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yoshitomo-matsubara/bert-base-uncased-mrpc_from_bert-large-uncased-mrpc | 6b609d356394e9dbeaba75cfdd0f368620682565 | 2021-06-03T05:03:57.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:mrpc",
"transformers",
"mrpc",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-mrpc_from_bert-large-uncased-mrpc | 4 | null | transformers | 19,000 | ---
language: en
tags:
- bert
- mrpc
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- mrpc
metrics:
- f1
- accuracy
---
`bert-base-uncased` fine-tuned on MRPC dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/mrpc/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yoshitomo-matsubara/bert-base-uncased-qnli_from_bert-large-uncased-qnli | b96d3f6df6d856a3c4593bd1956f659333230884 | 2021-06-03T05:05:26.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:qnli",
"transformers",
"qnli",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-qnli_from_bert-large-uncased-qnli | 4 | null | transformers | 19,001 | ---
language: en
tags:
- bert
- qnli
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- qnli
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on QNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/qnli/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yoshitomo-matsubara/bert-base-uncased-qqp_from_bert-large-uncased-qqp | 20cfc23662ac0e3a5706cdba2878ba8f5f1fe195 | 2021-06-03T05:06:46.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:qqp",
"transformers",
"qqp",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-qqp_from_bert-large-uncased-qqp | 4 | null | transformers | 19,002 | ---
language: en
tags:
- bert
- qqp
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- qqp
metrics:
- f1
- accuracy
---
`bert-base-uncased` fine-tuned on QQP dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/qqp/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yoshitomo-matsubara/bert-base-uncased-sst2_from_bert-large-uncased-sst2 | df1c05873e39d591df2dd1040e87487e86eceb70 | 2021-06-03T05:09:20.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:sst2",
"transformers",
"sst2",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-sst2_from_bert-large-uncased-sst2 | 4 | null | transformers | 19,003 | ---
language: en
tags:
- bert
- sst2
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- sst2
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on SST-2 dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/sst2/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yoshitomo-matsubara/bert-base-uncased-stsb_from_bert-large-uncased-stsb | 62ab3f0ad56f78e946f88815fbce290cd6e4473d | 2021-06-03T05:10:42.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:stsb",
"transformers",
"stsb",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
] | text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-stsb_from_bert-large-uncased-stsb | 4 | null | transformers | 19,004 | ---
language: en
tags:
- bert
- stsb
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- stsb
metrics:
- pearson correlation
- spearman correlation
---
`bert-base-uncased` fine-tuned on STS-B dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/stsb/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yseop/text_smoothing | d2554e7535b0cc04cc6bba2fc487012f4e70e92b | 2021-10-27T10:50:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | yseop | null | yseop/text_smoothing | 4 | null | transformers | 19,005 | Entry not found |
ysslang/autonlp-test-459011902 | ff56206686e6bfa3ee3cb154c129612c001a5578 | 2021-12-30T17:05:31.000Z | [
"pytorch",
"bert",
"text-classification",
"zh",
"dataset:ysslang/autonlp-data-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | ysslang | null | ysslang/autonlp-test-459011902 | 4 | null | transformers | 19,006 | ---
tags: autonlp
language: zh
widget:
- text: "I love AutoNLP 🤗"
datasets:
- ysslang/autonlp-data-test
co2_eq_emissions: 10.9230691350863
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 459011902
- CO2 Emissions (in grams): 10.9230691350863
## Validation Metrics
- Loss: 0.7189690470695496
- Accuracy: 0.7453263867606497
- Macro F1: 0.630810193227066
- Micro F1: 0.7453263867606497
- Weighted F1: 0.7399327942874923
- Macro Precision: 0.656237447101913
- Micro Precision: 0.7453263867606497
- Weighted Precision: 0.7410161412822164
- Macro Recall: 0.6340140718425453
- Micro Recall: 0.7453263867606497
- Weighted Recall: 0.7453263867606497
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/ysslang/autonlp-test-459011902
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ysslang/autonlp-test-459011902", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ysslang/autonlp-test-459011902", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
zald/distilbert-base-uncased-finetuned-ner | a78c2e080bd6ec05eef5696f82cc073351b9c600 | 2021-08-27T16:39:55.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | zald | null | zald/distilbert-base-uncased-finetuned-ner | 4 | null | transformers | 19,007 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9835893688340985
---
<!-- 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 the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9253
- Recall: 0.9350
- F1: 0.9301
- Accuracy: 0.9836
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.237 | 1.0 | 878 | 0.0701 | 0.9131 | 0.9228 | 0.9179 | 0.9809 |
| 0.0509 | 2.0 | 1756 | 0.0617 | 0.9182 | 0.9333 | 0.9257 | 0.9826 |
| 0.0299 | 3.0 | 2634 | 0.0607 | 0.9253 | 0.9350 | 0.9301 | 0.9836 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.11.0
- Tokenizers 0.10.3
|
zhangxy-2019/cunlp-gpt2-dialog | ff60f0501727bb3c5ddbcb268385694c3199ee7e | 2021-05-23T14:07:17.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | zhangxy-2019 | null | zhangxy-2019/cunlp-gpt2-dialog | 4 | null | transformers | 19,008 | Entry not found |
zharry29/intent_fb-en_wh_id_rl | d581213820d29d589b58fe66013f956ee195c1ea | 2021-05-20T23:33:07.000Z | [
"pytorch",
"jax",
"roberta",
