model update

README.md

@@ -0,0 +1,168 @@
+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/roberta-large-tweetner7-selflabel2020
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: tner/tweetner7
+      type: tner/tweetner7
+      args: tner/tweetner7
+    metrics:
+    - name: F1 (test_2021)
+      type: f1
+      value: 0.6455908683974932
+    - name: Precision (test_2021)
+      type: precision
+      value: 0.6254336513443192
+    - name: Recall (test_2021)
+      type: recall
+      value: 0.6670906567992599
+    - name: Macro F1 (test_2021)
+      type: f1_macro
+      value: 0.5962839441412403
+    - name: Macro Precision (test_2021)
+      type: precision_macro
+      value: 0.5727192958380657
+    - name: Macro Recall (test_2021)
+      type: recall_macro
+      value: 0.6267698180905158
+    - name: Entity Span F1 (test_2021)
+      type: f1_entity_span
+      value: 0.7846231324492194
+    - name: Entity Span Precision (test_2020)
+      type: precision_entity_span
+      value: 0.7600823937554206
+    - name: Entity Span Recall (test_2021)
+      type: recall_entity_span
+      value: 0.8108014340233607
+    - name: F1 (test_2020)
+      type: f1
+      value: 0.6589874095901421
+    - name: Precision (test_2020)
+      type: precision
+      value: 0.6810631229235881
+    - name: Recall (test_2020)
+      type: recall
+      value: 0.6382978723404256
+    - name: Macro F1 (test_2020)
+      type: f1_macro
+      value: 0.6185133813760935
+    - name: Macro Precision (test_2020)
+      type: precision_macro
+      value: 0.6351153721439261
+    - name: Macro Recall (test_2020)
+      type: recall_macro
+      value: 0.6085669577041991
+    - name: Entity Span F1 (test_2020)
+      type: f1_entity_span
+      value: 0.7670865719646207
+    - name: Entity Span Precision (test_2020)
+      type: precision_entity_span
+      value: 0.7932372505543237
+    - name: Entity Span Recall (test_2020)
+      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/roberta-large-tweetner7-selflabel2020
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
+[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2020` split of the [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) annotated by [tner/roberta-large](https://huggingface.co/tner/tner/roberta-large-tweetner7-2020)). Please check [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling) for more detail of reproducing the model. 
+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): 0.6455908683974932
+- Precision (micro): 0.6254336513443192
+- Recall (micro): 0.6670906567992599
+- F1 (macro): 0.5962839441412403
+- Precision (macro): 0.5727192958380657
+- Recall (macro): 0.6267698180905158
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.522762148337596
+- creative_work: 0.468235294117647
+- event: 0.4446564885496183
+- group: 0.6155398587285571
+- location: 0.6423718344657197
+- person: 0.840225906358171
+- product: 0.6401960784313725 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6371204057050158, 0.6550747724054871]
+    - 95%: [0.6350657043101348, 0.6568098006368783] 
+- F1 (macro): 
+    - 90%: [0.6371204057050158, 0.6550747724054871]
+    - 95%: [0.6350657043101348, 0.6568098006368783] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020/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/roberta-large-tweetner7-selflabel2020")
+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
+ - dataset_name: None
+ - local_dataset: {'train': 'tweet_ner/2020.extra.tner/roberta-large-2020.txt', 'validation': 'tweet_ner/2020.dev.txt'}
