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| library_name
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tner/bertweet-base-tweetner7-2021
|
tner
| 2022-09-27T15:23:18Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-09T21:16:21Z |
---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/bertweet-base-tweetner7-2021
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: 0.6308962917798349
- name: Precision (test_2021)
type: precision
value: 0.6058767167039285
- name: Recall (test_2021)
type: recall
value: 0.6580712303422757
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.5735468406550763
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.5503198173085064
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.6012922054817469
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7788214245778822
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7538694663924668
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.8054816699433329
- name: F1 (test_2020)
type: f1
value: 0.6205787781350482
- name: Precision (test_2020)
type: precision
value: 0.6415512465373961
- name: Recall (test_2020)
type: recall
value: 0.6009340944473275
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.5723158793505982
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.5910271170769507
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5568451570610017
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7595141700404859
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7913385826771654
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.7301504929942917
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/bertweet-base-tweetner7-2021
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` 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): 0.6308962917798349
- Precision (micro): 0.6058767167039285
- Recall (micro): 0.6580712303422757
- F1 (macro): 0.5735468406550763
- Precision (macro): 0.5503198173085064
- Recall (macro): 0.6012922054817469
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.4565701559020044
- creative_work: 0.4098984771573604
- event: 0.4628410159924742
- group: 0.593177511054959
- location: 0.6333949476278496
- person: 0.8279457768508863
- product: 0.631
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6218627510838193, 0.6407164862470697]
- 95%: [0.6201627010426306, 0.6422908401462293]
- F1 (macro):
- 90%: [0.6218627510838193, 0.6407164862470697]
- 95%: [0.6201627010426306, 0.6422908401462293]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bertweet-base-tweetner7-2021/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/bertweet-base-tweetner7-2021/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
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/bertweet-base-tweetner7-2021")
model.predict([text_format])
```
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_2021
- dataset_name: None
- local_dataset: None
- model: vinai/bertweet-base
- crf: False
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 0.0001
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.3
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bertweet-base-tweetner7-2021/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@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.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
tner/bert-large-tweetner7-random
|
tner
| 2022-09-27T15:22:53Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-11T11:22:19Z |
---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/bert-large-tweetner7-random
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: 0.6238958623895862
- name: Precision (test_2021)
type: precision
value: 0.6271028037383177
- name: Recall (test_2021)
type: recall
value: 0.6207215541165587
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.5754103658637805
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.5760445653768616
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.5751041088351385
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7648665930360984
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7689340813464236
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.760841910489187
- name: F1 (test_2020)
type: f1
value: 0.6154274575327208
- name: Precision (test_2020)
type: precision
value: 0.6640625
- name: Recall (test_2020)
type: recall
value: 0.5734302023871303
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.5709159092071027
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.6168953196783556
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5321784485961766
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.732943469785575
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7908653846153846
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.6829268292682927
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/bert-large-tweetner7-random
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_random` 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): 0.6238958623895862
- Precision (micro): 0.6271028037383177
- Recall (micro): 0.6207215541165587
- F1 (macro): 0.5754103658637805
- Precision (macro): 0.5760445653768616
- Recall (macro): 0.5751041088351385
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.49146005509641877
- creative_work: 0.3972413793103448
- event: 0.44788732394366193
- group: 0.5767073573078192
- location: 0.6721649484536083
- person: 0.8116810183451891
- product: 0.6307304785894207
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6141711573096726, 0.6329835528622229]
- 95%: [0.6127880547187768, 0.6345997209553179]
- F1 (macro):
- 90%: [0.6141711573096726, 0.6329835528622229]
- 95%: [0.6127880547187768, 0.6345997209553179]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-large-tweetner7-random/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/bert-large-tweetner7-random/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
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/bert-large-tweetner7-random")
model.predict([text_format])
```
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_random
- dataset_name: None
- local_dataset: None
- model: bert-large-cased
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 0.0001
- 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/bert-large-tweetner7-random/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@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.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
tner/bert-large-tweetner7-2021
|
tner
| 2022-09-27T15:21:36Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-12T09:24:07Z |
---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/bert-large-tweetner7-2021
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: 0.5974718775368201
- name: Precision (test_2021)
type: precision
value: 0.5992091183996279
- name: Recall (test_2021)
type: recall
value: 0.5957446808510638
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.5392877076670867
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.5398425980592713
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.5439768272225339
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7497514474530674
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7584003786086133
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.7412975598473459
- name: F1 (test_2020)
type: f1
value: 0.5662616558349817
- name: Precision (test_2020)
type: precision
value: 0.6215880893300249
- name: Recall (test_2020)
type: recall
value: 0.519979242345615
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.5096985017746614
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.5628721370469417
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.47520198274721537
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7065868263473053
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7841772151898734
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.6429683445770628
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/bert-large-tweetner7-2021
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` 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): 0.5974718775368201
- Precision (micro): 0.5992091183996279
- Recall (micro): 0.5957446808510638
- F1 (macro): 0.5392877076670867
- Precision (macro): 0.5398425980592713
- Recall (macro): 0.5439768272225339
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.4486772486772486
- creative_work: 0.34173228346456697
- event: 0.40238450074515647
- group: 0.556795797767564
- location: 0.6394904458598726
- person: 0.7940364439536168
- product: 0.5918972332015809
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.5884763705775744, 0.6075466841645367]
- 95%: [0.586724466800271, 0.6087071446445204]
- F1 (macro):
- 90%: [0.5884763705775744, 0.6075466841645367]
- 95%: [0.586724466800271, 0.6087071446445204]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-large-tweetner7-2021/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/bert-large-tweetner7-2021/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
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/bert-large-tweetner7-2021")
model.predict([text_format])
```
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_2021
- dataset_name: None
- local_dataset: None
- model: bert-large-cased
- crf: False
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 0.0001
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.3
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-large-tweetner7-2021/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@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.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
tner/bert-base-tweetner7-2020
|
tner
| 2022-09-27T15:20:02Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"dataset:tner/tweetner7",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-02T18:56:46Z |
---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/bert-base-tweetner7-2020
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: 0.6008989019741707
- name: Precision (test_2021)
type: precision
value: 0.591443610706686
- name: Recall (test_2021)
type: recall
value: 0.6106614246068455
- name: Macro F1 (test_2021)
type: f1_macro
value: 0.5467450408285621
- name: Macro Precision (test_2021)
type: precision_macro
value: 0.537717358363018
- name: Macro Recall (test_2021)
type: recall_macro
value: 0.5582367980568581
- name: Entity Span F1 (test_2021)
type: f1_entity_span
value: 0.7560892328704758
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.744313725490196
- name: Entity Span Recall (test_2021)
type: recall_entity_span
value: 0.7682433213831387
- name: F1 (test_2020)
type: f1
value: 0.6087425796006476
- name: Precision (test_2020)
type: precision
value: 0.6340640809443507
- name: Recall (test_2020)
type: recall
value: 0.5853658536585366
- name: Macro F1 (test_2020)
type: f1_macro
value: 0.5648877924450979
- name: Macro Precision (test_2020)
type: precision_macro
value: 0.5930039411771633
- name: Macro Recall (test_2020)
type: recall_macro
value: 0.5426595099078766
- name: Entity Span F1 (test_2020)
type: f1_entity_span
value: 0.7242309767943875
- name: Entity Span Precision (test_2020)
type: precision_entity_span
value: 0.7543563799887577
- name: Entity Span Recall (test_2020)
type: recall_entity_span
value: 0.6964193046185781
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/bert-base-tweetner7-2020
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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): 0.6008989019741707
- Precision (micro): 0.591443610706686
- Recall (micro): 0.6106614246068455
- F1 (macro): 0.5467450408285621
- Precision (macro): 0.537717358363018
- Recall (macro): 0.5582367980568581
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.4411294619072989
- creative_work: 0.3751552795031057
- event: 0.40279069767441866
- group: 0.5576791808873721
- location: 0.6179921773142112
- person: 0.8051622154507977
- product: 0.6273062730627307
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.5924664556782363, 0.6106294776916564]
- 95%: [0.5905572257793882, 0.6119935888266077]
- F1 (macro):
- 90%: [0.5924664556782363, 0.6106294776916564]
- 95%: [0.5905572257793882, 0.6119935888266077]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-2020/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/bert-base-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
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.
```python
import re
from urlextract import URLExtract
from tner import TransformersNER
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/bert-base-tweetner7-2020")
model.predict([text_format])
```
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: bert-base-cased
- 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/bert-base-tweetner7-2020/raw/main/trainer_config.json).
### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```
@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.",
}
```
- TweetNER7
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
gabrielsgaspar/bert-base-uncased-emotions-augmented
|
gabrielsgaspar
| 2022-09-27T15:13:36Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-27T14:00:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-emotions-augmented
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-emotions-augmented
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9815
- Accuracy: 0.7539
- F1: 0.7506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8475 | 1.0 | 819 | 0.6336 | 0.7655 | 0.7651 |
| 0.5594 | 2.0 | 1638 | 0.6109 | 0.7695 | 0.7680 |
| 0.4596 | 3.0 | 2457 | 0.6528 | 0.7601 | 0.7556 |
| 0.3663 | 4.0 | 3276 | 0.6992 | 0.7631 | 0.7612 |
| 0.2809 | 5.0 | 4095 | 0.7773 | 0.7571 | 0.7542 |
| 0.2142 | 6.0 | 4914 | 0.8879 | 0.7541 | 0.7504 |
| 0.1671 | 7.0 | 5733 | 0.9476 | 0.7552 | 0.7517 |
| 0.1416 | 8.0 | 6552 | 0.9815 | 0.7539 | 0.7506 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
anas-awadalla/t5-small-few-shot-k-512-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-27T15:10:37Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T15:02:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-512-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
sd-concepts-library/felps
|
sd-concepts-library
| 2022-09-27T15:06:32Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-27T15:06:26Z |
---
license: mit
---
### Felps on Stable Diffusion
This is the `<Felps>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
IIIT-L/xlm-roberta-base-finetuned-TRAC-DS
|
IIIT-L
| 2022-09-27T14:41:12Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-09T07:50:14Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-base-finetuned-TRAC-DS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-TRAC-DS
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0206
- Accuracy: 0.6814
- Precision: 0.6561
- Recall: 0.6528
- F1: 0.6543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9928 | 0.5 | 612 | 0.9026 | 0.6201 | 0.5845 | 0.5812 | 0.5809 |
| 0.8756 | 1.0 | 1224 | 0.7883 | 0.6373 | 0.6358 | 0.6382 | 0.6251 |
| 0.7793 | 1.5 | 1836 | 0.8551 | 0.6340 | 0.6226 | 0.6368 | 0.6020 |
| 0.7667 | 2.0 | 2448 | 0.7861 | 0.6618 | 0.6518 | 0.6637 | 0.6442 |
| 0.6619 | 2.5 | 3060 | 0.8597 | 0.6887 | 0.6662 | 0.6472 | 0.6503 |
| 0.6786 | 3.0 | 3672 | 0.7905 | 0.6634 | 0.6587 | 0.6658 | 0.6513 |
| 0.573 | 3.5 | 4284 | 0.9263 | 0.6797 | 0.6575 | 0.6488 | 0.6514 |
| 0.5805 | 4.0 | 4896 | 0.8351 | 0.6944 | 0.6719 | 0.6740 | 0.6723 |
| 0.5069 | 4.5 | 5508 | 0.9772 | 0.6748 | 0.6564 | 0.6572 | 0.6546 |
| 0.5085 | 5.0 | 6120 | 1.0206 | 0.6814 | 0.6561 | 0.6528 | 0.6543 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
anas-awadalla/t5-small-few-shot-k-128-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-27T14:19:03Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T13:59:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-128-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-small-few-shot-k-128-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-27T13:58:03Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T13:54:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-128-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-small-few-shot-k-64-finetuned-squad-seed-4
|
anas-awadalla
| 2022-09-27T13:52:23Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T13:49:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-64-finetuned-squad-seed-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-64-finetuned-squad-seed-4
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Najeen/marian-finetuned-kde4-en-to-fr
|
Najeen
| 2022-09-27T13:50:11Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-27T02:54:56Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.83113187001415
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8560
- Bleu: 52.8311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
huynguyen208/bert-base-multilingual-cased-finetuned-ner
|
huynguyen208
| 2022-09-27T12:43:41Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-25T12:10:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0247
- Precision: 0.9269
- Recall: 0.9509
- F1: 0.9387
- Accuracy: 0.9945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0744 | 1.0 | 843 | 0.0266 | 0.8945 | 0.9293 | 0.9116 | 0.9920 |
| 0.016 | 2.0 | 1686 | 0.0239 | 0.9279 | 0.9446 | 0.9362 | 0.9942 |
| 0.0075 | 3.0 | 2529 | 0.0247 | 0.9269 | 0.9509 | 0.9387 | 0.9945 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
anas-awadalla/t5-small-few-shot-k-32-finetuned-squad-seed-0
|
anas-awadalla
| 2022-09-27T12:43:36Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T12:35:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-32-finetuned-squad-seed-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
anas-awadalla/t5-small-few-shot-k-16-finetuned-squad-seed-2
|
anas-awadalla
| 2022-09-27T12:24:59Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-27T12:18:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: t5-small-few-shot-k-16-finetuned-squad-seed-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
Hoax0930/kyoto_marian_mod_2_1
|
Hoax0930
| 2022-09-27T11:09:17Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-27T09:18:33Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_mod_2_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kyoto_marian_mod_2_1
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2_0](https://huggingface.co/Hoax0930/kyoto_marian_mod_2_0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2568
- Bleu: 20.9923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ericntay/stbl_clinical_bert_ft_rs6
|
ericntay
| 2022-09-27T09:57:00Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-27T09:38:14Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: stbl_clinical_bert_ft_rs6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# stbl_clinical_bert_ft_rs6
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0876
- F1: 0.9177
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2778 | 1.0 | 101 | 0.0871 | 0.8482 |
| 0.066 | 2.0 | 202 | 0.0700 | 0.8892 |
| 0.031 | 3.0 | 303 | 0.0657 | 0.9053 |
| 0.0152 | 4.0 | 404 | 0.0716 | 0.9057 |
| 0.0099 | 5.0 | 505 | 0.0717 | 0.9105 |
| 0.0049 | 6.0 | 606 | 0.0807 | 0.9145 |
| 0.0042 | 7.0 | 707 | 0.0796 | 0.9140 |
| 0.0028 | 8.0 | 808 | 0.0833 | 0.9140 |
| 0.002 | 9.0 | 909 | 0.0836 | 0.9141 |
| 0.0013 | 10.0 | 1010 | 0.0866 | 0.9177 |
| 0.0011 | 11.0 | 1111 | 0.0867 | 0.9178 |
| 0.001 | 12.0 | 1212 | 0.0876 | 0.9177 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
hadiqa123/XLS-R_timit_en
|
hadiqa123
| 2022-09-27T09:26:46Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-22T05:39:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: XLS-R_timit_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R_timit_en
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3799
- Wer: 0.3019
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5228 | 3.3 | 1000 | 0.9889 | 0.8394 |
| 0.6617 | 6.6 | 2000 | 0.3566 | 0.4027 |
| 0.3177 | 9.9 | 3000 | 0.3112 | 0.3606 |
| 0.2262 | 13.2 | 4000 | 0.3521 | 0.3324 |
| 0.1683 | 16.5 | 5000 | 0.3563 | 0.3260 |
| 0.137 | 19.8 | 6000 | 0.3605 | 0.3149 |
| 0.1139 | 23.1 | 7000 | 0.3768 | 0.3069 |
| 0.1068 | 26.4 | 8000 | 0.3643 | 0.3044 |
| 0.0897 | 29.7 | 9000 | 0.3799 | 0.3019 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
Hoax0930/kyoto_marian_mod_2_0
|
Hoax0930
| 2022-09-27T09:07:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-09-27T07:15:28Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kyoto_marian_mod_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kyoto_marian_mod_3
This model is a fine-tuned version of [Hoax0930/kyoto_marian_mod_2](https://huggingface.co/Hoax0930/kyoto_marian_mod_2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2477
- Bleu: 19.9506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/rishusei-style
|
sd-concepts-library
| 2022-09-27T08:48:42Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-27T08:48:36Z |
---
license: mit
---
### Rishusei style on Stable Diffusion
This is the `<crishusei-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
nlpodyssey/bert-italian-uncased-iptc-headlines
|
nlpodyssey
| 2022-09-27T07:43:12Z | 104 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-23T07:34:20Z |
---
license: apache-2.0
language:
- it
tags:
- text-classification
---
# bert-italian-uncased-iptc-headlines
This a bert-base-italian-uncased model fine-tuned for news headlines classification in Italian.
It predicts the top-level category of the [IPTC subject taxonomy](https://cv.iptc.org/newscodes/subjectcode):
| Class | English label |
| -------- | ----------------- |
| 01000000 | Arts, Culture & Entertainment |
| 02000000 | Crime, Law & Justice |
| 03000000 | Disasters & Accidents |
| 04000000 | Economy, Business & Finance |
| 05000000 | Education |
| 06000000 | Environmental Issues |
| 07000000 | Health |
| 08000000 | Human Interest |
| 09000000 | Labour |
| 10000000 | Lifestyle & Leisure |
| 11000000 | Politics |
| 12000000 | Religion & Belief |
| 13000000 | Science & Technology |
| 14000000 | Social Issues |
| 15000000 | Sport |
| 16000000 | Unrest, Conflicts & War |
| 17000000 | Weather |
# Authors
The [NLP Odyssey](https://github.com/nlpodyssey/) Authors (Matteo Grella, Marco Nicola)
|
crescendonow/pwa_categorical_complaint
|
crescendonow
| 2022-09-27T07:42:44Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-27T07:24:36Z |
---
license: apache-2.0
---
This Model finetunes from WangchanBERTa ("wangchanberta-base-att-spm-uncased") uses only the Provincial Waterworks Authority of Thailand.
The Model classification into ten categories describe by the dictionary are
{'ข้อร้องเรียน-ปริมาณน้ำ':[11,0],
'ข้อร้องเรียน-ท่อแตกรั่ว':[12,1],
'ข้อร้องเรียน-คุณภาพน้ำ':[13,2],
'ข้อร้องเรียน-การบริการ':[14,3],
'ข้อร้องเรียน-บุคลากร':[15,4],
'ข้อสอบถามทั่วไป':[2,5],
'ข้อเสนอแนะ':[3,6],
'ข้อคิดเห็น':[4,7],
'อื่นๆ':[8,8],
'ไม่เกี่ยวข้องกับกปภ.':[9,9]}
|
albertdestajo/distilbert-base-uncased-finetuned-mrpc
|
albertdestajo
| 2022-09-27T05:40:59Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T18:31:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: train
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7916666666666666
- name: F1
type: f1
value: 0.8608837970540099
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4502
- Accuracy: 0.7917
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4474 | 1.0 | 230 | 0.4502 | 0.7917 | 0.8609 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
kerkathy/distilbert-base-uncased-finetuned-imdb
|
kerkathy
| 2022-09-27T04:57:38Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-27T04:50:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ricardmc99/Reinforce-CartPole-v1
|
Ricardmc99
| 2022-09-27T03:46:36Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-27T03:45:38Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 90.30 +/- 49.35
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
SmilestheSad/bert-base-multilingual-uncased-sep-26
|
SmilestheSad
| 2022-09-27T03:46:06Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-27T01:23:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-multilingual-uncased-sep-26
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-sep-26
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
- F1: 0.9369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0798 | 1.0 | 8623 | 0.0682 | 0.8979 |
| 0.0498 | 2.0 | 17246 | 0.0551 | 0.9270 |
| 0.0351 | 3.0 | 25869 | 0.0483 | 0.9369 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Ricardmc99/testpyramidsrnd
|
Ricardmc99
| 2022-09-27T02:57:53Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-09-27T02:57:47Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Ricardmc99/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sd-concepts-library/happy-person12345
|
sd-concepts-library
| 2022-09-27T02:56:00Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-27T02:55:55Z |
---
license: mit
---
### Happy_Person12345 on Stable Diffusion
This is the `<Happy-Person12345>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
sd-concepts-library/kawaii-girl-plus-object
|
sd-concepts-library
| 2022-09-26T22:23:30Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T22:23:21Z |
---
license: mit
---
### kawaii_girl_plus_object on Stable Diffusion
This is the `<kawaii_girl>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
ammarpl/t5-base-finetuned-eli5-a
|
ammarpl
| 2022-09-26T22:02:48Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-26T19:36:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-eli5-a
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 14.6711
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-eli5-a
This model is a fine-tuned version of [ammarpl/t5-base-finetuned-xsum-a](https://huggingface.co/ammarpl/t5-base-finetuned-xsum-a) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1773
- Rouge1: 14.6711
- Rouge2: 2.2878
- Rougel: 11.3676
- Rougelsum: 13.1805
- Gen Len: 18.9892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.3417 | 1.0 | 17040 | 3.1773 | 14.6711 | 2.2878 | 11.3676 | 13.1805 | 18.9892 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
enaserian/distilbert-base-uncased-finetuned
|
enaserian
| 2022-09-26T20:11:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-23T10:58:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.2813
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.6309 | 1.0 | 76 | 7.4774 |
| 7.0806 | 2.0 | 152 | 6.9937 |
| 6.6842 | 3.0 | 228 | 6.9314 |
| 6.4592 | 4.0 | 304 | 6.9088 |
| 6.2936 | 5.0 | 380 | 6.9135 |
| 6.1301 | 6.0 | 456 | 6.9018 |
| 5.9878 | 7.0 | 532 | 6.8865 |
| 5.8071 | 8.0 | 608 | 6.8926 |
| 5.6372 | 9.0 | 684 | 6.8750 |
| 5.4791 | 10.0 | 760 | 6.9394 |
| 5.3365 | 11.0 | 836 | 6.9594 |
| 5.2117 | 12.0 | 912 | 6.9962 |
| 5.0887 | 13.0 | 988 | 7.0570 |
| 4.9288 | 14.0 | 1064 | 7.0549 |
| 4.8169 | 15.0 | 1140 | 7.0971 |
| 4.7008 | 16.0 | 1216 | 7.1439 |
| 4.6149 | 17.0 | 1292 | 7.1320 |
| 4.487 | 18.0 | 1368 | 7.1577 |
| 4.364 | 19.0 | 1444 | 7.1712 |
| 4.3208 | 20.0 | 1520 | 7.1959 |
| 4.2492 | 21.0 | 1596 | 7.2136 |
| 4.1423 | 22.0 | 1672 | 7.2304 |
| 4.0873 | 23.0 | 1748 | 7.2526 |
| 4.0261 | 24.0 | 1824 | 7.2681 |
| 3.9598 | 25.0 | 1900 | 7.2715 |
| 3.9562 | 26.0 | 1976 | 7.2648 |
| 3.8951 | 27.0 | 2052 | 7.2665 |
| 3.8772 | 28.0 | 2128 | 7.2781 |
| 3.8403 | 29.0 | 2204 | 7.2801 |
| 3.8275 | 30.0 | 2280 | 7.2813 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BumblingOrange/Hiten
|
BumblingOrange
| 2022-09-26T19:26:04Z | 0 | 35 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-09-26T18:50:36Z |
---
license: bigscience-bloom-rail-1.0
---
Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion
Custom Dreambooth model based off of the artist Hiten. Dataset was 6 training images, and 34 regularization images. Trained for 4000 steps.