"multiple-choice",
"transformers"
] | multiple-choice | false | zharry29 | null | zharry29/intent_fb-en_wh_id_rl | 4 | null | transformers | 19,009 | Entry not found |
zharry29/step_benchmark_gpt | a44fc69555e7bbf0ad7bdb72e114c4563b6fc9c2 | 2021-05-23T14:09:43.000Z | [
"pytorch",
"gpt2",
"transformers"
] | null | false | zharry29 | null | zharry29/step_benchmark_gpt | 4 | null | transformers | 19,010 | Entry not found |
zhc/distilbert-base-uncased-finetuned-mrpc-test | 3418215240fc657af3356ae0e95c94bcb11c51f9 | 2021-09-11T04:10:39.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | zhc | null | zhc/distilbert-base-uncased-finetuned-mrpc-test | 4 | null | transformers | 19,011 | ---
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
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7034313725490197
- name: F1
type: f1
value: 0.8207407407407408
---
<!-- 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: 0.5708
- Accuracy: 0.7034
- F1: 0.8207
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 58 | 0.5708 | 0.7034 | 0.8207 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
zhuqing/roberta-base-uncased-netmums-classification-intersection | 5521cfb5028db4d0ba9374c7404e24de3f998588 | 2021-08-23T14:36:13.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | zhuqing | null | zhuqing/roberta-base-uncased-netmums-classification-intersection | 4 | null | transformers | 19,012 | Entry not found |
zitterbewegung/DialoGPT-medium-ja | 88b65cfedb4458ac3d8cb58ce7122ab320cbec39 | 2021-05-23T14:11:28.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | zitterbewegung | null | zitterbewegung/DialoGPT-medium-ja | 4 | null | transformers | 19,013 | Entry not found |
zwang199/autonlp-traffic-nlp-451311592 | 1d419c5fcdb99195071fd48c2aa0797e6c31c2c5 | 2021-12-27T18:31:57.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:zwang199/autonlp-data-traffic-nlp",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | zwang199 | null | zwang199/autonlp-traffic-nlp-451311592 | 4 | null | transformers | 19,014 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- zwang199/autonlp-data-traffic-nlp
co2_eq_emissions: 1.8697144296865242
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 451311592
- CO2 Emissions (in grams): 1.8697144296865242
## Validation Metrics
- Loss: 0.4544260799884796
- Accuracy: 0.8042452830188679
- Precision: 0.8331288343558282
- Recall: 0.8573232323232324
- AUC: 0.8759811658249159
- F1: 0.8450528935905414
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/zwang199/autonlp-traffic-nlp-451311592
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("zwang199/autonlp-traffic-nlp-451311592", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("zwang199/autonlp-traffic-nlp-451311592", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
zwang199/autonlp-traffic_nlp_binary-537215209 | d86e0d5a1b8398c0aca0d39f6ec22322d712bb73 | 2022-01-28T19:34:25.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:zwang199/autonlp-data-traffic_nlp_binary",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | zwang199 | null | zwang199/autonlp-traffic_nlp_binary-537215209 | 4 | null | transformers | 19,015 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- zwang199/autonlp-data-traffic_nlp_binary
co2_eq_emissions: 1.171798205242445
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 537215209
- CO2 Emissions (in grams): 1.171798205242445
## Validation Metrics
- Loss: 0.3879534602165222
- Accuracy: 0.8597449908925319
- Precision: 0.8318042813455657
- Recall: 0.9251700680272109
- AUC: 0.9230158730158731
- F1: 0.8760064412238325
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/zwang199/autonlp-traffic_nlp_binary-537215209
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("zwang199/autonlp-traffic_nlp_binary-537215209", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
zyl1024/bert-base-cased-finetuned-qqp | 6799e7ed1b9d231f8fc685ce203f4971e2b390dd | 2022-03-12T17:08:41.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | zyl1024 | null | zyl1024/bert-base-cased-finetuned-qqp | 4 | null | transformers | 19,016 | Entry not found |
wietsedv/xlm-roberta-base-ft-udpos28-be | f1ebda2082a828dcf185c1e585122fa9b3b5f0c0 | 2022-02-25T09:58:04.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"be",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-be | 4 | null | transformers | 19,017 |
---
language:
- be
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-be
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 77.5
- type: accuracy
name: Dutch Test accuracy
value: 80.7
- type: accuracy
name: German Test accuracy
value: 79.4
- type: accuracy
name: Italian Test accuracy
value: 80.1
- type: accuracy
name: French Test accuracy
value: 81.2
- type: accuracy
name: Spanish Test accuracy
value: 83.6
- type: accuracy
name: Russian Test accuracy
value: 95.3
- type: accuracy
name: Swedish Test accuracy
value: 85.9
- type: accuracy
name: Norwegian Test accuracy
value: 80.0
- type: accuracy
name: Danish Test accuracy
value: 84.3
- type: accuracy
name: Low Saxon Test accuracy
value: 25.5
- type: accuracy
name: Akkadian Test accuracy
value: 8.2
- type: accuracy
name: Armenian Test accuracy
value: 87.4
- type: accuracy
name: Welsh Test accuracy
value: 64.2
- type: accuracy
name: Old East Slavic Test accuracy
value: 79.2
- type: accuracy
name: Albanian Test accuracy
value: 76.2
- type: accuracy
name: Slovenian Test accuracy
value: 80.1
- type: accuracy
name: Guajajara Test accuracy
value: 14.3
- type: accuracy
name: Kurmanji Test accuracy
value: 75.0
- type: accuracy
name: Turkish Test accuracy
value: 73.0
- type: accuracy
name: Finnish Test accuracy
value: 83.6
- type: accuracy
name: Indonesian Test accuracy
value: 75.2
- type: accuracy
name: Ukrainian Test accuracy
value: 94.4
- type: accuracy
name: Polish Test accuracy
value: 88.9
- type: accuracy
name: Portuguese Test accuracy
value: 83.0
- type: accuracy
name: Kazakh Test accuracy
value: 81.1
- type: accuracy