+ - model: roberta-large
+ - 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/roberta-large-tweetner7-selflabel2020/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": 0.6497159859345416, "micro/f1_ci": {}, "micro/recall": 0.6274817136886103, "micro/precision": 0.6735838474481212, "macro/f1": 0.595168476497736, "macro/f1_ci": {}, "macro/recall": 0.5823140804003363, "macro/precision": 0.6134127256594191, "per_entity_metric": {"corporation": {"f1": 0.503896103896104, "f1_ci": {}, "precision": 0.532967032967033, "recall": 0.47783251231527096}, "creative_work": {"f1": 0.5169082125603864, "f1_ci": {}, "precision": 0.5194174757281553, "recall": 0.5144230769230769}, "event": {"f1": 0.3747276688453159, "f1_ci": {}, "precision": 0.4236453201970443, "recall": 0.3359375}, "group": {"f1": 0.5687203791469194, "f1_ci": {}, "precision": 0.6153846153846154, "recall": 0.5286343612334802}, "location": {"f1": 0.6716417910447762, "f1_ci": {}, "precision": 0.6108597285067874, "recall": 0.7458563535911602}, "person": {"f1": 0.8765217391304349, "f1_ci": {}, "precision": 0.9130434782608695, "recall": 0.842809364548495}, "product": {"f1": 0.6537634408602151, "f1_ci": {}, "precision": 0.6785714285714286, "recall": 0.6307053941908713}}}, "2021.test": {"micro/f1": 0.6455908683974932, "micro/f1_ci": {"90": [0.6371204057050158, 0.6550747724054871], "95": [0.6350657043101348, 0.6568098006368783]}, "micro/recall": 0.6670906567992599, "micro/precision": 0.6254336513443192, "macro/f1": 0.5962839441412403, "macro/f1_ci": {"90": [0.5866402556988275, 0.6060444749938684], "95": [0.5847471469279276, 0.6077635044702457]}, "macro/recall": 0.6267698180905158, "macro/precision": 0.5727192958380657, "per_entity_metric": {"corporation": {"f1": 0.522762148337596, "f1_ci": {"90": [0.49973681393417374, 0.5471596100193149], "95": [0.4945873783946403, 0.5525733399971992]}, "precision": 0.4843601895734597, "recall": 0.5677777777777778}, "creative_work": {"f1": 0.468235294117647, "f1_ci": {"90": [0.4391420478553882, 0.4976060818012257], "95": [0.43346081582227297, 0.5044102177633336]}, "precision": 0.4107327141382869, "recall": 0.5444596443228454}, "event": {"f1": 0.4446564885496183, "f1_ci": {"90": [0.42187388657957237, 0.46923141953822195], "95": [0.4184013216599329, 0.4749416023012337]}, "precision": 0.4674022066198596, "recall": 0.4240218380345769}, "group": {"f1": 0.6155398587285571, "f1_ci": {"90": [0.5951667589966113, 0.6381605192440433], "95": [0.5920980489275562, 0.6428354803338648]}, "precision": 0.6288659793814433, "recall": 0.6027667984189723}, "location": {"f1": 0.6423718344657197, "f1_ci": {"90": [0.6144162449467848, 0.6682611851729876], "95": [0.61024506683641, 0.6720074834037416]}, "precision": 0.5758582502768549, "recall": 0.7262569832402235}, "person": {"f1": 0.840225906358171, "f1_ci": {"90": [0.8297661584761087, 0.851118291744757], "95": [0.8284000592006534, 0.8525454107688213]}, "precision": 0.8303925099027728, "recall": 0.8502949852507374}, "product": {"f1": 0.6401960784313725, "f1_ci": {"90": [0.6188782031721285, 0.6619447156064523], "95": [0.6147969820761672, 0.6654252026481837]}, "precision": 0.6114232209737828, "recall": 0.6718106995884774}}}, "2020.test": {"micro/f1": 0.6589874095901421, "micro/f1_ci": {"90": [0.6385021699858212, 0.6777882762990388], "95": [0.6344556724262541, 0.6809892848641955]}, "micro/recall": 0.6382978723404256, "micro/precision": 0.6810631229235881, "macro/f1": 0.6185133813760935, "macro/f1_ci": {"90": [0.5945654495892557, 