model update

README.md

@@ -0,0 +1,176 @@
+---
+datasets:
+- tner/tweetner7
+metrics:
+- f1
+- precision
+- recall
+model-index:
+- name: tner/twitter-roberta-base-dec2021-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: 0.6417969860676713
+    - name: Precision
+      type: precision
+      value: 0.6314199395770392
+    - name: Recall
+      type: recall
+      value: 0.6525208140610546
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.5950190138355756
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.5844336783514947
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.6100191042323923
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.77377161055505
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7612174107642385
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7867468486180178
+  - 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: 0.6535560344827587
+    - name: Precision
+      type: precision
+      value: 0.6795518207282913
+    - name: Recall
+      type: recall
+      value: 0.6294758692267773
+    - name: F1 (macro)
+      type: f1_macro
+      value: 0.6112036126522273
+    - name: Precision (macro)
+      type: precision_macro
+      value: 0.6366190072656497
+    - name: Recall (macro)
+      type: recall_macro
+      value: 0.5931815043549611
+    - name: F1 (entity span)
+      type: f1_entity_span
+      value: 0.7636755591484775
+    - name: Precision (entity span)
+      type: precision_entity_span
+      value: 0.7942825112107623
+    - name: Recall (entity span)
+      type: recall_entity_span
+      value: 0.7353399065905553
+
+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-dec2021-tweetner7-2020
+
+This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) 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): 0.6417969860676713
+- Precision (micro): 0.6314199395770392
+- Recall (micro): 0.6525208140610546
+- F1 (macro): 0.5950190138355756
+- Precision (macro): 0.5844336783514947
+- Recall (macro): 0.6100191042323923
+
+
+
+The per-entity breakdown of the F1 score on the test set are below:
+- corporation: 0.5161953727506428
+- creative_work: 0.4749841671944269
+- event: 0.43429109750353273
+- group: 0.593413759373981
+- location: 0.6431718061674009
+- person: 0.8327532515112659
+- product: 0.6703236423477785 
+
+For F1 scores, the confidence interval is obtained by bootstrap as below:
+- F1 (micro): 
+    - 90%: [0.6334648803400447, 0.651188450223803]
+    - 95%: [0.6314263719566943, 0.6528797499551452] 
+- F1 (macro): 
+    - 90%: [0.6334648803400447, 0.651188450223803]
+    - 95%: [0.6314263719566943, 0.6528797499551452] 
+
+Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-2020/raw/main/eval/metric.json) 
+and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-dec2021-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-dec2021-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-dec2021
+ - 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-dec2021-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": 0.6370572207084468, "micro/f1_ci": {}, "micro/recall": 0.6107628004179728, "micro/precision": 0.6657175398633257, "macro/f1": 0.5818500927746258, "macro/f1_ci": {}, "macro/recall": 0.558634696207478, "macro/precision": 0.6115327302781756, "per_entity_metric": {"corporation": {"f1": 0.4693877551020408, "f1_ci": {}, "precision": 0.48677248677248675, "recall": 0.45320197044334976}, "creative_work": {"f1": 0.5144356955380578, "f1_ci": {}, "precision": 0.5664739884393064, "recall": 0.47115384615384615}, "event": {"f1": 0.3828920570264766, "f1_ci": {}, "precision": 0.4, "recall": 0.3671875}, "group": {"f1": 0.5622119815668202, "f1_ci": {}, "precision": 0.5893719806763285, "recall": 0.5374449339207048}, "location": {"f1": 0.6285714285714284, "f1_ci": {}, "precision": 0.5931372549019608, "recall": 0.6685082872928176}, "person": {"f1": 0.8676975945017182, "f1_ci": {}, "precision": 0.892226148409894, "recall": 0.8444816053511706}, "product": {"f1": 0.6477541371158392, "f1_ci": {}, "precision": 0.7527472527472527, "recall": 0.5684647302904564}}}, "2021.test": {"micro/f1": 0.6417969860676713, "micro/f1_ci": {"90": [0.6334648803400447, 0.651188450223803], "95": [0.6314263719566943, 0.6528797499551452]}, "micro/recall": 0.6525208140610546, "micro/precision": 0.6314199395770392, "macro/f1": 0.5950190138355756, "macro/f1_ci": {"90": [0.5855854380997908, 0.6046272067604185], "95": [0.5837653478173793, 0.606080435069187]}, "macro/recall": 0.6100191042323923, "macro/precision": 0.5844336783514947, "per_entity_metric": {"corporation": {"f1": 0.5161953727506428, "f1_ci": {"90": [0.4927218605886716, 0.5414831199725443], "95": [0.4870848768801742, 0.547844877649912]}, "precision": 0.48038277511961724, "recall": 0.5577777777777778}, "creative_work": {"f1": 0.4749841671944269, "f1_ci": {"90": [0.44315016015693665, 0.5054160340115397], "95": [0.4373215286098878, 0.5122643035682656]}, "precision": 0.44221698113207547, "recall": 0.5129958960328317}, "event": {"f1": 0.43429109750353273, "f1_ci": {"90": [0.4106041087816789, 0.45832656118533116], "95": [0.40788582234365367, 0.46288163349626044]}, "precision": 0.4501953125, "recall": 0.4194722474977252}, "group": {"f1": 0.593413759373981, "f1_ci": {"90": [0.5729480380392004, 0.6136009860444586], "95": [0.569351806541065, 0.6194689670779012]}, "precision": 0.5874757908327953, "recall": 0.5994729907773386}, "location": {"f1": 0.6431718061674009, "f1_ci": {"90": [0.6159219058444587, 0.6695603128406853], "95": [0.6090556482248325, 0.6734092369794471]}, "precision": 0.5853379152348225, "recall": 0.7136871508379888}, "person": {"f1": 0.8327532515112659, "f1_ci": {"90": [0.8230547559590614, 0.8433818253656356], "95": [0.8211029923755827, 0.8457833808000503]}, "precision": 0.827448125227521, "recall": 0.8381268436578171}, "product": {"f1": 0.6703236423477785, "f1_ci": {"90": [0.6478723110217235, 0.6911223620805819], "95": [0.6441389195232122, 0.6947141029343396]}, "precision": 0.717978848413631, "recall": 0.6286008230452675}}}, "2020.test": {"micro/f1": 0.6535560344827587, "micro/f1_ci": {"90": [0.6322110846801919, 0.6725291873276269], "95": [0.6284137294993963, 0.6757531238447321]}, "micro/recall": 0.6294758692267773, "micro/precision": 0.6795518207282913, "macro/f1": 0.6112036126522273, "macro/f1_ci": {"90": [0.5885812318209095, 0.6302682399052447], "95": [0.5851527157984716, 0.6350317949646983]}, "macro/recall": 0.5931815043549611, "macro/precision": 0.6366190072656497, "per_entity_metric": {"corporation": {"f1": 0.5960591133004925, "f1_ci": {"90": [0.5395060701236211, 0.6494117647058822], "95": [0.5253672975902609, 0.6620697527948355]}, "precision": 0.5627906976744186, "recall": 0.6335078534031413}, "creative_work": {"f1": 0.5341246290801188, "f1_ci": {"90": [0.47351444952608207, 0.5861169548093196], "95": [0.4651130219529672, 0.5969428423543331]}, "precision": 0.569620253164557, "recall": 0.5027932960893855}, "event": {"f1": 0.45136186770428016, "f1_ci": {"90": [0.39823686811875786, 0.5019271027404221], "95": [0.3909613718310647, 0.5127203344600605]}, "precision": 0.46586345381526106, "recall": 0.4377358490566038}, "group": {"f1": 0.5533453887884269, "f1_ci": {"90": [0.4990720961035416, 0.6060777264167093], "95": [0.48433459051724126, 0.6135325916276041]}, "precision": 0.6322314049586777, "recall": 0.4919614147909968}, "location": {"f1": 0.6361031518624641, "f1_ci": {"90": [0.5650739843960184, 0.6985154228855721], "95": [0.5545887898306531, 0.7110143602918171]}, "precision": 0.6032608695652174, "recall": 0.6727272727272727}, "person": {"f1": 0.8492678725236864, "f1_ci": {"90": [0.8247403953294568, 0.8714447733934279], "95": [0.8194677632518109, 0.8756164968824685]}, "precision": 0.8725663716814159, "recall": 0.8271812080536913}, "product": {"f1": 0.6581632653061223, "f1_ci": {"90": [0.6046445933517278, 0.7088196286472149], "95": [0.5933362116697867, 0.7185306985239963]}, "precision": 0.75, "recall": 0.5863636363636363}}}, "2021.test (span detection)": {"micro/f1": 0.77377161055505, "micro/f1_ci": {}, "micro/recall": 0.7867468486180178, "micro/precision": 0.7612174107642385, "macro/f1": 0.77377161055505, "macro/f1_ci": {}, "macro/recall": 0.7867468486180178, "macro/precision": 0.7612174107642385}, "2020.test (span detection)": {"micro/f1": 0.7636755591484775, "micro/f1_ci": {}, "micro/recall": 0.7353399065905553, "micro/precision": 0.7942825112107623, "macro/f1": 0.7636755591484775, "macro/f1_ci": {}, "macro/recall": 0.7353399065905553, "macro/precision": 0.7942825112107623}}