name: Latin Test accuracy
value: 75.6
- type: accuracy
name: Old French Test accuracy
value: 27.0
- type: accuracy
name: Buryat Test accuracy
value: 61.1
- type: accuracy
name: Kaapor Test accuracy
value: 2.9
- type: accuracy
name: Korean Test accuracy
value: 61.8
- type: accuracy
name: Estonian Test accuracy
value: 83.0
- type: accuracy
name: Croatian Test accuracy
value: 90.4
- type: accuracy
name: Gothic Test accuracy
value: 1.7
- type: accuracy
name: Swiss German Test accuracy
value: 31.4
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 19.4
- type: accuracy
name: Naija Test accuracy
value: 13.0
- type: accuracy
name: Latvian Test accuracy
value: 89.5
- type: accuracy
name: Chinese Test accuracy
value: 52.7
- type: accuracy
name: Tagalog Test accuracy
value: 70.5
- type: accuracy
name: Bambara Test accuracy
value: 15.9
- type: accuracy
name: Lithuanian Test accuracy
value: 89.9
- type: accuracy
name: Galician Test accuracy
value: 84.0
- type: accuracy
name: Vietnamese Test accuracy
value: 63.9
- type: accuracy
name: Greek Test accuracy
value: 79.1
- type: accuracy
name: Catalan Test accuracy
value: 80.5
- type: accuracy
name: Czech Test accuracy
value: 88.3
- type: accuracy
name: Erzya Test accuracy
value: 52.7
- type: accuracy
name: Bhojpuri Test accuracy
value: 51.6
- type: accuracy
name: Thai Test accuracy
value: 63.4
- type: accuracy
name: Marathi Test accuracy
value: 85.3
- type: accuracy
name: Basque Test accuracy
value: 74.9
- type: accuracy
name: Slovak Test accuracy
value: 88.6
- type: accuracy
name: Kiche Test accuracy
value: 20.7
- type: accuracy
name: Yoruba Test accuracy
value: 16.2
- type: accuracy
name: Warlpiri Test accuracy
value: 20.6
- type: accuracy
name: Tamil Test accuracy
value: 85.4
- type: accuracy
name: Maltese Test accuracy
value: 12.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 66.9
- type: accuracy
name: Icelandic Test accuracy
value: 81.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 22.8
- type: accuracy
name: Urdu Test accuracy
value: 67.2
- type: accuracy
name: Romanian Test accuracy
value: 81.7
- type: accuracy
name: Persian Test accuracy
value: 75.2
- type: accuracy
name: Apurina Test accuracy
value: 22.7
- type: accuracy
name: Japanese Test accuracy
value: 38.7
- type: accuracy
name: Hungarian Test accuracy
value: 76.2
- type: accuracy
name: Hindi Test accuracy
value: 73.1
- type: accuracy
name: Classical Chinese Test accuracy
value: 28.0
- type: accuracy
name: Komi Permyak Test accuracy
value: 49.0
- type: accuracy
name: Faroese Test accuracy
value: 72.2
- type: accuracy
name: Sanskrit Test accuracy
value: 9.1
- type: accuracy
name: Livvi Test accuracy
value: 54.0
- type: accuracy
name: Arabic Test accuracy
value: 82.8
- type: accuracy
name: Wolof Test accuracy
value: 13.6
- type: accuracy
name: Bulgarian Test accuracy
value: 91.6
- type: accuracy
name: Akuntsu Test accuracy
value: 11.1
- type: accuracy
name: Makurap Test accuracy
value: 1.4
- type: accuracy
name: Kangri Test accuracy
value: 51.4
- type: accuracy
name: Breton Test accuracy
value: 52.2
- type: accuracy
name: Telugu Test accuracy
value: 83.6
- type: accuracy
name: Cantonese Test accuracy
value: 51.7
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.7
- type: accuracy
name: Karelian Test accuracy
value: 67.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 67.0
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 67.4
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.8
- type: accuracy
name: Irish Test accuracy
value: 59.2
- type: accuracy
name: Nayini Test accuracy
value: 43.6
- type: accuracy
name: Munduruku Test accuracy
value: 7.6
- type: accuracy
name: Manx Test accuracy
value: 16.9
- type: accuracy
name: Skolt Sami Test accuracy
value: 25.6
- type: accuracy
name: Afrikaans Test accuracy
value: 76.7
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 18.3
- type: accuracy
name: Belarusian Test accuracy
value: 98.3
- type: accuracy
name: Serbian Test accuracy
value: 91.0
- type: accuracy
name: Moksha Test accuracy
value: 50.1
- type: accuracy
name: Western Armenian Test accuracy
value: 78.4
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 48.6
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 89.6
- type: accuracy
name: Uyghur Test accuracy
value: 75.8
- type: accuracy
name: Chukchi Test accuracy
value: 39.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Belarusian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-be")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-bg | 8ec1399237ffadeb6a98e191612025a6bfd71fe5 | 2022-02-25T09:58:06.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"bg",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-bg | 4 | null | transformers | 19,018 |
---
language:
- bg
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-bg
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 85.0
- type: accuracy
name: Dutch Test accuracy
value: 81.6
- type: accuracy
name: German Test accuracy
value: 82.6
- type: accuracy
name: Italian Test accuracy
value: 82.5
- type: accuracy
name: French Test accuracy
value: 83.1
- type: accuracy
name: Spanish Test accuracy
value: 85.5
- type: accuracy
name: Russian Test accuracy
value: 92.7
- type: accuracy
name: Swedish Test accuracy
value: 89.5
- type: accuracy
name: Norwegian Test accuracy
value: 84.5
- type: accuracy
name: Danish Test accuracy
value: 88.4
- type: accuracy
name: Low Saxon Test accuracy
value: 56.5
- type: accuracy
name: Akkadian Test accuracy
value: 40.5
- type: accuracy
name: Armenian Test accuracy
value: 86.6
- type: accuracy
name: Welsh Test accuracy
value: 66.7
- type: accuracy
name: Old East Slavic Test accuracy
value: 76.7
- type: accuracy
name: Albanian Test accuracy
value: 78.4
- type: accuracy
name: Slovenian Test accuracy
value: 84.0
- type: accuracy
name: Guajajara Test accuracy
value: 36.2
- type: accuracy
name: Kurmanji Test accuracy
value: 78.5
- type: accuracy
name: Turkish Test accuracy
value: 77.6
- type: accuracy