To use the model, simply insert the name 'Hiten' into your prompts. The class token used was 'girl_anime_8k_wallpaper'.
Append the class token after Hiten for stronger results.
EX: "1girl by Hiten girl_anime_8k_wallpaper"
|
sd-concepts-library/kira-sensei
|
sd-concepts-library
| 2022-09-26T19:25:20Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T19:25:07Z |
---
license: mit
---
### kira-sensei on Stable Diffusion
This is the `<kira-sensei>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
mrm8488/setfit-mpnet-base-v2-finetuned-sentEval-CR
|
mrm8488
| 2022-09-26T18:50:11Z | 7 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-26T18:49:59Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 20,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
LucasBorth/jurisbert-base-classify
|
LucasBorth
| 2022-09-26T18:16:34Z | 101 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T18:12:23Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: jurisbert-base-classify
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# jurisbert-base-classify
This model is a fine-tuned version of [juridics/jurisbert-base-portuguese-uncased](https://huggingface.co/juridics/jurisbert-base-portuguese-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4893
- Accuracy: 0.8991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
espnet/transformer_tts_cmu_indic_hin_ab
|
espnet
| 2022-09-26T18:15:07Z | 0 | 2 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"hi",
"dataset:cmu_indic",
"region:us"
] |
text-to-speech
| 2022-09-26T18:02:38Z |
---
tags:
- espnet
- audio
- text-to-speech
language: hi
datasets:
- cmu_indic
---
|
sd-concepts-library/fairytale
|
sd-concepts-library
| 2022-09-26T17:23:45Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T17:23:44Z |
---
license: mit
---
### fAIrytale on Stable Diffusion
This is the `<fAIrytale>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
ammarpl/t5-small-finetuned-xsum
|
ammarpl
| 2022-09-26T16:38:17Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-25T16:48:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-finetuned-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 0.01 | 128 | 3.0141 | 18.0313 | 2.7105 | 14.1325 | 14.3393 | 18.8882 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
pjcordero04/distilbert-base-uncased-finetuned-cola
|
pjcordero04
| 2022-09-26T16:32:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-26T14:35:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5442538936990396
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8348
- Matthews Correlation: 0.5443
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5236 | 1.0 | 535 | 0.5495 | 0.4205 |
| 0.3505 | 2.0 | 1070 | 0.5176 | 0.4977 |
| 0.2401 | 3.0 | 1605 | 0.5498 | 0.5354 |
| 0.1751 | 4.0 | 2140 | 0.7975 | 0.5270 |
| 0.1229 | 5.0 | 2675 | 0.8348 | 0.5443 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
shoang/wav2vec2-base-timit-demo-google-colab
|
shoang
| 2022-09-26T16:25:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-26T14:27:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5218
- Wer: 0.3434
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5634 | 1.0 | 500 | 2.0727 | 1.0096 |
| 0.9357 | 2.01 | 1000 | 0.6623 | 0.5634 |
| 0.4536 | 3.01 | 1500 | 1.4421 | 0.4829 |
| 0.3044 | 4.02 | 2000 | 0.4361 | 0.4363 |
| 0.2369 | 5.02 | 2500 | 0.5098 | 0.4495 |
| 0.1994 | 6.02 | 3000 | 0.4741 | 0.3711 |
| 0.1699 | 7.03 | 3500 | 0.4652 | 0.3898 |
| 0.1499 | 8.03 | 4000 | 0.4151 | 0.3949 |
| 0.1308 | 9.04 | 4500 | 0.4685 | 0.3838 |
| 0.1234 | 10.04 | 5000 | 0.5076 | 0.3794 |
| 0.1055 | 11.04 | 5500 | 0.4492 | 0.3790 |
| 0.0953 | 12.05 | 6000 | 0.4726 | 0.3679 |
| 0.0863 | 13.05 | 6500 | 0.4797 | 0.3717 |
| 0.0816 | 14.06 | 7000 | 0.4725 | 0.3655 |
| 0.0842 | 15.06 | 7500 | 0.5181 | 0.3405 |
| 0.0661 | 16.06 | 8000 | 0.5315 | 0.3510 |
| 0.0593 | 17.07 | 8500 | 0.5024 | 0.3668 |
| 0.0624 | 18.07 | 9000 | 0.5374 | 0.3663 |
| 0.0535 | 19.08 | 9500 | 0.4861 | 0.3517 |
| 0.0524 | 20.08 | 10000 | 0.4812 | 0.3574 |
| 0.0461 | 21.08 | 10500 | 0.4976 | 0.3431 |
| 0.0363 | 22.09 | 11000 | 0.5062 | 0.3476 |
| 0.0351 | 23.09 | 11500 | 0.5094 | 0.3479 |
| 0.0327 | 24.1 | 12000 | 0.5291 | 0.3455 |
| 0.0319 | 25.1 | 12500 | 0.5209 | 0.3460 |
| 0.0268 | 26.1 | 13000 | 0.5173 | 0.3481 |
| 0.0263 | 27.11 | 13500 | 0.5362 | 0.3486 |
| 0.0234 | 28.11 | 14000 | 0.5333 | 0.3444 |
| 0.0237 | 29.12 | 14500 | 0.5218 | 0.3434 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0
|
tner/deberta-v3-large-wnut2017
|
tner
| 2022-09-26T15:10:46Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:14:32Z |
---
datasets:
- tner/wnut2017
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-wnut2017
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/wnut2017
type: tner/wnut2017
args: tner/wnut2017
metrics:
- name: F1
type: f1
value: 0.5047353760445682
- name: Precision
type: precision
value: 0.63268156424581
- name: Recall
type: recall
value: 0.4198331788693234
- name: F1 (macro)
type: f1_macro
value: 0.4165125500830091
- name: Precision (macro)
type: precision_macro
value: 0.5356144444686111
- name: Recall (macro)
type: recall_macro
value: 0.3573954549633822
- name: F1 (entity span)
type: f1_entity_span
value: 0.6249999999999999
- name: Precision (entity span)
type: precision_entity_span
value: 0.7962697274031564
- name: Recall (entity span)
type: recall_entity_span
value: 0.5143651529193698
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-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): 0.5047353760445682
- Precision (micro): 0.63268156424581
- Recall (micro): 0.4198331788693234
- F1 (macro): 0.4165125500830091
- Precision (macro): 0.5356144444686111
- Recall (macro): 0.3573954549633822
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.25477707006369427
- group: 0.34309623430962344
- location: 0.6187050359712232
- person: 0.6721763085399448
- product: 0.18579234972677597
- work_of_art: 0.42452830188679247
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
- F1 (macro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/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/deberta-v3-large-wnut2017")
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/wnut2017']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-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/deberta-v3-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.",
}
```
|
tner/deberta-v3-large-ttc
|
tner
| 2022-09-26T14:41:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/ttc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-17T11:20:57Z |
---
datasets:
- tner/ttc
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-ttc
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ttc
type: tner/ttc
args: tner/ttc
metrics:
- name: F1
type: f1
value: 0.8266925817946227
- name: Precision
type: precision
value: 0.8264248704663213
- name: Recall
type: recall
value: 0.8269604666234608
- name: F1 (macro)
type: f1_macro
value: 0.8267742072572187
- name: Precision (macro)
type: precision_macro
value: 0.8278533291801137
- name: Recall (macro)
type: recall_macro
value: 0.8257668793195109
- name: F1 (entity span)
type: f1_entity_span
value: 0.8713961775186264
- name: Precision (entity span)
type: precision_entity_span
value: 0.8711139896373057
- name: Recall (entity span)
type: recall_entity_span
value: 0.8716785482825664
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-ttc
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/ttc](https://huggingface.co/datasets/tner/ttc) 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): 0.8266925817946227
- Precision (micro): 0.8264248704663213
- Recall (micro): 0.8269604666234608
- F1 (macro): 0.8267742072572187
- Precision (macro): 0.8278533291801137
- Recall (macro): 0.8257668793195109
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7862266857962696
- organization: 0.7770320656226697
- person: 0.9170638703527169
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8124223893760291, 0.8416139230675236]
- 95%: [0.8098712905029445, 0.8440240645643514]
- F1 (macro):
- 90%: [0.8124223893760291, 0.8416139230675236]
- 95%: [0.8098712905029445, 0.8440240645643514]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-ttc/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-ttc/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/deberta-v3-large-ttc")
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/ttc']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- 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/deberta-v3-large-ttc/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.",
}
```
|
tner/deberta-v3-large-tweebank-ner
|
tner
| 2022-09-26T14:39:17Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-10T10:07:10Z |
---
datasets:
- tner/tweebank_ner
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-tweebank-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweebank_ner
type: tner/tweebank_ner
args: tner/tweebank_ner
metrics:
- name: F1
type: f1
value: 0.7253474520185308
- name: Precision
type: precision
value: 0.7201051248357424
- name: Recall
type: recall
value: 0.7306666666666667
- name: F1 (macro)
type: f1_macro
value: 0.701874697798745
- name: Precision (macro)
type: precision_macro
value: 0.7043005470796733
- name: Recall (macro)
type: recall_macro
value: 0.706915721861374
- name: F1 (entity span)
type: f1_entity_span
value: 0.8178343949044585
- name: Precision (entity span)
type: precision_entity_span
value: 0.7829268292682927
- name: Recall (entity span)
type: recall_entity_span
value: 0.856
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-tweebank-ner
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) 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): 0.7253474520185308
- Precision (micro): 0.7201051248357424
- Recall (micro): 0.7306666666666667
- F1 (macro): 0.701874697798745
- Precision (macro): 0.7043005470796733
- Recall (macro): 0.706915721861374
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7289719626168224
- organization: 0.7040816326530612
- other: 0.5182926829268293
- person: 0.856152512998267
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]
- F1 (macro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-tweebank-ner/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/deberta-v3-large-tweebank-ner")
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/tweebank_ner']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- 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/deberta-v3-large-tweebank-ner/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.",
}
```
|
tner/deberta-v3-large-mit-movie-trivia
|
tner
| 2022-09-26T14:30:39Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/mit_movie_trivia",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T11:41:52Z |
---
datasets:
- tner/mit_movie_trivia
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-mit-movie-trivia
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_movie_trivia
type: tner/mit_movie_trivia
args: tner/mit_movie_trivia
metrics:
- name: F1
type: f1
value: 0.7324478178368122
- name: Precision
type: precision
value: 0.7186865267433988
- name: Recall
type: recall
value: 0.746746394653535
- name: F1 (macro)
type: f1_macro
value: 0.6597589403836301
- name: Precision (macro)
type: precision_macro
value: 0.6493939604029393
- name: Recall (macro)
type: recall_macro
value: 0.6747458149186768
- name: F1 (entity span)
type: f1_entity_span
value: 0.749525289142068
- name: Precision (entity span)
type: precision_entity_span
value: 0.7359322033898306
- name: Recall (entity span)
type: recall_entity_span
value: 0.7636299683432993
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-mit-movie-trivia
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) 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): 0.7324478178368122
- Precision (micro): 0.7186865267433988
- Recall (micro): 0.746746394653535
- F1 (macro): 0.6597589403836301
- Precision (macro): 0.6493939604029393
- Recall (macro): 0.6747458149186768
The per-entity breakdown of the F1 score on the test set are below:
- actor: 0.9590417310664605
- award: 0.4755244755244755
- character_name: 0.7391304347826086
- date: 0.9640179910044978
- director: 0.909706546275395
- genre: 0.755114693118413
- opinion: 0.4910714285714286
- origin: 0.3922518159806296
- plot: 0.4929757343550447
- quote: 0.7391304347826088
- relationship: 0.5705705705705706
- soundtrack: 0.42857142857142855
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.7213456287685677, 0.742502895519075]
- 95%: [0.7198169787204788, 0.7460320515170399]
- F1 (macro):
- 90%: [0.7213456287685677, 0.742502895519075]
- 95%: [0.7198169787204788, 0.7460320515170399]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/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/deberta-v3-large-mit-movie-trivia")
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/mit_movie_trivia']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- 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: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-mit-movie-trivia/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.",
}
```
|
tner/deberta-large-wnut2017
|
tner
| 2022-09-26T14:29:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"token-classification",
"dataset:tner/wnut2017",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:25:51Z |
---
datasets:
- tner/wnut2017
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-large-wnut2017
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/wnut2017
type: tner/wnut2017
args: tner/wnut2017
metrics:
- name: F1
type: f1
value: 0.5105386416861827
- name: Precision
type: precision
value: 0.6931637519872814
- name: Recall
type: recall
value: 0.4040778498609824
- name: F1 (macro)
type: f1_macro
value: 0.4263428845085451
- name: Precision (macro)
type: precision_macro
value: 0.6003185137596864
- name: Recall (macro)
type: recall_macro
value: 0.35195768262641947
- name: F1 (entity span)
type: f1_entity_span
value: 0.5936768149882904
- name: Precision (entity span)
type: precision_entity_span
value: 0.8060413354531002
- name: Recall (entity span)
type: recall_entity_span
value: 0.46987951807228917
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-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): 0.5105386416861827
- Precision (micro): 0.6931637519872814
- Recall (micro): 0.4040778498609824
- F1 (macro): 0.4263428845085451
- Precision (macro): 0.6003185137596864
- Recall (macro): 0.35195768262641947
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.3503649635036496