eval/metric.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6535560344827587, "micro/f1_ci": {"90": [0.6322110846801919, 0.6725291873276269], "95": [0.6284137294993963, 0.6757531238447321]}, "micro/recall": 0.6294758692267773, "micro/precision": 0.6795518207282913, "macro/f1": 0.6112036126522273, "macro/f1_ci": {"90": [0.5885812318209095, 0.6302682399052447], "95": [0.5851527157984716, 0.6350317949646983]}, "macro/recall": 0.5931815043549611, "macro/precision": 0.6366190072656497, "per_entity_metric": {"corporation": {"f1": 0.5960591133004925, "f1_ci": {"90": [0.5395060701236211, 0.6494117647058822], "95": [0.5253672975902609, 0.6620697527948355]}, "precision": 0.5627906976744186, "recall": 0.6335078534031413}, "creative_work": {"f1": 0.5341246290801188, "f1_ci": {"90": [0.47351444952608207, 0.5861169548093196], "95": [0.4651130219529672, 0.5969428423543331]}, "precision": 0.569620253164557, "recall": 0.5027932960893855}, "event": {"f1": 0.45136186770428016, "f1_ci": {"90": [0.39823686811875786, 0.5019271027404221], "95": [0.3909613718310647, 0.5127203344600605]}, "precision": 0.46586345381526106, "recall": 0.4377358490566038}, "group": {"f1": 0.5533453887884269, "f1_ci": {"90": [0.4990720961035416, 0.6060777264167093], "95": [0.48433459051724126, 0.6135325916276041]}, "precision": 0.6322314049586777, "recall": 0.4919614147909968}, "location": {"f1": 0.6361031518624641, "f1_ci": {"90": [0.5650739843960184, 0.6985154228855721], "95": [0.5545887898306531, 0.7110143602918171]}, "precision": 0.6032608695652174, "recall": 0.6727272727272727}, "person": {"f1": 0.8492678725236864, "f1_ci": {"90": [0.8247403953294568, 0.8714447733934279], "95": [0.8194677632518109, 0.8756164968824685]}, "precision": 0.8725663716814159, "recall": 0.8271812080536913}, "product": {"f1": 0.6581632653061223, "f1_ci": {"90": [0.6046445933517278, 0.7088196286472149], "95": [0.5933362116697867, 0.7185306985239963]}, "precision": 0.75, "recall": 0.5863636363636363}}}