name: Finnish Test accuracy
value: 84.5
- type: accuracy
name: Indonesian Test accuracy
value: 83.1
- type: accuracy
name: Ukrainian Test accuracy
value: 93.4
- type: accuracy
name: Polish Test accuracy
value: 91.8
- type: accuracy
name: Portuguese Test accuracy
value: 85.8
- type: accuracy
name: Kazakh Test accuracy
value: 80.9
- type: accuracy
name: Latin Test accuracy
value: 79.6
- type: accuracy
name: Old French Test accuracy
value: 60.9
- type: accuracy
name: Buryat Test accuracy
value: 61.9
- type: accuracy
name: Kaapor Test accuracy
value: 24.6
- type: accuracy
name: Korean Test accuracy
value: 61.0
- type: accuracy
name: Estonian Test accuracy
value: 86.8
- type: accuracy
name: Croatian Test accuracy
value: 92.8
- type: accuracy
name: Gothic Test accuracy
value: 28.7
- type: accuracy
name: Swiss German Test accuracy
value: 49.2
- type: accuracy
name: Assyrian Test accuracy
value: 16.1
- type: accuracy
name: North Sami Test accuracy
value: 46.2
- type: accuracy
name: Naija Test accuracy
value: 43.1
- type: accuracy
name: Latvian Test accuracy
value: 88.6
- type: accuracy
name: Chinese Test accuracy
value: 37.9
- type: accuracy
name: Tagalog Test accuracy
value: 81.6
- type: accuracy
name: Bambara Test accuracy
value: 30.0
- type: accuracy
name: Lithuanian Test accuracy
value: 85.7
- type: accuracy
name: Galician Test accuracy
value: 84.1
- type: accuracy
name: Vietnamese Test accuracy
value: 65.9
- type: accuracy
name: Greek Test accuracy
value: 79.9
- type: accuracy
name: Catalan Test accuracy
value: 83.3
- type: accuracy
name: Czech Test accuracy
value: 92.3
- type: accuracy
name: Erzya Test accuracy
value: 53.5
- type: accuracy
name: Bhojpuri Test accuracy
value: 56.5
- type: accuracy
name: Thai Test accuracy
value: 57.4
- type: accuracy
name: Marathi Test accuracy
value: 85.9
- type: accuracy
name: Basque Test accuracy
value: 75.8
- type: accuracy
name: Slovak Test accuracy
value: 91.3
- type: accuracy
name: Kiche Test accuracy
value: 42.5
- type: accuracy
name: Yoruba Test accuracy
value: 31.4
- type: accuracy
name: Warlpiri Test accuracy
value: 40.1
- type: accuracy
name: Tamil Test accuracy
value: 83.3
- type: accuracy
name: Maltese Test accuracy
value: 34.0
- type: accuracy
name: Ancient Greek Test accuracy
value: 61.9
- type: accuracy
name: Icelandic Test accuracy
value: 82.8
- type: accuracy
name: Mbya Guarani Test accuracy
value: 34.4
- type: accuracy
name: Urdu Test accuracy
value: 72.5
- type: accuracy
name: Romanian Test accuracy
value: 84.0
- type: accuracy
name: Persian Test accuracy
value: 77.7
- type: accuracy
name: Apurina Test accuracy
value: 48.2
- type: accuracy
name: Japanese Test accuracy
value: 27.8
- type: accuracy
name: Hungarian Test accuracy
value: 76.9
- type: accuracy
name: Hindi Test accuracy
value: 75.0
- type: accuracy
name: Classical Chinese Test accuracy
value: 31.2
- type: accuracy
name: Komi Permyak Test accuracy
value: 52.2
- type: accuracy
name: Faroese Test accuracy
value: 78.8
- type: accuracy
name: Sanskrit Test accuracy
value: 39.9
- type: accuracy
name: Livvi Test accuracy
value: 67.5
- type: accuracy
name: Arabic Test accuracy
value: 84.4
- type: accuracy
name: Wolof Test accuracy
value: 39.8
- type: accuracy
name: Bulgarian Test accuracy
value: 99.3
- type: accuracy
name: Akuntsu Test accuracy
value: 39.6
- type: accuracy
name: Makurap Test accuracy
value: 28.1
- type: accuracy
name: Kangri Test accuracy
value: 50.5
- type: accuracy
name: Breton Test accuracy
value: 60.8
- type: accuracy
name: Telugu Test accuracy
value: 82.7
- type: accuracy
name: Cantonese Test accuracy
value: 42.1
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 47.9
- type: accuracy
name: Karelian Test accuracy
value: 74.0
- type: accuracy
name: Upper Sorbian Test accuracy
value: 79.1
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.7
- type: accuracy
name: Irish Test accuracy
value: 65.6
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 28.5
- type: accuracy
name: Manx Test accuracy
value: 40.1
- type: accuracy
name: Skolt Sami Test accuracy
value: 41.3
- type: accuracy
name: Afrikaans Test accuracy
value: 81.5
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 44.6
- type: accuracy
name: Belarusian Test accuracy
value: 92.3
- type: accuracy
name: Serbian Test accuracy
value: 92.1
- type: accuracy
name: Moksha Test accuracy
value: 49.2
- type: accuracy
name: Western Armenian Test accuracy
value: 83.2
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 58.2
- type: accuracy
name: Khunsari Test accuracy
value: 47.3
- type: accuracy
name: Hebrew Test accuracy
value: 88.5
- type: accuracy
name: Uyghur Test accuracy
value: 75.8
- type: accuracy
name: Chukchi Test accuracy
value: 39.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Bulgarian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-bg")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-bg")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-cu | 6d280510ae537cdf7c2bed696a3506acc20319aa | 2022-02-25T09:58:11.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"cu",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-cu | 4 | null | transformers | 19,019 |
---
language:
- cu
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-cu
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 60.1
- type: accuracy
name: Dutch Test accuracy
value: 58.7
- type: accuracy
name: German Test accuracy
value: 59.9
- type: accuracy
name: Italian Test accuracy
value: 55.8
- type: accuracy
name: French Test accuracy
value: 59.5
- type: accuracy
name: Spanish Test accuracy
value: 58.4
- type: accuracy
name: Russian Test accuracy
value: 63.2
- type: accuracy
name: Swedish Test accuracy
value: 64.5
- type: accuracy
name: Norwegian Test accuracy
value: 58.0
- type: accuracy
name: Danish Test accuracy
value: 59.9
- type: accuracy
name: Low Saxon Test accuracy
value: 38.3
- type: accuracy
name: Akkadian Test accuracy
value: 30.4
- type: accuracy
name: Armenian Test accuracy
value: 57.3