- group: 0.3148148148148148
- location: 0.6029411764705882
- person: 0.6628895184135977
- product: 0.1951219512195122
- work_of_art: 0.431924882629108
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.47970650356554456, 0.5385161869734422]
- 95%: [0.47475901512925966, 0.5430870496346687]
- F1 (macro):
- 90%: [0.47970650356554456, 0.5385161869734422]
- 95%: [0.47475901512925966, 0.5430870496346687]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-large-wnut2017/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-large-wnut2017/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/deberta-large-wnut2017")
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/wnut2017']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-large
- crf: True
- 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/deberta-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.",
}
```
|
tner/deberta-v3-large-fin
|
tner
| 2022-09-26T14:28:32Z | 8 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/fin",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T22:13:20Z |
---
datasets:
- tner/fin
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-fin
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/fin
type: tner/fin
args: tner/fin
metrics:
- name: F1
type: f1
value: 0.7060755336617406
- name: Precision
type: precision
value: 0.738831615120275
- name: Recall
type: recall
value: 0.6761006289308176
- name: F1 (macro)
type: f1_macro
value: 0.45092058848834204
- name: Precision (macro)
type: precision_macro
value: 0.45426465258085835
- name: Recall (macro)
type: recall_macro
value: 0.45582773707773705
- name: F1 (entity span)
type: f1_entity_span
value: 0.7293729372937293
- name: Precision (entity span)
type: precision_entity_span
value: 0.7594501718213058
- name: Recall (entity span)
type: recall_entity_span
value: 0.7015873015873015
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-fin
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/fin](https://huggingface.co/datasets/tner/fin) 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): 0.7060755336617406
- Precision (micro): 0.738831615120275
- Recall (micro): 0.6761006289308176
- F1 (macro): 0.45092058848834204
- Precision (macro): 0.45426465258085835
- Recall (macro): 0.45582773707773705
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.4000000000000001
- organization: 0.5762711864406779
- other: 0.0
- person: 0.8274111675126904
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6370316240330781, 0.7718233002182738]
- 95%: [0.6236274300363168, 0.7857205513784461]
- F1 (macro):
- 90%: [0.6370316240330781, 0.7718233002182738]
- 95%: [0.6236274300363168, 0.7857205513784461]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-fin/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/deberta-v3-large-fin")
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/fin']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- 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/deberta-v3-large-fin/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.",
}
```
|
tner/roberta-large-ttc
|
tner
| 2022-09-26T14:25:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/ttc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:49:56Z |
---
datasets:
- tner/ttc
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-ttc
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ttc
type: tner/ttc
args: tner/ttc
metrics:
- name: F1
type: f1
value: 0.8314534321624235
- name: Precision
type: precision
value: 0.8269230769230769
- name: Recall
type: recall
value: 0.8360337005832793
- name: F1 (macro)
type: f1_macro
value: 0.8317396497007042
- name: Precision (macro)
type: precision_macro
value: 0.8296690551538254
- name: Recall (macro)
type: recall_macro
value: 0.8340850231639706
- name: F1 (entity span)
type: f1_entity_span
value: 0.8739929100870126
- name: Precision (entity span)
type: precision_entity_span
value: 0.8692307692307693
- name: Recall (entity span)
type: recall_entity_span
value: 0.8788075178224238
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-ttc
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ttc](https://huggingface.co/datasets/tner/ttc) 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): 0.8314534321624235
- Precision (micro): 0.8269230769230769
- Recall (micro): 0.8360337005832793
- F1 (macro): 0.8317396497007042
- Precision (macro): 0.8296690551538254
- Recall (macro): 0.8340850231639706
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7817403708987161
- organization: 0.7737656595431097
- person: 0.939712918660287
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8153670265512099, 0.8476331336073506]
- 95%: [0.8126974643551524, 0.8505459585794019]
- F1 (macro):
- 90%: [0.8153670265512099, 0.8476331336073506]
- 95%: [0.8126974643551524, 0.8505459585794019]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ttc/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-ttc/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-ttc")
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/ttc']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 16
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 2
- weight_decay: None
- lr_warmup_step_ratio: 0.1
- max_grad_norm: None
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ttc/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.",
}
```
|
tner/roberta-large-mit-restaurant
|
tner
| 2022-09-26T14:24:20Z | 181 | 3 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/mit_restaurant",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T00:20:40Z |
---
datasets:
- tner/mit_restaurant
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-mit-restaurant
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_restaurant
type: tner/mit_restaurant
args: tner/mit_restaurant
metrics:
- name: F1
type: f1
value: 0.8164676304211189
- name: Precision
type: precision
value: 0.8085901027077498
- name: Recall
type: recall
value: 0.8245001586797842
- name: F1 (macro)
type: f1_macro
value: 0.8081522050756316
- name: Precision (macro)
type: precision_macro
value: 0.7974927131040113
- name: Recall (macro)
type: recall_macro
value: 0.8199029986502094
- name: F1 (entity span)
type: f1_entity_span
value: 0.8557510999371464
- name: Precision (entity span)
type: precision_entity_span
value: 0.8474945533769063
- name: Recall (entity span)
type: recall_entity_span
value: 0.8641701047286575
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-mit-restaurant
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_restaurant](https://huggingface.co/datasets/tner/mit_restaurant) 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): 0.8164676304211189
- Precision (micro): 0.8085901027077498
- Recall (micro): 0.8245001586797842
- F1 (macro): 0.8081522050756316
- Precision (macro): 0.7974927131040113
- Recall (macro): 0.8199029986502094
The per-entity breakdown of the F1 score on the test set are below:
- amenity: 0.7140221402214022
- cuisine: 0.8558052434456929
- dish: 0.829103214890017
- location: 0.8611793611793611
- money: 0.8579710144927537
- rating: 0.8
- restaurant: 0.8713375796178344
- time: 0.6757990867579908
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.8050039870241192, 0.8289531287254172]
- 95%: [0.8030897272187587, 0.8312785732455824]
- F1 (macro):
- 90%: [0.8050039870241192, 0.8289531287254172]
- 95%: [0.8030897272187587, 0.8312785732455824]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-mit-restaurant/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-mit-restaurant/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-mit-restaurant")
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/mit_restaurant']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- 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-mit-restaurant/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.",
}
```
|
tner/roberta-large-bionlp2004
|
tner
| 2022-09-26T14:23:31Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/bionlp2004",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T00:10:08Z |
---
datasets:
- tner/bionlp2004
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-bionlp2004
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/bionlp2004
type: tner/bionlp2004
args: tner/bionlp2004
metrics:
- name: F1
type: f1
value: 0.7513434294088912
- name: Precision
type: precision
value: 0.7090462042823481
- name: Recall
type: recall
value: 0.7990071577003002
- name: F1 (macro)
type: f1_macro
value: 0.7165656135319811
- name: Precision (macro)
type: precision_macro
value: 0.6765580411075789
- name: Recall (macro)
type: recall_macro
value: 0.7685019796698731
- name: F1 (entity span)
type: f1_entity_span
value: 0.7936818107800032
- name: Precision (entity span)
type: precision_entity_span
value: 0.7490011269337158
- name: Recall (entity span)
type: recall_entity_span
value: 0.8440314015238974
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-bionlp2004
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) 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): 0.7513434294088912
- Precision (micro): 0.7090462042823481
- Recall (micro): 0.7990071577003002
- F1 (macro): 0.7165656135319811
- Precision (macro): 0.6765580411075789
- Recall (macro): 0.7685019796698731
The per-entity breakdown of the F1 score on the test set are below:
- cell_line: 0.6080273270708796
- cell_type: 0.7536311318169361
- dna: 0.7150259067357512
- protein: 0.7738602374694099
- rna: 0.7322834645669293
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.7433198691879565, 0.7598437899577305]
- 95%: [0.7420570442205622, 0.7606216680394585]
- F1 (macro):
- 90%: [0.7433198691879565, 0.7598437899577305]
- 95%: [0.7420570442205622, 0.7606216680394585]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bionlp2004/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-bionlp2004/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-bionlp2004")
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/bionlp2004']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- 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/roberta-large-bionlp2004/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.",
}
```
|
tner/roberta-large-fin
|
tner
| 2022-09-26T14:22:04Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:fin",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T20:28:39Z |
---
datasets:
- fin
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-fin
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fin
type: fin
args: fin
metrics:
- name: F1
type: f1
value: 0.6988727858293075
- name: Precision
type: precision
value: 0.7161716171617162
- name: Recall
type: recall
value: 0.6823899371069182
- name: F1 (macro)
type: f1_macro
value: 0.45636958249281745
- name: Precision (macro)
type: precision_macro
value: 0.4519134760270864
- name: Recall (macro)
type: recall_macro
value: 0.4705942205942206
- name: F1 (entity span)
type: f1_entity_span
value: 0.7087378640776698
- name: Precision (entity span)
type: precision_entity_span
value: 0.7227722772277227
- name: Recall (entity span)
type: recall_entity_span
value: 0.6952380952380952
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-fin
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/fin](https://huggingface.co/datasets/tner/fin) 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): 0.6988727858293075
- Precision (micro): 0.7161716171617162
- Recall (micro): 0.6823899371069182
- F1 (macro): 0.45636958249281745
- Precision (macro): 0.4519134760270864
- Recall (macro): 0.4705942205942206
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.5121951219512196
- organization: 0.49624060150375937
- other: 0.0
- person: 0.8170426065162907
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.6355508274231678, 0.7613829748047737]
- 95%: [0.624150263185174, 0.7724430709173716]
- F1 (macro):
- 90%: [0.6355508274231678, 0.7613829748047737]
- 95%: [0.624150263185174, 0.7724430709173716]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-fin/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-fin/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-fin")
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/fin']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- 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-fin/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.",
}
```
|
tner/roberta-large-tweebank-ner
|
tner
| 2022-09-26T14:21:19Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/tweebank_ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-10T10:03:35Z |
---
datasets:
- tner/tweebank_ner
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-tweebank-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweebank_ner
type: tner/tweebank_ner
args: tner/tweebank_ner
metrics:
- name: F1
type: f1
value: 0.7439490445859872
- name: Precision
type: precision
value: 0.7121951219512195
- name: Recall
type: recall
value: 0.7786666666666666
- name: F1 (macro)
type: f1_macro
value: 0.7354319457314183
- name: Precision (macro)
type: precision_macro
value: 0.712928566565599
- name: Recall (macro)
type: recall_macro
value: 0.7620465365030582
- name: F1 (entity span)
type: f1_entity_span
value: 0.8178343949044585
- name: Precision (entity span)
type: precision_entity_span
value: 0.7829268292682927
- name: Recall (entity span)
type: recall_entity_span
value: 0.856
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-tweebank-ner
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) 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): 0.7439490445859872
- Precision (micro): 0.7121951219512195
- Recall (micro): 0.7786666666666666
- F1 (macro): 0.7354319457314183
- Precision (macro): 0.712928566565599
- Recall (macro): 0.7620465365030582
The per-entity breakdown of the F1 score on the test set are below:
- location: 0.7782805429864253
- organization: 0.7377049180327869
- other: 0.5520581113801453
- person: 0.8736842105263157
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.7156413818791614, 0.771698046498159]
- 95%: [0.7063867669973017, 0.7763088810979543]
- F1 (macro):
- 90%: [0.7156413818791614, 0.771698046498159]
- 95%: [0.7063867669973017, 0.7763088810979543]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweebank-ner/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-tweebank-ner")
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/tweebank_ner']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- 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-tweebank-ner/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.",
}
```
|
tner/roberta-large-mit-movie-trivia