0.6390184922948274], "95": [0.5910565487766962, 0.6431119627935548]}, "macro/recall": 0.6085669577041991, "macro/precision": 0.6351153721439261, "per_entity_metric": {"corporation": {"f1": 0.581453634085213, "f1_ci": {"90": [0.5203143050197259, 0.6341570860642527], "95": [0.5087267380681245, 0.645339211120602]}, "precision": 0.5576923076923077, "recall": 0.6073298429319371}, "creative_work": {"f1": 0.519893899204244, "f1_ci": {"90": [0.45919192076494225, 0.5729194819819821], "95": [0.4496993234778251, 0.5788147694818441]}, "precision": 0.494949494949495, "recall": 0.547486033519553}, "event": {"f1": 0.48049281314168374, "f1_ci": {"90": [0.4285569985569985, 0.5315007805396874], "95": [0.4188008130081301, 0.5401474904055986]}, "precision": 0.527027027027027, "recall": 0.44150943396226416}, "group": {"f1": 0.5724907063197026, "f1_ci": {"90": [0.5248468820106881, 0.6198803159753711], "95": [0.5152538655348761, 0.6319220978293597]}, "precision": 0.6784140969162996, "recall": 0.49517684887459806}, "location": {"f1": 0.6685878962536022, "f1_ci": {"90": [0.6005705705705706, 0.7272727272727272], "95": [0.5917728867357167, 0.7374579124579128]}, "precision": 0.6373626373626373, "recall": 0.703030303030303}, "person": {"f1": 0.8415584415584415, "f1_ci": {"90": [0.8165424697279215, 0.8626729733238138], "95": [0.8128192329092423, 0.8676861375030747]}, "precision": 0.8694096601073346, "recall": 0.8154362416107382}, "product": {"f1": 0.6651162790697673, "f1_ci": {"90": [0.6157048774126238, 0.7126683307139996], "95": [0.6045411621182, 0.723205128986824]}, "precision": 0.680952380952381, "recall": 0.65}}}, "2021.test (span detection)": {"micro/f1": 0.7846231324492194, "micro/f1_ci": {}, "micro/recall": 0.8108014340233607, "micro/precision": 0.7600823937554206, "macro/f1": 0.7846231324492194, "macro/f1_ci": {}, "macro/recall": 0.8108014340233607, "macro/precision": 0.7600823937554206}, "2020.test (span detection)": {"micro/f1": 0.7670865719646207, "micro/f1_ci": {}, "micro/recall": 0.7426050856253243, "micro/precision": 0.7932372505543237, "macro/f1": 0.7670865719646207, "macro/f1_ci": {}, "macro/recall": 0.7426050856253243, "macro/precision": 0.7932372505543237}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6589874095901421, "micro/f1_ci": {"90": [0.6385021699858212, 0.6777882762990388], "95": [0.6344556724262541, 0.6809892848641955]}, "micro/recall": 0.6382978723404256, "micro/precision": 0.6810631229235881, "macro/f1": 0.6185133813760935, "macro/f1_ci": {"90": [0.5945654495892557, 0.6390184922948274], "95": [0.5910565487766962, 0.6431119627935548]}, "macro/recall": 0.6085669577041991, "macro/precision": 0.6351153721439261, "per_entity_metric": {"corporation": {"f1": 0.581453634085213, "f1_ci": {"90": [0.5203143050197259, 0.6341570860642527], "95": [0.5087267380681245, 0.645339211120602]}, "precision": 0.5576923076923077, "recall": 0.6073298429319371}, "creative_work": {"f1": 0.519893899204244, "f1_ci": {"90": [0.45919192076494225, 0.5729194819819821], "95": [0.4496993234778251, 0.5788147694818441]}, "precision": 0.494949494949495, "recall": 0.547486033519553}, "event": {"f1": 0.48049281314168374, "f1_ci": {"90": [0.4285569985569985, 0.5315007805396874], "95": [0.4188008130081301, 0.5401474904055986]}, "precision": 0.527027027027027, "recall": 0.44150943396226416}, "group": {"f1": 0.5724907063197026, "f1_ci": {"90": [0.5248468820106881, 0.6198803159753711], "95": [0.5152538655348761, 0.6319220978293597]}, "precision": 0.6784140969162996, "recall": 0.49517684887459806}, "location": {"f1": 