- type: accuracy
name: Welsh Test accuracy
value: 50.8
- type: accuracy
name: Old East Slavic Test accuracy
value: 72.5
- type: accuracy
name: Albanian Test accuracy
value: 62.5
- type: accuracy
name: Slovenian Test accuracy
value: 60.4
- type: accuracy
name: Guajajara Test accuracy
value: 20.5
- type: accuracy
name: Kurmanji Test accuracy
value: 57.1
- type: accuracy
name: Turkish Test accuracy
value: 53.3
- type: accuracy
name: Finnish Test accuracy
value: 61.2
- type: accuracy
name: Indonesian Test accuracy
value: 56.0
- type: accuracy
name: Ukrainian Test accuracy
value: 62.2
- type: accuracy
name: Polish Test accuracy
value: 63.2
- type: accuracy
name: Portuguese Test accuracy
value: 57.8
- type: accuracy
name: Kazakh Test accuracy
value: 55.1
- type: accuracy
name: Latin Test accuracy
value: 63.9
- type: accuracy
name: Old French Test accuracy
value: 55.9
- type: accuracy
name: Buryat Test accuracy
value: 36.1
- type: accuracy
name: Kaapor Test accuracy
value: 26.2
- type: accuracy
name: Korean Test accuracy
value: 46.0
- type: accuracy
name: Estonian Test accuracy
value: 62.0
- type: accuracy
name: Croatian Test accuracy
value: 64.4
- type: accuracy
name: Gothic Test accuracy
value: 32.4
- type: accuracy
name: Swiss German Test accuracy
value: 41.3
- type: accuracy
name: Assyrian Test accuracy
value: 15.0
- type: accuracy
name: North Sami Test accuracy
value: 22.6
- type: accuracy
name: Naija Test accuracy
value: 38.0
- type: accuracy
name: Latvian Test accuracy
value: 61.9
- type: accuracy
name: Chinese Test accuracy
value: 42.8
- type: accuracy
name: Tagalog Test accuracy
value: 57.1
- type: accuracy
name: Bambara Test accuracy
value: 13.4
- type: accuracy
name: Lithuanian Test accuracy
value: 62.7
- type: accuracy
name: Galician Test accuracy
value: 59.7
- type: accuracy
name: Vietnamese Test accuracy
value: 50.0
- type: accuracy
name: Greek Test accuracy
value: 57.0
- type: accuracy
name: Catalan Test accuracy
value: 57.0
- type: accuracy
name: Czech Test accuracy
value: 63.9
- type: accuracy
name: Erzya Test accuracy
value: 20.4
- type: accuracy
name: Bhojpuri Test accuracy
value: 36.7
- type: accuracy
name: Thai Test accuracy
value: 59.0
- type: accuracy
name: Marathi Test accuracy
value: 46.0
- type: accuracy
name: Basque Test accuracy
value: 55.1
- type: accuracy
name: Slovak Test accuracy
value: 64.2
- type: accuracy
name: Kiche Test accuracy
value: 15.0
- type: accuracy
name: Yoruba Test accuracy
value: 11.1
- type: accuracy
name: Warlpiri Test accuracy
value: 25.9
- type: accuracy
name: Tamil Test accuracy
value: 60.3
- type: accuracy
name: Maltese Test accuracy
value: 21.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 59.1
- type: accuracy
name: Icelandic Test accuracy
value: 62.5
- type: accuracy
name: Mbya Guarani Test accuracy
value: 13.1
- type: accuracy
name: Urdu Test accuracy
value: 49.0
- type: accuracy
name: Romanian Test accuracy
value: 63.4
- type: accuracy
name: Persian Test accuracy
value: 60.8
- type: accuracy
name: Apurina Test accuracy
value: 14.1
- type: accuracy
name: Japanese Test accuracy
value: 34.1
- type: accuracy
name: Hungarian Test accuracy
value: 53.0
- type: accuracy
name: Hindi Test accuracy
value: 51.5
- type: accuracy
name: Classical Chinese Test accuracy
value: 40.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 25.7
- type: accuracy
name: Faroese Test accuracy
value: 56.9
- type: accuracy
name: Sanskrit Test accuracy
value: 37.6
- type: accuracy
name: Livvi Test accuracy
value: 41.5
- type: accuracy
name: Arabic Test accuracy
value: 65.4
- type: accuracy
name: Wolof Test accuracy
value: 22.1
- type: accuracy
name: Bulgarian Test accuracy
value: 65.4
- type: accuracy
name: Akuntsu Test accuracy
value: 16.2
- type: accuracy
name: Makurap Test accuracy
value: 6.8
- type: accuracy
name: Kangri Test accuracy
value: 33.9
- type: accuracy
name: Breton Test accuracy
value: 43.5
- type: accuracy
name: Telugu Test accuracy
value: 52.1
- type: accuracy
name: Cantonese Test accuracy
value: 37.9
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 94.9
- type: accuracy
name: Karelian Test accuracy
value: 49.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 51.8
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 51.5
- type: accuracy
name: Komi Zyrian Test accuracy
value: 21.8
- type: accuracy
name: Irish Test accuracy
value: 41.6
- type: accuracy
name: Nayini Test accuracy
value: 39.7
- type: accuracy
name: Munduruku Test accuracy
value: 11.8
- type: accuracy
name: Manx Test accuracy
value: 19.8
- type: accuracy
name: Skolt Sami Test accuracy
value: 20.7
- type: accuracy
name: Afrikaans Test accuracy
value: 62.4
- type: accuracy
name: Old Turkish Test accuracy
value: 4.5
- type: accuracy
name: Tupinamba Test accuracy
value: 18.2
- type: accuracy
name: Belarusian Test accuracy
value: 66.3
- type: accuracy
name: Serbian Test accuracy
value: 65.4
- type: accuracy
name: Moksha Test accuracy
value: 23.3
- type: accuracy
name: Western Armenian Test accuracy
value: 58.5
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 41.3
- type: accuracy
name: Khunsari Test accuracy
value: 35.1
- type: accuracy
name: Hebrew Test accuracy
value: 58.3
- type: accuracy
name: Uyghur Test accuracy
value: 52.8
- type: accuracy
name: Chukchi Test accuracy
value: 12.5
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Old Church Slavonic
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cu")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cu")
```
|