|
tner
| 2022-09-26T14:15:35Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/mit_movie_trivia",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:37:29Z |
---
datasets:
- tner/mit_movie_trivia
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-mit-movie-trivia
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/mit_movie_trivia
type: tner/mit_movie_trivia
args: tner/mit_movie_trivia
metrics:
- name: F1
type: f1
value: 0.7284025200655909
- name: Precision
type: precision
value: 0.7151330283002881
- name: Recall
type: recall
value: 0.7421737601125572
- name: F1 (macro)
type: f1_macro
value: 0.6502255723148889
- name: Precision (macro)
type: precision_macro
value: 0.6457158565124362
- name: Recall (macro)
type: recall_macro
value: 0.6578012664661943
- name: F1 (entity span)
type: f1_entity_span
value: 0.749525289142068
- name: Precision (entity span)
type: precision_entity_span
value: 0.7359322033898306
- name: Recall (entity span)
type: recall_entity_span
value: 0.7636299683432993
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-mit-movie-trivia
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/mit_movie_trivia](https://huggingface.co/datasets/tner/mit_movie_trivia) 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): 0.7284025200655909
- Precision (micro): 0.7151330283002881
- Recall (micro): 0.7421737601125572
- F1 (macro): 0.6502255723148889
- Precision (macro): 0.6457158565124362
- Recall (macro): 0.6578012664661943
The per-entity breakdown of the F1 score on the test set are below:
- actor: 0.9557453416149068
- award: 0.41726618705035967
- character_name: 0.7467105263157895
- date: 0.9668674698795181
- director: 0.9148936170212766
- genre: 0.7277079593058049
- opinion: 0.43478260869565216
- origin: 0.28846153846153844
- plot: 0.5132575757575758
- quote: 0.8387096774193549
- relationship: 0.5697329376854599
- soundtrack: 0.42857142857142855
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.718570586211627, 0.7387631655667131]
- 95%: [0.7170135350354089, 0.7412372838115527]
- F1 (macro):
- 90%: [0.718570586211627, 0.7387631655667131]
- 95%: [0.7170135350354089, 0.7412372838115527]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-mit-movie-trivia/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-mit-movie-trivia/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-mit-movie-trivia")
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/mit_movie_trivia']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- batch_size: 64
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 1
- 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/roberta-large-mit-movie-trivia/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.",
}
```
|
tner/deberta-v3-large-ontonotes5
|
tner
| 2022-09-26T14:14:54Z | 336 | 2 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"dataset:tner/ontonotes5",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-19T18:22:34Z |
---
datasets:
- tner/ontonotes5
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-ontonotes5
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ontonotes5
type: tner/ontonotes5
args: tner/ontonotes5
metrics:
- name: F1
type: f1
value: 0.9069623608411381
- name: Precision
type: precision
value: 0.902100360312857
- name: Recall
type: recall
value: 0.9118770542773386
- name: F1 (macro)
type: f1_macro
value: 0.834586960779896
- name: Precision (macro)
type: precision_macro
value: 0.8237351069457466
- name: Recall (macro)
type: recall_macro
value: 0.8475169311172334
- name: F1 (entity span)
type: f1_entity_span
value: 0.9267538434352359
- name: Precision (entity span)
type: precision_entity_span
value: 0.9217857456718517
- name: Recall (entity span)
type: recall_entity_span
value: 0.9317757839566492
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-ontonotes5
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) 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): 0.9069623608411381
- Precision (micro): 0.902100360312857
- Recall (micro): 0.9118770542773386
- F1 (macro): 0.834586960779896
- Precision (macro): 0.8237351069457466
- Recall (macro): 0.8475169311172334
The per-entity breakdown of the F1 score on the test set are below:
- cardinal_number: 0.853475935828877
- date: 0.8815545959284392
- event: 0.8030303030303031
- facility: 0.7896678966789669
- geopolitical_area: 0.9650033867690223
- group: 0.9337209302325581
- language: 0.8372093023255814
- law: 0.6756756756756757
- location: 0.7624020887728459
- money: 0.8818897637795275
- ordinal_number: 0.8635235732009926
- organization: 0.914952751528627
- percent: 0.9
- person: 0.9609866599546942
- product: 0.7901234567901234
- quantity: 0.8161434977578474
- time: 0.674364896073903
- work_of_art: 0.7188405797101449
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.9019409960743083, 0.911751130722053]
- 95%: [0.9010822890967028, 0.9125611412371442]
- F1 (macro):
- 90%: [0.9019409960743083, 0.911751130722053]
- 95%: [0.9010822890967028, 0.9125611412371442]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-ontonotes5/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-ontonotes5/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/deberta-v3-large-ontonotes5")
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/ontonotes5']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- 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/deberta-v3-large-ontonotes5/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.",
}
```
|
tner/roberta-large-bc5cdr
|
tner
| 2022-09-26T14:13:58Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/bc5cdr",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:32:35Z |
---
datasets:
- tner/bc5cdr
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-bc5cdr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/bc5cdr
type: tner/bc5cdr
args: tner/bc5cdr
metrics:
- name: F1
type: f1
value: 0.8840696387239609
- name: Precision
type: precision
value: 0.8728266269249876
- name: Recall
type: recall
value: 0.8956060760526048
- name: F1 (macro)
type: f1_macro
value: 0.8797360472482783
- name: Precision (macro)
type: precision_macro
value: 0.8684274142690976
- name: Recall (macro)
type: recall_macro
value: 0.8913672531528037
- name: F1 (entity span)
type: f1_entity_span
value: 0.886283586595552
- name: Precision (entity span)
type: precision_entity_span
value: 0.8750124192747144
- name: Recall (entity span)
type: recall_entity_span
value: 0.8978489142624121
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-bc5cdr
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/bc5cdr](https://huggingface.co/datasets/tner/bc5cdr) 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): 0.8840696387239609
- Precision (micro): 0.8728266269249876
- Recall (micro): 0.8956060760526048
- F1 (macro): 0.8797360472482783
- Precision (macro): 0.8684274142690976
- Recall (macro): 0.8913672531528037
The per-entity breakdown of the F1 score on the test set are below:
- chemical: 0.9256943167187788
- disease: 0.8337777777777777
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.878869501707946, 0.8890795634554179]
- 95%: [0.8776790106527211, 0.8897422640465147]
- F1 (macro):
- 90%: [0.878869501707946, 0.8890795634554179]
- 95%: [0.8776790106527211, 0.8897422640465147]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-bc5cdr/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-bc5cdr/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-bc5cdr")
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/bc5cdr']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- 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-bc5cdr/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.",
}
```
|
tner/roberta-large-conll2003
|
tner
| 2022-09-26T14:13:18Z | 65 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-09T23:19:06Z |
---
datasets:
- tner/conll2003
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/conll2003
type: tner/conll2003
args: tner/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).
### 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-conll2003")
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/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.",
}
```
|
tner/roberta-large-ontonotes5
|
tner
| 2022-09-26T14:12:05Z | 30,400 | 16 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"dataset:tner/ontonotes5",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T10:33:41Z |
---
datasets:
- tner/ontonotes5
metrics:
- f1
- precision
- recall
model-index:
- name: tner/roberta-large-ontonotes5
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/ontonotes5
type: tner/ontonotes5
args: tner/ontonotes5
metrics:
- name: F1
type: f1
value: 0.908632361399938
- name: Precision
type: precision
value: 0.905148095909732
- name: Recall
type: recall
value: 0.9121435551212579
- name: F1 (macro)
type: f1_macro
value: 0.8265477704565624
- name: Precision (macro)
type: precision_macro
value: 0.8170668848546687
- name: Recall (macro)
type: recall_macro
value: 0.8387672780349001
- name: F1 (entity span)
type: f1_entity_span
value: 0.9284544931640193
- name: Precision (entity span)
type: precision_entity_span
value: 0.9248942172073342
- name: Recall (entity span)
type: recall_entity_span
value: 0.9320422848005685
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/roberta-large-ontonotes5
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the
[tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) 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): 0.908632361399938
- Precision (micro): 0.905148095909732
- Recall (micro): 0.9121435551212579
- F1 (macro): 0.8265477704565624
- Precision (macro): 0.8170668848546687
- Recall (macro): 0.8387672780349001
The per-entity breakdown of the F1 score on the test set are below:
- cardinal_number: 0.8605277329025309
- date: 0.872996300863132
- event: 0.7424242424242424
- facility: 0.7732342007434945
- geopolitical_area: 0.9687148323205043
- group: 0.9470588235294117
- language: 0.7499999999999999
- law: 0.6666666666666666
- location: 0.7593582887700535
- money: 0.901098901098901
- ordinal_number: 0.85785536159601
- organization: 0.9227360841872057
- percent: 0.9171428571428571
- person: 0.9556004036326943
- product: 0.7857142857142858
- quantity: 0.7945205479452055
- time: 0.6870588235294116
- work_of_art: 0.7151515151515151
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.9039454247544766, 0.9128956119702822]
- 95%: [0.9030263216115454, 0.9138350859566045]
- F1 (macro):
- 90%: [0.9039454247544766, 0.9128956119702822]
- 95%: [0.9030263216115454, 0.9138350859566045]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/roberta-large-ontonotes5/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-ontonotes5")
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/ontonotes5']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: roberta-large
- crf: True
- max_length: 128
- epoch: 15
- 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-ontonotes5/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.",
}
```
|
dartkain/newforproject
|
dartkain
| 2022-09-26T14:00:12Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-09-26T14:00:12Z |
---
license: creativeml-openrail-m
---
|
kp9z2/distilgpt2-finetuned-wikitext2
|
kp9z2
| 2022-09-26T13:51:59Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-26T13:40:05Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: kp9z2/distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kp9z2/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8579
- Validation Loss: 3.6750
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8579 | 3.6750 | 0 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/ba-shiroko
|
sd-concepts-library
| 2022-09-26T13:20:08Z | 0 | 5 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T13:20:04Z |
---
license: mit
---
### BA_Shiroko on Stable Diffusion
This is the `<shiroko>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
jurabi/bert-ner-japanese
|
jurabi
| 2022-09-26T12:13:44Z | 3,771 | 10 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ja",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-26T07:46:38Z |
---
language:
- ja
widget:
- text: 株式会社Jurabiは、東京都台東区に本社を置くIT企業である。
license: cc-by-sa-3.0
---
# BERTによる日本語固有表現抽出のモデル
[BertForTokenClassification](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForTokenClassification)を用いて、日本語の文から固有表現を抽出します。
抽出される固有表現のタイプは、以下の8種類です。
- 人名
- 法人名（法人または法人に類する組織）
- 政治的組織名（政治的組織名、政党名、政府組織名、行政組織名、軍隊名、国際組織名）
- その他の組織名 （競技組織名、公演組織名、その他）
- 地名
- 施設名
- 製品名（商品名、番組名、映画名、書籍名、歌名、ブランド名等）
- イベント名
## 使用方法
必要なライブラリ（transformers、unidic_lite、fugashi）をpipなどでインストールして、下記のコードを実行するだけです。
```python
from transformers import BertJapaneseTokenizer, BertForTokenClassification
from transformers import pipeline
model = BertForTokenClassification.from_pretrained("jurabi/bert-ner-japanese")
tokenizer = BertJapaneseTokenizer.from_pretrained("jurabi/bert-ner-japanese")
ner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)
ner_pipeline("株式会社Jurabiは、東京都台東区に本社を置くIT企業である。")
```
## 事前学習モデル
東北大学乾研究室が公開している日本語BERTモデル（[cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2)）
## 学習データ
ストックマーク株式会社が公開しているWikipediaを用いた日本語の固有表現抽出データセット（[stockmarkteam/ner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)）
## ソースコード
ファインチューニングに使用したプログラムは、[jurabiinc/bert-ner-japanese](https://github.com/jurabiinc/bert-ner-japanese)で公開しています。
## ライセンス
[Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
|
sd-concepts-library/poring-ragnarok-online
|
sd-concepts-library
| 2022-09-26T12:11:17Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-26T12:11:11Z |
---
license: mit
---
### Poring Ragnarok Online on Stable Diffusion
This is the `<poring-ro>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
huggingtweets/dolceragazza26-femdomfusion-mistressleiaa
|
huggingtweets
| 2022-09-26T10:24:27Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-26T10:23:11Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dolceragazza26-femdomfusion-mistressleiaa/1664187862433/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1196458544947769345/S04dF85Y_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1548917860742729728/Kl_FyA-Y_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1574160039232868352/If7OL-Q-_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Femdom Fusion & 💞 M.Francesca ⛔️No porn🚫 & Mistress Leia</div>
<div style="text-align: center; font-size: 14px;">@dolceragazza26-femdomfusion-mistressleiaa</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Femdom Fusion & 💞 M.Francesca ⛔️No porn🚫 & Mistress Leia.
