685518423, "f1_ci": {"90": [0.8127127749861485, 0.8636382189239333], "95": [0.806420397665552, 0.86896508864127]}, "precision": 0.8581436077057794, "recall": 0.8221476510067114}, "product": {"f1": 0.669833729216152, "f1_ci": {"90": [0.6180720440280364, 0.715330305760356], "95": [0.6086568322981365, 0.7247099284158108]}, "precision": 0.7014925373134329, "recall": 0.6409090909090909}}}, "2021.test (span detection)": {"micro/f1": 0.7649255811360722, "micro/f1_ci": {}, "micro/recall": 0.7934543772406615, "micro/precision": 0.7383770985794231, "macro/f1": 0.7649255811360722, "macro/f1_ci": {}, "macro/recall": 0.7934543772406615, "macro/precision": 0.7383770985794231}, "2020.test (span detection)": {"micro/f1": 0.7565424266455195, "micro/f1_ci": {}, "micro/recall": 0.7426050856253243, "micro/precision": 0.7710129310344828, "macro/f1": 0.7565424266455195, "macro/f1_ci": {}, "macro/recall": 0.7426050856253243, "macro/precision": 0.7710129310344828}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6439333862014274, "micro/f1_ci": {"90": [0.6234163633400337, 0.6617054879836103], "95": [0.620296601140631, 0.6652159767398678]}, "micro/recall": 0.6320705760249092, "micro/precision": 0.65625, "macro/f1": 0.6031387609223192, "macro/f1_ci": {"90": [0.5814650043179064, 0.6214737405579287], "95": [0.5769478753146529, 0.6247167595269991]}, "macro/recall": 0.5938222468969824, "macro/precision": 0.6135739317575558, "per_entity_metric": {"corporation": {"f1": 0.5518987341772151, "f1_ci": {"90": [0.495774647887324, 0.6018518518518519], "95": [0.481054054054054, 0.6117767106842738]}, "precision": 0.5343137254901961, "recall": 0.5706806282722513}, "creative_work": {"f1": 0.5172413793103449, "f1_ci": {"90": [0.4618975618975619, 0.5738577425556122], "95": [0.44866459269501097, 0.5829040366996946]}, "precision": 0.5325443786982249, "recall": 0.5027932960893855}, "event": {"f1": 0.42911877394636017, "f1_ci": {"90": [0.38280066287878795, 0.4780351622131284], "95": [0.3752165216808781, 0.4866137266023824]}, "precision": 0.4357976653696498, "recall": 0.4226415094339623}, "group": {"f1": 0.559463986599665, "f1_ci": {"90": [0.5110015268737078, 0.6065222988556122], "95": [0.5, 0.6178964376394552]}, "precision": 0.583916083916084, "recall": 0.5369774919614148}, "location": {"f1": 0.6546546546546548, "f1_ci": {"90": [0.5867749707685221, 0.7157319269359402], "95": [0.5732812621501827, 0.7224163619125017]}, "precision": 0.6488095238095238, "recall": 0.6606060606060606}, "person": {"f1": 0.8397600685518423, "f1_ci": {"90": [0.8127127749861485, 0.8636382189239333], "95": [0.806420397665552, 0.86896508864127]}, "precision": 0.8581436077057794, "recall": 0.8221476510067114}, "product": {"f1": 0.669833729216152, "f1_ci": {"90": [0.6180720440280364, 0.715330305760356], "95": [0.6086568322981365, 0.7247099284158108]}, "precision": 0.7014925373134329, "recall": 0.6409090909090909}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6286510590858417, "micro/f1_ci": {"90": [0.6202748254882829, 0.6378443623614226], "95": [0.6184976815588282, 0.6399304136434694]}, "micro/recall": 0.6520582793709528, "micro/precision": 0.6068661213947482, "macro/f1": 0.5826193263874178, "macro/f1_ci": {"90": [0.5730758041885631, 0.5923948982929359], "95": [0.5710195948548461, 0.5939565674784726]}, "macro/recall": 0.6077023445382103, "macro/precision": 0.5604713618790561, "per_entity_metric": {"corporation": {"f1": 0.49895397489539745, "f1_ci": {"90": [0.4740900380273677, 0.5254155321720864], "95": [0.46839534789719484, 0.5299778434362932]}, "precision": 0.47134387351778656, "recall": 0.53}, "creative_work": {"f1": 0.44903064415259536, "f1_ci": {"90": [0.41834966834966836, 0.47882006500914226], "95": [0.4111298735015004, 0.48611544056525346]}, "precision": 0.41359447004608296, "recall": 0.4911080711354309}, "event": {"f1": 0.43684450524395807, "f1_ci": {"90": [0.4132443746794808, 0.4595091419890372], "95": [0.4083257469654529, 0.46410276457343014]}, "precision": 0.43784277879341865, "recall": 0.43585077343039125}, "group": {"f1": 0.5762400489895897, "f1_ci": {"90": [0.5565039530120158, 0.5954325184333603], "95": [0.5526282709880291, 0.5985174584532377]}, "precision": 0.5383295194508009, "recall": 0.6198945981554678}, "location": {"f1": 0.6437541308658294, "f1_ci": {"90": [0.6177487356743749, 0.6711433093555442], "95": [0.6108565669901822, 0.6758728290717165]}, "precision": 0.6110414052697616, "recall": 0.6801675977653632}, "person": {"f1": 0.8228613299139035, "f1_ci": {"90": [0.8118870888802372, 0.8341448223449718], "95": [0.8093167258284707, 0.8359517294083364]}, "precision": 0.8176192209683291, "recall": 0.8281710914454278}, "product": {"f1": 0.6506506506506508, "f1_ci": {"90": [0.628450730150472, 0.672123577203644], "95": [0.6258410385414003, 0.6746884809110153]}, "precision": 0.6335282651072125, "recall": 0.668724279835391}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7565424266455195, "micro/f1_ci": {}, "micro/recall": 0.7426050856253243, "micro/precision": 0.7710129310344828, "macro/f1": 0.7565424266455195, "macro/f1_ci": {}, "macro/recall": 0.7426050856253243, "macro/precision": 0.7710129310344828}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7649255811360722, "micro/f1_ci": {}, "micro/recall": 0.7934543772406615, "micro/precision": 0.7383770985794231, "macro/f1": 0.7649255811360722, "macro/f1_ci": {}, "macro/recall": 0.7934543772406615, "macro/precision": 0.7383770985794231}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020.train", "model": "cardiffnlp/twitter-roberta-base-dec2020", "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": 0.15, "max_grad_norm": 1}
+{"dataset": ["tner/tweetner7"], "dataset_split": "train_2020", "dataset_name": null, "local_dataset": null, "model": "cardiffnlp/twitter-roberta-base-dec2020", "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": 0.15, "max_grad_norm": 1}