DoyyingFace/bert-asian-hate-tweets-self-unclean | 362e4f4a31d3e877f383139a8d0100acf8cc4f5d | 2022-02-24T10:25:37.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-unclean | 4 | null | transformers | 19,020 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-asonam-unclean | 4f16941e2706fb883e4010b5495fe2013de43135 | 2022-02-24T13:04:35.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-asonam-unclean | 4 | null | transformers | 19,021 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-asonam-clean | 355503a28854563e5b6f42ef991becce2d8839ea | 2022-02-24T13:06:48.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-asonam-clean | 4 | null | transformers | 19,022 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-concat-unclean | 7bdc74994a376ba4ed9b7312dde5e683554f2ded | 2022-02-24T13:46:22.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-concat-unclean | 4 | null | transformers | 19,023 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-concat-clean | b6568690192aff74030344c6273f510ab07c8b7d | 2022-02-24T13:57:51.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-concat-clean | 4 | null | transformers | 19,024 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-concat-unclean-with-clean-valid | 84ee076969b3e72dd14f649603f1e2eaf7853582 | 2022-02-24T15:14:37.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-concat-unclean-with-clean-valid | 4 | null | transformers | 19,025 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-self-clean-with-unclean-valid | 52a34f154629b509c2d6e040663ff594fafdf593 | 2022-02-24T15:42:38.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-clean-with-unclean-valid | 4 | null | transformers | 19,026 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | 96218d3451941b55888d22f7c6b32bd703ef4e04 | 2022-02-24T16:06:36.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | 4 | null | transformers | 19,027 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 | b27c52182a494bc921dc65ecce6e6cf2c2d808bc | 2022-02-24T16:44:13.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 | 4 | null | transformers | 19,028 | Entry not found |
anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0 | b360fd97b8128187c6d8f33c50a3459f981db370 | 2022-02-24T21:54:26.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0 | 4 | null | transformers | 19,029 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-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. -->
# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4 | 92658827d041acc3e40ee56e80eebc89a807f8f7 | 2022-02-25T01:28:25.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4 | 4 | null | transformers | 19,030 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-few-shot-k-128-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. -->
# bert-base-uncased-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-4 | c1b96710a7ea66c388f901df12378614f6841bff | 2022-02-25T03:09:00.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-4 | 4 | null | transformers | 19,031 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-12 | edad78f72b8800057a691cc3d8456e1fe3771f0e | 2022-02-25T03:21:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-unclean-freeze-12 | 4 | null | transformers | 19,032 | Entry not found |
anantoj/wav2vec2-xls-r-300m-adult-child-cls | 044719fc86338eda09c2eb107a629a7c46e5fdfb | 2022-02-25T07:47:57.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | anantoj | null | anantoj/wav2vec2-xls-r-300m-adult-child-cls | 4 | null | transformers | 19,033 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: wav2vec2-xls-r-300m-adult-child-cls
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-xls-r-300m-adult-child-cls
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1770
- Accuracy: 0.9404
- F1: 0.9440
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.25 | 1.0 | 383 | 0.2516 | 0.9077 | 0.9106 |
| 0.2052 | 2.0 | 766 | 0.2138 | 0.9321 | 0.9353 |
| 0.1901 | 3.0 | 1149 | 0.1770 | 0.9404 | 0.9440 |
| 0.2255 | 4.0 | 1532 | 0.1794 | 0.9404 | 0.9440 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | f8743b1e0e5593966d84fbe1544a1cbd15e07a8a | 2022-02-25T04:40:13.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | 4 | null | transformers | 19,034 | Entry not found |
BigSalmon/GPTNeo350MInformalToFormalLincoln4 | 09b11fd2f707d4901fa06788c80d0e652c5e7355 | 2022-02-25T05:04:06.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/GPTNeo350MInformalToFormalLincoln4 | 4 | null | transformers | 19,035 | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel.
Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle.
Essay Intro (
``` |
Davlan/xlm-roberta-base-finetuned-lingala | 6350a63ff26b3ef1aa45298a4dba208622f206b3 | 2022-02-25T15:36:10.000Z | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Davlan | null | Davlan/xlm-roberta-base-finetuned-lingala | 4 | 1 | transformers | 19,036 | ---
license: apache-2.0
---
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0 | c2b6e21b8e82c4502597508b592bdfdd04dbec0a | 2022-02-25T19:16:24.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0 | 4 | null | transformers | 19,037 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-2 | 7c7467da0544daad7916e4e58ae1ea39ef190059 | 2022-02-25T19:29:02.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-2 | 4 | null | transformers | 19,038 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-6 | 6c7a6de9a74e0dcf19062804017f0708dd2d4c4c | 2022-02-25T21:27:44.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-6 | 4 | null | transformers | 19,039 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-6
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. -->
# spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-6
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-2 | 0e4c5a3e9eb67c0c053c1429575c7aa1cdf90465 | 2022-02-25T22:28:13.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-2 | 4 | null | transformers | 19,040 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-64-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. -->
# spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DoyyingFace/bert-asian-hate-tweets-self-unclean-small | fea2648b590b9b10cc71c5dcdeea3b74ccd78a4a | 2022-02-26T02:45:51.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-unclean-small | 4 | null | transformers | 19,041 | Entry not found |
DoyyingFace/bert-asian-hate-tweets-self-clean-small | c457e34a8210bcbc58f55d1a3a4b1f2fbf78c972 | 2022-02-26T02:51:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-clean-small | 4 | null | transformers | 19,042 | Entry not found |
ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 | 1fd847361186bcf6566e978ff2f795122484edd5 | 2022-02-26T03:03:20.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 | 4 | null | transformers | 19,043 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
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. -->
# finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4345
- Accuracy: 0.8321
- F1: 0.8904
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3922 | 0.8061 | 0.8747 |
| No log | 2.0 | 390 | 0.3764 | 0.8171 | 0.8837 |
| 0.4074 | 3.0 | 585 | 0.3873 | 0.8220 | 0.8843 |
| 0.4074 | 4.0 | 780 | 0.4361 | 0.8232 | 0.8854 |
| 0.4074 | 5.0 | 975 | 0.4555 | 0.8159 | 0.8793 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26 | 535177c027ef3ac14265accbe29dde63ca82ec7c | 2022-02-26T03:08:55.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26 | 4 | null | transformers | 19,044 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_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. -->
# finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 | e74e66159536b4e4ec8420b4854294831234f3f0 | 2022-02-26T03:14:31.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 | 4 | null | transformers | 19,045 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01
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. -->
# finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch6 | a39b0fe7457cc58ae708a90c2fb2e73a14ab9ffd | 2022-02-26T03:17:53.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | DoyyingFace | null | DoyyingFace/bert-asian-hate-tweets-self-clean-small-epoch6 | 4 | null | transformers | 19,046 | Entry not found |
ali2066/finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39 | 2b758f4dffc42a8e85ce343efe2a0e792c230523 | 2022-02-26T03:31:07.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39 | 4 | null | transformers | 19,047 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39
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. -->
# finetuned_sentence_itr5_2e-05_all_26_02_2022-04_25_39
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr7_2e-05_all_26_02_2022-04_36_45 | 1592a27af206197bdc5b5d499b29c98831dfbdb3 | 2022-02-26T03:42:02.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr7_2e-05_all_26_02_2022-04_36_45 | 4 | null | transformers | 19,048 | Entry not found |
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0 | db73dc8f2d43dc620696313c3caec6350f3f32b9 | 2022-02-26T04:19:12.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0 | 4 | null | transformers | 19,049 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-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. -->
# spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-2 | d65e31fd22ef135a3abec502cb45ed15d1fd85ee | 2022-02-26T05:38:42.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | anas-awadalla | null | anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-2 | 4 | null | transformers | 19,050 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-256-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. -->
# spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) 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: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
swcrazyfan/Dekingify-T5-Large | 191a5ca5c930a0c90fd93414e69bf186bf16b408 | 2022-03-06T09:44:13.000Z | [
"pytorch",
"onnx",
"t5",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | swcrazyfan | null | swcrazyfan/Dekingify-T5-Large | 4 | null | transformers | 19,051 | ---
license: apache-2.0
---
|
abhinema/distillgpt2 | 80cb469e58ce1bb5348782f9848242806e6ef92c | 2022-02-27T04:08:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | abhinema | null | abhinema/distillgpt2 | 4 | null | transformers | 19,052 | Entry not found |
ali2066/finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22 | 8cc876c76f22401b13add392758ee969ee21f637 | 2022-02-27T16:38:53.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22 | 4 | null | transformers | 19,053 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22
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. -->
# finetuned_sentence_itr1_2e-05_all_27_02_2022-17_33_22
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4095
- Accuracy: 0.8263
- F1: 0.8865
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 |
| No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 |
| 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 |
| 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 |
| 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 | be4762e02f28f2684e3c6f9d0c3910e2bb227cd2 | 2022-02-27T16:44:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 | 4 | null | transformers | 19,054 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58
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. -->
# finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4095
- Accuracy: 0.8263
- F1: 0.8865
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 |
| No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 |
| 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 |
| 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 |
| 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32 | d316e24b0f20c16fb36481516b2a8733e37acfd9 | 2022-02-27T16:50:01.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32 | 4 | null | transformers | 19,055 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32
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. -->
# finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4095
- Accuracy: 0.8263
- F1: 0.8865
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 |
| No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 |
| 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 |
| 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 |