| Data | Femdom Fusion | 💞 M.Francesca ⛔️No porn🚫 | Mistress Leia |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3222 | 3249 |
| Retweets | 0 | 2204 | 663 |
| Short tweets | 355 | 168 | 367 |
| Tweets kept | 2893 | 850 | 2219 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ov6jnnk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dolceragazza26-femdomfusion-mistressleiaa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/15gbfzn2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/15gbfzn2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/dolceragazza26-femdomfusion-mistressleiaa')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
microsoft/deberta-v3-xsmall
|
microsoft
| 2022-09-26T08:59:28Z | 54,424 | 42 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta-v2",
"deberta",
"deberta-v3",
"fill-mask",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta
- deberta-v3
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
The DeBERTa V3 xsmall model comes with 12 layers and a hidden size of 384. It has only **22M** backbone parameters with a vocabulary containing 128K tokens which introduces 48M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 2.0 and MNLI tasks.
| Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
|-------------------|----------|-------------------|-----------|----------|
| RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- |
| XLNet-base |32 |92 | -/80.2 | 86.8/- |
| ELECTRA-base |30 |86 | -/80.5 | 88.8/ |
| DeBERTa-base |50 |100 | 86.2/83.1| 88.8/88.5|
| DeBERTa-v3-large|128|304 | 91.5/89.0 | 91.8/91.9|
| DeBERTa-v3-base |128|86 | 88.4/85.4 | 90.6/90.7|
| DeBERTa-v3-small |128|44 | 82.8/80.4 | 88.3/87.7|
| **DeBERTa-v3-xsmall** |128|**22** | **84.8/82.0** | **88.1/88.3**|
| DeBERTa-v3-xsmall+SiFT|128|22 | -/- | 88.4/88.5|
[#| ELECTRA-small |30 |9.6 | - | - |]::
#### Fine-tuning with HF transformers
```bash
#!/bin/bash
cd transformers/examples/pytorch/text-classification/
pip install datasets
export TASK_NAME=mnli
output_dir="ds_results"
num_gpus=8
batch_size=8
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
run_glue.py \
--model_name_or_path microsoft/deberta-v3-xsmall \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--evaluation_strategy steps \
--max_seq_length 256 \
--warmup_steps 1000 \
--per_device_train_batch_size ${batch_size} \
--learning_rate 4.5e-5 \
--num_train_epochs 3 \
--output_dir $output_dir \
--overwrite_output_dir \
--logging_steps 1000 \
--logging_dir $output_dir
```
### Citation
If you find DeBERTa useful for your work, please cite the following papers:
``` latex
@misc{he2021debertav3,
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
year={2021},
eprint={2111.09543},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
microsoft/deberta-v3-small
|
microsoft
| 2022-09-26T08:59:13Z | 338,854 | 49 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta-v2",
"deberta",
"deberta-v3",
"fill-mask",
"en",
"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta
- deberta-v3
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. It has **44M** backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 2.0 and MNLI tasks.
| Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
|-------------------|----------|-------------------|-----------|----------|
| RoBERTa-base |50 |86 | 83.7/80.5 | 87.6/- |
| XLNet-base |32 |92 | -/80.2 | 86.8/- |
| ELECTRA-base |30 |86 | -/80.5 | 88.8/ |
| DeBERTa-base |50 |100 | 86.2/83.1| 88.8/88.5|
| DeBERTa-v3-large|128|304 | 91.5/89.0 | 91.8/91.9 |
| DeBERTa-v3-base |128|86 | 88.4/85.4 | 90.6/90.7|
| **DeBERTa-v3-small** |128|**44** | **82.8/80.4** | **88.3/87.7**|
| DeBERTa-v3-small+SiFT|128|22 | -/- | 88.8/88.5|
#### Fine-tuning with HF transformers
```bash
#!/bin/bash
cd transformers/examples/pytorch/text-classification/
pip install datasets
export TASK_NAME=mnli
output_dir="ds_results"
num_gpus=8
batch_size=8
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
run_glue.py \
--model_name_or_path microsoft/deberta-v3-small \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--evaluation_strategy steps \
--max_seq_length 256 \
--warmup_steps 1500 \
--per_device_train_batch_size ${batch_size} \
--learning_rate 4.5e-5 \
--num_train_epochs 3 \
--output_dir $output_dir \
--overwrite_output_dir \
--logging_steps 1000 \
--logging_dir $output_dir
```
### Citation
If you find DeBERTa useful for your work, please cite the following papers:
``` latex
@misc{he2021debertav3,
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
year={2021},
eprint={2111.09543},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
microsoft/deberta-v2-xlarge
|
microsoft
| 2022-09-26T08:59:06Z | 104,224 | 23 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"deberta-v2",
"deberta",
"fill-mask",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta
- fill-mask
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This is the DeBERTa V2 xlarge model with 24 layers, 1536 hidden size. The total parameters are 900M and it is trained with 160GB raw data.
### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\\\
--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\\\
--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
prikarsartam/Olga
|
prikarsartam
| 2022-09-26T08:17:24Z | 67 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-26T04:59:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: prikarsartam/Olga
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# prikarsartam/Olga
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8904
- Validation Loss: 2.6281
- Train Rouge1: 25.0368
- Train Rouge2: 5.6914
- Train Rougel: 19.4806
- Train Rougelsum: 19.4874
- Train Gen Len: 18.7987
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.0715 | 2.6854 | 23.4337 | 4.8994 | 18.1348 | 18.1316 | 18.7024 | 0 |
| 2.8904 | 2.6281 | 25.0368 | 5.6914 | 19.4806 | 19.4874 | 18.7987 | 1 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Immanuelraja/layoutlmv3-finetuned-cord_100
|
Immanuelraja
| 2022-09-26T07:33:00Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-26T06:36:43Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: train
args: cord
metrics:
- name: Precision
type: precision
value: 0.9478778853313478
- name: Recall
type: recall
value: 0.9528443113772455
- name: F1
type: f1
value: 0.950354609929078
- name: Accuracy
type: accuracy
value: 0.9541595925297114
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2176
- Precision: 0.9479
- Recall: 0.9528
- F1: 0.9504
- Accuracy: 0.9542
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 1.0378 | 0.7404 | 0.7964 | 0.7674 | 0.8035 |
| 1.4104 | 3.12 | 500 | 0.5605 | 0.8291 | 0.8645 | 0.8465 | 0.8790 |
| 1.4104 | 4.69 | 750 | 0.3959 | 0.8728 | 0.8990 | 0.8857 | 0.9155 |
| 0.4054 | 6.25 | 1000 | 0.3111 | 0.9231 | 0.9349 | 0.9290 | 0.9393 |
| 0.4054 | 7.81 | 1250 | 0.2847 | 0.9135 | 0.9251 | 0.9193 | 0.9317 |
| 0.2124 | 9.38 | 1500 | 0.2457 | 0.9281 | 0.9379 | 0.9330 | 0.9410 |
| 0.2124 | 10.94 | 1750 | 0.2390 | 0.9371 | 0.9484 | 0.9427 | 0.9520 |
| 0.1438 | 12.5 | 2000 | 0.2196 | 0.9443 | 0.9513 | 0.9478 | 0.9546 |
| 0.1438 | 14.06 | 2250 | 0.2182 | 0.9478 | 0.9521 | 0.9500 | 0.9533 |
| 0.1093 | 15.62 | 2500 | 0.2176 | 0.9479 | 0.9528 | 0.9504 | 0.9542 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
neeva/query2query
|
neeva
| 2022-09-26T07:11:21Z | 32 | 8 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-22T18:23:35Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: cc-by-nc-sa-4.0
---
# query2query
This is a [sentence-transformers](https://www.SBERT.net) model: It maps queries to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search over queries.