eval/metric.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.6417969860676713, "micro/f1_ci": {"90": [0.6334648803400447, 0.651188450223803], "95": [0.6314263719566943, 0.6528797499551452]}, "micro/recall": 0.6525208140610546, "micro/precision": 0.6314199395770392, "macro/f1": 0.5950190138355756, "macro/f1_ci": {"90": [0.5855854380997908, 0.6046272067604185], "95": [0.5837653478173793, 0.606080435069187]}, "macro/recall": 0.6100191042323923, "macro/precision": 0.5844336783514947, "per_entity_metric": {"corporation": {"f1": 0.5161953727506428, "f1_ci": {"90": [0.4927218605886716, 0.5414831199725443], "95": [0.4870848768801742, 0.547844877649912]}, "precision": 0.48038277511961724, "recall": 0.5577777777777778}, "creative_work": {"f1": 0.4749841671944269, "f1_ci": {"90": [0.44315016015693665, 0.5054160340115397], "95": [0.4373215286098878, 0.5122643035682656]}, "precision": 0.44221698113207547, "recall": 0.5129958960328317}, "event": {"f1": 0.43429109750353273, "f1_ci": {"90": [0.4106041087816789, 0.45832656118533116], "95": [0.40788582234365367, 0.46288163349626044]}, "precision": 0.4501953125, "recall": 0.4194722474977252}, "group": {"f1": 0.593413759373981, "f1_ci": {"90": [0.5729480380392004, 0.6136009860444586], "95": [0.569351806541065, 0.6194689670779012]}, "precision": 0.5874757908327953, "recall": 0.5994729907773386}, "location": {"f1": 0.6431718061674009, "f1_ci": {"90": [0.6159219058444587, 0.6695603128406853], "95": [0.6090556482248325, 0.6734092369794471]}, "precision": 0.5853379152348225, "recall": 0.7136871508379888}, "person": {"f1": 0.8327532515112659, "f1_ci": {"90": [0.8230547559590614, 0.8433818253656356], "95": [0.8211029923755827, 0.8457833808000503]}, "precision": 0.827448125227521, "recall": 0.8381268436578171}, "product": {"f1": 0.6703236423477785, "f1_ci": {"90": [0.6478723110217235, 0.6911223620805819], "95": [0.6441389195232122, 0.6947141029343396]}, "precision": 0.717978848413631, "recall": 0.6286008230452675}}}

eval/metric_span.test_2020.json

@@ -0,0 +1 @@
+{"micro/f1": 0.7636755591484775, "micro/f1_ci": {}, "micro/recall": 0.7353399065905553, "micro/precision": 0.7942825112107623, "macro/f1": 0.7636755591484775, "macro/f1_ci": {}, "macro/recall": 0.7353399065905553, "macro/precision": 0.7942825112107623}

eval/metric_span.test_2021.json

@@ -0,0 +1 @@
+{"micro/f1": 0.77377161055505, "micro/f1_ci": {}, "micro/recall": 0.7867468486180178, "micro/precision": 0.7612174107642385, "macro/f1": 0.77377161055505, "macro/f1_ci": {}, "macro/recall": 0.7867468486180178, "macro/precision": 0.7612174107642385}

trainer_config.json

@@ -1 +1 @@
-{"data_split": "2020.train", "model": "cardiffnlp/twitter-roberta-base-dec2021", "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-dec2021", "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}