0.6685878962536022, "f1_ci": {"90": [0.6005705705705706, 0.7272727272727272], "95": [0.5917728867357167, 0.7374579124579128]}, "precision": 0.6373626373626373, "recall": 0.703030303030303}, "person": {"f1": 0.8415584415584415, "f1_ci": {"90": [0.8165424697279215, 0.8626729733238138], "95": [0.8128192329092423, 0.8676861375030747]}, "precision": 0.8694096601073346, "recall": 0.8154362416107382}, "product": {"f1": 0.6651162790697673, "f1_ci": {"90": [0.6157048774126238, 0.7126683307139996], "95": [0.6045411621182, 0.723205128986824]}, "precision": 0.680952380952381, "recall": 0.65}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6455908683974932, "micro/f1_ci": {"90": [0.6371204057050158, 0.6550747724054871], "95": [0.6350657043101348, 0.6568098006368783]}, "micro/recall": 0.6670906567992599, "micro/precision": 0.6254336513443192, "macro/f1": 0.5962839441412403, "macro/f1_ci": {"90": [0.5866402556988275, 0.6060444749938684], "95": [0.5847471469279276, 0.6077635044702457]}, "macro/recall": 0.6267698180905158, "macro/precision": 0.5727192958380657, "per_entity_metric": {"corporation": {"f1": 0.522762148337596, "f1_ci": {"90": [0.49973681393417374, 0.5471596100193149], "95": [0.4945873783946403, 0.5525733399971992]}, "precision": 0.4843601895734597, "recall": 0.5677777777777778}, "creative_work": {"f1": 0.468235294117647, "f1_ci": {"90": [0.4391420478553882, 0.4976060818012257], "95": [0.43346081582227297, 0.5044102177633336]}, "precision": 0.4107327141382869, "recall": 0.5444596443228454}, "event": {"f1": 0.4446564885496183, "f1_ci": {"90": [0.42187388657957237, 0.46923141953822195], "95": [0.4184013216599329, 0.4749416023012337]}, "precision": 0.4674022066198596, "recall": 0.4240218380345769}, "group": {"f1": 0.6155398587285571, "f1_ci": {"90": [0.5951667589966113, 0.6381605192440433], "95": [0.5920980489275562, 0.6428354803338648]}, "precision": 0.6288659793814433, "recall": 0.6027667984189723}, "location": {"f1": 0.6423718344657197, "f1_ci": {"90": [0.6144162449467848, 0.6682611851729876], "95": [0.61024506683641, 0.6720074834037416]}, "precision": 0.5758582502768549, "recall": 0.7262569832402235}, "person": {"f1": 0.840225906358171, "f1_ci": {"90": [0.8297661584761087, 0.851118291744757], "95": [0.8284000592006534, 0.8525454107688213]}, "precision": 0.8303925099027728, "recall": 0.8502949852507374}, "product": {"f1": 0.6401960784313725, "f1_ci": {"90": [0.6188782031721285, 0.6619447156064523], "95": [0.6147969820761672, 0.6654252026481837]}, "precision": 0.6114232209737828, "recall": 0.6718106995884774}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7670865719646207, "micro/f1_ci": {}, "micro/recall": 0.7426050856253243, "micro/precision": 0.7932372505543237, "macro/f1": 0.7670865719646207, "macro/f1_ci": {}, "macro/recall": 0.7426050856253243, "macro/precision": 0.7932372505543237}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7846231324492194, "micro/f1_ci": {}, "micro/recall": 0.8108014340233607, "micro/precision": 0.7600823937554206, "macro/f1": 0.7846231324492194, "macro/f1_ci": {}, "macro/recall": 0.8108014340233607, "macro/precision": 0.7600823937554206}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020.extra.roberta-large-2020", "model": "roberta-large", "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", "dataset_name": null, "local_dataset": {"train": "tweet_ner/2020.extra.tner/roberta-large-2020.txt", "validation": "tweet_ner/2020.dev.txt"}, "model": "roberta-large", "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}