Checkout this announcing blogpost for more information: https://neeva.com/blog/state-of-the-art-query2query-similarity(https://neeva.com/blog/state-of-the-art-query2query-similarity)
**Note: we are releasing this under a license which prevents commercial use. If you want to use it for commercial purposes, please reach out to contact@neeva.co or rajhans@neeva.co with a brief description of what you want to use it for and we will try our best to respond very quickly.**
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
queries = ["flight cost from nyc to la", "ticket prices from nyc to la"]
model = SentenceTransformer('neeva/query2query')
embeddings = model.encode(queries)
print(embeddings)
```
## Training
The model was trained for 1M steps with a batch size of 1024 at a learning rate of 2e-5 using a cosine learning rate scheduler with 10000 warmup steps.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DataParallel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
|
MGanesh29/parrot_paraphraser_on_T5-finetuned-xsum-v7
|
MGanesh29
| 2022-09-26T06:47:57Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-22T09:34:29Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: parrot_paraphraser_on_T5-finetuned-xsum-v7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# parrot_paraphraser_on_T5-finetuned-xsum-v7
This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_paraphraser_on_T5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0316
- Rouge1: 86.4178
- Rouge2: 84.901
- Rougel: 86.458
- Rougelsum: 86.4281
- Gen Len: 17.887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0752 | 1.0 | 2000 | 0.0439 | 86.0044 | 84.1284 | 86.0265 | 86.0167 | 17.895 |
| 0.0454 | 2.0 | 4000 | 0.0352 | 86.2948 | 84.6092 | 86.3256 | 86.293 | 17.88 |
| 0.0308 | 3.0 | 6000 | 0.0324 | 86.3316 | 84.7883 | 86.374 | 86.3355 | 17.887 |
| 0.0242 | 4.0 | 8000 | 0.0316 | 86.4178 | 84.901 | 86.458 | 86.4281 | 17.887 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
jamiehuang12/t5-small-finetuned-xsum
|
jamiehuang12
| 2022-09-26T04:44:45Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-26T03:22:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 13.2843
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6744
- Rouge1: 13.2843
- Rouge2: 2.006
- Rougel: 10.6541
- Rougelsum: 12.0343
- Gen Len: 18.9984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8822 | 1.0 | 17040 | 3.6744 | 13.2843 | 2.006 | 10.6541 | 12.0343 | 18.9984 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
erikejw/swinv2-small-patch4-window16-256-finetuned-eurosat
|
erikejw
| 2022-09-26T03:31:23Z | 172 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-26T01:44:49Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swinv2-small-patch4-window16-256-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9892592592592593
- name: F1
type: f1
value: 0.9892542163878574
- name: Precision
type: precision
value: 0.9892896521886161
- name: Recall
type: recall
value: 0.9892592592592593
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-small-patch4-window16-256-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0328
- Accuracy: 0.9893
- F1: 0.9893
- Precision: 0.9893
- Recall: 0.9893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2326 | 1.0 | 253 | 0.0870 | 0.9715 | 0.9716 | 0.9720 | 0.9715 |
| 0.1955 | 2.0 | 506 | 0.0576 | 0.9789 | 0.9788 | 0.9794 | 0.9789 |
| 0.1229 | 3.0 | 759 | 0.0450 | 0.9837 | 0.9837 | 0.9839 | 0.9837 |
| 0.0797 | 4.0 | 1012 | 0.0332 | 0.9889 | 0.9889 | 0.9889 | 0.9889 |
| 0.0826 | 5.0 | 1265 | 0.0328 | 0.9893 | 0.9893 | 0.9893 | 0.9893 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sahajrajmalla/patrakar
|
sahajrajmalla
| 2022-09-26T02:06:00Z | 107 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"nepali-nlp",
"nepali-news-classificiation",
"nlp",
"deep-learning",
"transfer-learning",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-15T07:05:22Z |
---
license: mit
tags:
- nepali-nlp
- nepali-news-classificiation
- nlp
- transformers
- deep-learning
- pytorch
- transfer-learning
model-index:
- name: patrakar
results: []
widget:
- text: "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"
example_title: "Example 1"
- text: "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन ।"
example_title: "Example 2"
- text: "ठूलो उद्योग खोल्न महिलालाई ऋण दिइन्न"
example_title: "Example 3"
---
# patrakar/ पत्रकार (Nepali News Classifier)
Last updated: September 2022
## Model Details
**patrakar** is a DistilBERT pre-trained sequence classification transformer model which classifies Nepali language news into 9 newsgroup category, such as:
- politics
- opinion
- bank
- entertainment
- economy
- health
- literature
- sports
- tourism
It is developed by Sahaj Raj Malla to be generally usefuly for general public and so that others could explore them for commercial and scientific purposes. This model was trained on [Sakonii/distilgpt2-nepali](https://huggingface.co/Sakonii/distilgpt2-nepali) model.
It achieves the following results on the test dataset:
| Total Number of samples | Accuracy(%)
|:-------------:|:---------------:
| 5670 | 95.475
### Model date
September 2022
### Model type
Sequence classification model
### Model version
1.0.0
## Model Usage
This model can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
from transformers import pipeline, set_seed
set_seed(42)
model_name = "sahajrajmalla/patrakar"
classifier = pipeline('text-classification', model=model_name)
text = "नेकपा (एमाले)का नेता गोकर्णराज विष्टले सहमति र सहकार्यबाटै संविधान बनाउने तथा जनताको जीवनस्तर उकास्ने काम गर्नु नै अबको मुख्य काम रहेको बताएका छन् ।"
classifier(text)
```
Here is how we can use the model to get the features of a given text in PyTorch:
```python
!pip install transformers torch
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
import torch
import torch.nn.functional as F
# initializing model and tokenizer
model_name = "sahajrajmalla/patrakar"
# downloading tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# downloading model
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def tokenize_function(examples):
return tokenizer(examples["data"], padding="max_length", truncation=True)
# predicting with the model
sequence_i_want_to_predict = "राजनीतिक स्थिरता नहुँदा विकास निर्माणले गति लिन सकेन"
# initializing our labels
label_list = [
"bank",
"economy",
"entertainment",
"health",
"literature",
"opinion",
"politics",
"sports",
"tourism"
]
batch = tokenizer(sequence_i_want_to_predict, padding=True, truncation=True, max_length=512, return_tensors='pt')
with torch.no_grad():
outputs = model(**batch)
predictions = F.softmax(outputs.logits, dim=1)
labels = torch.argmax(predictions, dim=1)
print(f"The sequence: \n\n {word_i_want_to_predict} \n\n is predicted to be of newsgroup {label_list[labels.item()]}")
```
## Training data
This model is trained on 50,945 rows of Nepali language news grouped [dataset](https://www.kaggle.com/competitions/text-it-meet-22/data?select=train.csv) found on Kaggle which was also used in IT Meet 2022 Text challenge.
## Framework versions
- Transformers 4.20.1
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ramsformers/shoes-brand
|
ramsformers
| 2022-09-26T01:56:03Z | 226 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-26T01:55:52Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: shoes-brand
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6716417670249939
---
# shoes-brand
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### adidas shoes
#### nike shoes
#### puma shoes
|
jamiehuang/t5-small-finetuned-xsum
|
jamiehuang
| 2022-09-26T01:29:12Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-24T21:08:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 13.2962
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6746
- Rouge1: 13.2962
- Rouge2: 2.0081
- Rougel: 10.6529
- Rougelsum: 12.049
- Gen Len: 18.9985
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 3.8901 | 1.0 | 17040 | 3.6746 | 13.2962 | 2.0081 | 10.6529 | 12.049 | 18.9985 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ammarpl/t5-base-finetuned-elif-attempt1
|
ammarpl
| 2022-09-26T01:14:32Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-25T21:01:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: t5-base-finetuned-elif-attempt1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: train_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 3.9675
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-elif-attempt1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3889
- Rouge1: 3.9675
- Rouge2: 0.248
- Rougel: 3.454
- Rougelsum: 3.765
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 5.8271 | 1.0 | 17040 | 5.3889 | 3.9675 | 0.248 | 3.454 | 3.765 | 19.0 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
farleyknight/patent-summarization-allen-led-large-2022-09-20
|
farleyknight
| 2022-09-25T23:46:58Z | 96 | 1 |
transformers
|
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"generated_from_trainer",
"dataset:farleyknight/big_patent_5_percent",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-20T16:22:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- farleyknight/big_patent_5_percent
metrics:
- rouge
model-index:
- name: patent-summarization-allen-led-large-2022-09-20
results:
- task:
name: Summarization
type: summarization
dataset:
name: farleyknight/big_patent_5_percent
type: farleyknight/big_patent_5_percent
config: all
split: train
args: all
metrics:
- name: Rouge1
type: rouge
value: 0.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# patent-summarization-allen-led-large-2022-09-20
This model is a fine-tuned version of [allenai/led-large-16384-arxiv](https://huggingface.co/allenai/led-large-16384-arxiv) on the farleyknight/big_patent_5_percent dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8233
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 128.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.4766 | 0.08 | 5000 | 3.4240 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 3.2549 | 0.17 | 10000 | 3.2908 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 3.2295 | 0.25 | 15000 | 3.1862 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 3.1455 | 0.33 | 20000 | 3.1291 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 3.0526 | 0.41 | 25000 | 3.0684 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 3.0024 | 0.5 | 30000 | 3.0134 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.9671 | 0.58 | 35000 | 2.9696 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.9862 | 0.66 | 40000 | 2.9431 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.9168 | 0.75 | 45000 | 2.8989 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.9063 | 0.83 | 50000 | 2.8559 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.8417 | 0.91 | 55000 | 2.8398 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
| 2.7853 | 0.99 | 60000 | 2.8240 | 0.0 | 0.0 | 0.0 | 0.0 | 512.0 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
amirabbas/wav2vec2-large-xls-r-300m-turkish-demo-colab-1
|
amirabbas
| 2022-09-25T23:11:58Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-25T19:40:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-demo-colab-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-demo-colab-1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3487
- Wer: 0.3000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.0425 | 3.67 | 400 | 0.7168 | 0.6650 |
| 0.4365 | 7.34 | 800 | 0.4498 | 0.4695 |
| 0.2103 | 11.01 | 1200 | 0.3975 | 0.3840 |
| 0.1257 | 14.68 | 1600 | 0.3655 | 0.3341 |
| 0.0828 | 18.35 | 2000 | 0.3487 | 0.3000 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BumblingOrange/Shalltear_Bloodfallen
|
BumblingOrange
| 2022-09-25T22:25:26Z | 0 | 1 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-09-25T19:31:46Z |
---
license: bigscience-bloom-rail-1.0
---
Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion
Custom Dreambooth model based off of the likeness of Shalltear Bloodfallen from Overlord. Dataset was 15 training images, and 13 regularization images. Trained for 3000 steps.
To use the model, simply insert the name 'Shalltear Bloodfallen' into your prompts. The class token used was 'vampire_girl_hair_bow_white_hair'. Append the class token after Shalltear Bloodfallen for stronger result.
EX: "A photo of Shalltear Bloodfallen vampire_girl_hair_bow_white_hair"
|
sd-concepts-library/remert
|
sd-concepts-library
| 2022-09-25T20:50:59Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-09-25T20:50:05Z |
---
license: mit
---
### remert on Stable Diffusion
This is the `<Remert>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
BumblingOrange/Cirno
|
BumblingOrange
| 2022-09-25T20:08:39Z | 0 | 4 | null |
[
"region:us"
] | null | 2022-09-25T19:37:51Z |
Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion
Custom Dreambooth model based off of the likeness of Cirno from Touhou. Dataset was 16 training images, and 18 regularization images. Trained for 3000 steps.
To use the model, simply insert the phrase 'A photo of sks' into your prompts. The class token used was 'ice_fairy'. Append the class token after 'A photo of sks' for stronger results. EX: "A photo of sks ice_fairy"
|
quecopiones/distillbert-base-spanish-uncased-finetuned-full-suicidios
|
quecopiones
| 2022-09-25T19:52:22Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-25T14:14:14Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distillbert-base-spanish-uncased-finetuned-full-suicidios
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distillbert-base-spanish-uncased-finetuned-full-suicidios
This model is a fine-tuned version of [CenIA/distillbert-base-spanish-uncased](https://huggingface.co/CenIA/distillbert-base-spanish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0825
- Accuracy: 0.9814
- F1: 0.9814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2059 | 1.0 | 32058 | 0.1142 | 0.9694 | 0.9694 |
| 0.1229 | 2.0 | 64116 | 0.0825 | 0.9814 | 0.9814 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
monakth/distilbert-base-multilingual-cased-finetuned-squad
|
monakth
| 2022-09-25T19:18:13Z | 121 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-25T16:02:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-multilingual-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2983 | 1.0 | 5555 | 1.2202 |
| 1.0252 | 2.0 | 11110 | 1.1583 |
| 0.8078 | 3.0 | 16665 | 1.1954 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
eliwill/stoic-generator-10e
|
eliwill
| 2022-09-25T18:37:19Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-09-25T18:25:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: eliwill/stoic-generator-10e
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eliwill/stoic-generator-10e
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.4753
- Validation Loss: 3.7980
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.0230 | 3.9474 | 0 |
| 3.8580 | 3.8982 | 1 |
| 3.7757 | 3.8721 | 2 |
| 3.7149 | 3.8489 | 3 |
| 3.6640 | 3.8343 | 4 |
| 3.6210 | 3.8152 | 5 |
| 3.5796 | 3.8088 | 6 |
| 3.5429 | 3.8038 | 7 |
| 3.5061 | 3.7967 | 8 |
| 3.4753 | 3.7980 | 9 |
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sharonpeng/distilbert-base-uncased-finetuned-squad
|
sharonpeng
| 2022-09-25T18:31:46Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-06T06:04:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.215 | 1.0 | 5533 | 1.1619 |
| 0.9533 | 2.0 | 11066 | 1.1257 |
| 0.7566 | 3.0 | 16599 | 1.1456 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
amirabbas/wav2vec2-large-xls-r-300m-turkish-demo-colab
|
amirabbas
| 2022-09-25T18:23:15Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-25T12:17:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BumblingOrange/Holo
|
BumblingOrange
| 2022-09-25T17:15:45Z | 0 | 2 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-09-25T05:01:30Z |
---
license: bigscience-bloom-rail-1.0
---
Uses the Waifu Diffusion model as a base, linked here: https://huggingface.co/hakurei/waifu-diffusion
Custom Dreambooth model based off of the likeness of Holo from Spice and Wolf. Dataset was 7 training images, and 14 regularization images. Trained for 3000 steps.
To use the model, simply insert the name "Holo" into your prompts. The class token was "wolf_girl".
Add the class token after "Holo" from stronger results.
EXAMPLE: "A photo of Holo wolf_girl"
|
kevinbram/nyfin
|
kevinbram
| 2022-09-25T17:13:57Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-09-25T15:28:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: nyfin
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nyfin
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.26 | 1.0 | 5533 | 1.2155 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.11.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Lagstill/GPT-2-Hindi
|
Lagstill
| 2022-09-25T16:48:30Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-09-25T16:48:30Z |
---
license: bigscience-openrail-m
---
|
simecek/DNADebertaK6_Worm
|
simecek
| 2022-09-25T14:28:30Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-19T08:02:15Z |
---
tags:
- generated_from_trainer
model-index:
- name: DNADebertaK6_Worm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaK6_Worm
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6161
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 600001
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 4.5653 | 7.26 | 20000 | 1.8704 |
| 1.8664 | 14.53 | 40000 | 1.7762 |
| 1.7803 | 21.79 | 60000 | 1.7429 |
| 1.7502 | 29.06 | 80000 | 1.7305 |
| 1.7329 | 36.32 | 100000 | 1.7185 |
| 1.7191 | 43.59 | 120000 | 1.7073 |
| 1.7065 | 50.85 | 140000 | 1.6925 |
| 1.6945 | 58.12 | 160000 | 1.6877 |
| 1.6862 | 65.38 | 180000 | 1.6792 |
| 1.6788 | 72.65 | 200000 | 1.6712 |
| 1.6729 | 79.91 | 220000 | 1.6621 |
| 1.6679 | 87.18 | 240000 | 1.6608 |
| 1.6632 | 94.44 | 260000 | 1.6586 |
| 1.6582 | 101.71 | 280000 | 1.6585 |
| 1.6551 | 108.97 | 300000 | 1.6564 |
| 1.6507 | 116.24 | 320000 | 1.6449 |
| 1.6481 | 123.5 | 340000 | 1.6460 |
| 1.6448 | 130.77 | 360000 | 1.6411 |
| 1.6425 | 138.03 | 380000 | 1.6408 |
| 1.6387 | 145.3 | 400000 | 1.6358 |
| 1.6369 | 152.56 | 420000 | 1.6373 |
| 1.6337 | 159.83 | 440000 | 1.6364 |
| 1.6312 | 167.09 | 460000 | 1.6303 |
| 1.6298 | 174.36 | 480000 | 1.6346 |
| 1.6273 | 181.62 | 500000 | 1.6272 |
| 1.6244 | 188.88 | 520000 | 1.6268 |
| 1.6225 | 196.15 | 540000 | 1.6295 |
| 1.6207 | 203.41 | 560000 | 1.6206 |
| 1.6186 | 210.68 | 580000 | 1.6277 |
| 1.6171 | 217.94 | 600000 | 1.6161 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
simecek/DNADebertaK6_Arabidopsis
|
simecek
| 2022-09-25T14:27:59Z | 178 | 1 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-19T07:42:31Z |
---
tags:
- generated_from_trainer
model-index:
- name: DNADebertaK6_Arabidopsis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNADebertaK6_Arabidopsis
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 600001
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 4.6174 | 6.12 | 20000 | 1.9257 |
| 1.8873 | 12.24 | 40000 | 1.8098 |
| 1.8213 | 18.36 | 60000 | 1.7952 |
| 1.8042 | 24.48 | 80000 | 1.7888 |
| 1.7945 | 30.6 | 100000 | 1.7861 |
| 1.7873 | 36.72 | 120000 | 1.7772 |
| 1.782 | 42.84 | 140000 | 1.7757 |
| 1.7761 | 48.96 | 160000 | 1.7632 |
| 1.7714 | 55.08 | 180000 | 1.7685 |
| 1.7677 | 61.2 | 200000 | 1.7568 |
| 1.7637 | 67.32 | 220000 | 1.7570 |
| 1.7585 | 73.44 | 240000 | 1.7442 |
| 1.7554 | 79.56 | 260000 | 1.7556 |
| 1.7515 | 85.68 | 280000 | 1.7505 |
| 1.7483 | 91.8 | 300000 | 1.7463 |
| 1.745 | 97.92 | 320000 | 1.7425 |
| 1.7427 | 104.04 | 340000 | 1.7425 |
| 1.7398 | 110.16 | 360000 | 1.7359 |
| 1.7377 | 116.28 | 380000 | 1.7369 |
| 1.7349 | 122.4 | 400000 | 1.7340 |
| 1.7325 | 128.52 | 420000 | 1.7313 |
| 1.731 | 134.64 | 440000 | 1.7256 |
| 1.7286 | 140.76 | 460000 | 1.7238 |
| 1.7267 | 146.88 | 480000 | 1.7324 |
| 1.7247 | 153.0 | 500000 | 1.7247 |
| 1.7228 | 159.12 | 520000 | 1.7185 |
| 1.7209 | 165.24 | 540000 | 1.7166 |
| 1.7189 | 171.36 | 560000 | 1.7206 |
| 1.7181 | 177.48 | 580000 | 1.7190 |
| 1.7159 | 183.6 | 600000 | 1.7194 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
apipond/ppo-LunarLander-v2
|
apipond
| 2022-09-25T13:37:42Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-25T13:37:18Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 226.89 +/- 17.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Okyx/fillmaskmodel
|
Okyx
| 2022-09-25T12:36:49Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-25T12:32:35Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: fillmaskmodel
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# fillmaskmodel
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1
|
weijiahaha/t5-small-summarization
|
weijiahaha
| 2022-09-25T12:21:01Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-26T07:38:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: t5-small-summarization
results: []
---
# t5-small-summarization
This model is a fine-tuned version of t5-small (https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6477
## Model description
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9195 | 1.0 | 718 | 1.6477 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
rram12/Pixelcopter-PLE-v0
|
rram12
| 2022-09-25T11:56:34Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-09-25T11:56:26Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 7.10 +/- 5.39
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
huynguyen208/distilbert-base-uncased-finetuned-ner
|
huynguyen208
| 2022-09-25T11:51:11Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-25T11:36:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0984
- Precision: 0.6767
- Recall: 0.6693
- F1: 0.6730
- Accuracy: 0.9711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2551 | 1.0 | 843 | 0.1340 | 0.5996 | 0.5465 | 0.5718 | 0.9623 |
| 0.1126 | 2.0 | 1686 | 0.1039 | 0.6635 | 0.6331 | 0.6480 | 0.9699 |
| 0.0857 | 3.0 | 2529 | 0.0984 | 0.6767 | 0.6693 | 0.6730 | 0.9711 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
ShadowTwin41/distilbert-base-uncased-finetuned-imdb
|
ShadowTwin41
| 2022-09-25T10:07:17Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-25T09:54:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 384
- eval_batch_size: 384
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 27 | 0.7668 |
| No log | 2.0 | 54 | 0.7282 |
| No log | 3.0 | 81 | 0.7165 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
shed-e/thucnews
|
shed-e
| 2022-09-25T08:29:09Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:load_train",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-09-25T07:46:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- load_train
metrics:
- accuracy
model-index:
- name: thucnews
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: load_train
type: load_train
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9433
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# thucnews
This model is a fine-tuned version of [hfl/rbt6](https://huggingface.co/hfl/rbt6) on the load_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3191
- Accuracy: 0.9433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2038 | 1.0 | 704 | 0.2018 | 0.9332 |
| 0.1403 | 2.0 | 1408 | 0.1829 | 0.9406 |
| 0.0894 | 3.0 | 2112 | 0.2073 | 0.9419 |
| 0.056 | 4.0 | 2816 | 0.2228 | 0.9408 |
| 0.0321 | 5.0 | 3520 | 0.2689 | 0.9417 |
| 0.0209 | 6.0 | 4224 | 0.2819 | 0.9431 |
| 0.0099 | 7.0 | 4928 | 0.3131 | 0.9421 |
| 0.0057 | 8.0 | 5632 | 0.3191 | 0.9433 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
jamescalam/mpnet-snli-negatives
|
jamescalam
| 2022-09-25T07:33:28Z | 13 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:snli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-22T08:27:42Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
license: mit
datasets:
- snli
---
# MPNet NLI
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been fine-tuned using the **S**tanford **N**atural **L**anguage **I**nference (SNLI) dataset (including negatives) and returns MRR@10 and MAP scores of ~0.95 on the SNLI test set.
Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp).
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('jamescalam/mpnet-snli-negatives')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jamescalam/mpnet-snli-negatives')
model = AutoModel.from_pretrained('jamescalam/mpnet-snli-negatives')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4660 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 466,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
jamescalam/mpnet-nli-sts
|
jamescalam
| 2022-09-25T07:28:38Z | 5 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:snli",
"dataset:stsb",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-09-25T07:13:18Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- en
license: mit
datasets:
- snli
- stsb
---
# MPNet NLI and STS
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It uses the [jamescalam/mpnet-snli-negatives](https://huggingface.co/jamescalam/mpnet-snli-negatives) model as a starting point, and is fine-tuned further on the **S**emantic **T**extual **S**imilarity **b**enchmark (STSb) dataset. Returning evaluation scores of ~0.9 cosine Pearson correlation using the STSb test set.
Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp).
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('jamescalam/mpnet-nli-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jamescalam/mpnet-nli-sts')
model = AutoModel.from_pretrained('jamescalam/mpnet-nli-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 180 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 25,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 90,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
|
ShadowTwin41/bert-finetuned-ner
|
ShadowTwin41
| 2022-09-25T07:26:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-09-25T07:18:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9127878490935816
- name: Recall
type: recall
value: 0.9405923931336251
- name: F1
type: f1
value: 0.9264815582262743
- name: Accuracy
type: accuracy
value: 0.9841937952551951
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0586
- Precision: 0.9128
- Recall: 0.9406
- F1: 0.9265
- Accuracy: 0.9842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 293 | 0.0844 | 0.8714 | 0.9123 | 0.8914 | 0.9760 |
| 0.1765 | 2.0 | 586 | 0.0601 | 0.9109 | 0.9357 | 0.9231 | 0.9834 |
| 0.1765 | 3.0 | 879 | 0.0586 | 0.9128 | 0.9406 | 0.9265 | 0.9842 |
### Framework versions
- Transformers 4.22.0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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