| 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05 | 7594be3f654037ec7bf182c0360fdea6a45503f2 | 2022-02-27T16:55:39.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05 | 4 | null | transformers | 19,056 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05
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. -->
# finetuned_sentence_itr4_2e-05_all_27_02_2022-17_50_05
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4095
- Accuracy: 0.8263
- F1: 0.8865
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 |
| No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 |
| 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 |
| 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 |
| 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 | c32b0302ce6c724f503e2a4767635a1eed3f3791 | 2022-02-27T17:01:16.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43 | 4 | null | transformers | 19,057 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43
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. -->
# finetuned_sentence_itr0_0.0002_all_27_02_2022-17_55_43
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7600
- Accuracy: 0.8144
- F1: 0.8788
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 |
| No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 |
| 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 |
| 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 |
| 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22 | 145fa9dca570360d48c5df4dc0515d1853aa6b90 | 2022-02-27T17:06:54.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22 | 4 | null | transformers | 19,058 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22
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. -->
# finetuned_sentence_itr1_0.0002_all_27_02_2022-18_01_22
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7600
- Accuracy: 0.8144
- F1: 0.8788
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 |
| No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 |
| 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 |
| 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 |
| 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 | db8b2c75f10ebd6c0995fce07a4235412daa6381 | 2022-02-27T17:12:30.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 | 4 | null | transformers | 19,059 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59
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. -->
# finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7600
- Accuracy: 0.8144
- F1: 0.8788
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 |
| No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 |
| 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 |
| 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 |
| 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 | 31fb447e98d4ae7d2fdd1ce519c25fcde2c19d4a | 2022-02-27T17:18:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 | 4 | null | transformers | 19,060 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34
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. -->
# finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7600
- Accuracy: 0.8144
- F1: 0.8788
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 |
| No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 |
| 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 |
| 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 |
| 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11 | 7242432997977fc93643bce48bb5d4b02c9342f4 | 2022-02-27T17:23:43.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11 | 4 | null | transformers | 19,061 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11
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. -->
# finetuned_sentence_itr4_0.0002_all_27_02_2022-18_18_11
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7600
- Accuracy: 0.8144
- F1: 0.8788
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 |
| No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 |
| 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 |
| 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 |
| 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48 | 4c356b9e7e254263ad9e1f215e7dcee2dfbcad28 | 2022-02-27T17:29:20.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48 | 4 | null | transformers | 19,062 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48
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. -->
# finetuned_sentence_itr0_3e-05_all_27_02_2022-18_23_48
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3962
- Accuracy: 0.8231
- F1: 0.8873
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 |
| No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 |
| 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 |
| 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 |
| 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24 | d108f10aaf0afa62019cfca12d663050c22d4e37 | 2022-02-27T17:34:56.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24 | 4 | null | transformers | 19,063 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24
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. -->
# finetuned_sentence_itr1_3e-05_all_27_02_2022-18_29_24
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3962
- Accuracy: 0.8231
- F1: 0.8873
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 |
| No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 |
| 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 |
| 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 |
| 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40 | 08d942efb808b5a683404a6e7eedade4c8f01ab6 | 2022-02-27T17:46:15.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40 | 4 | null | transformers | 19,064 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40
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. -->
# finetuned_sentence_itr3_3e-05_all_27_02_2022-18_40_40
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3962
- Accuracy: 0.8231
- F1: 0.8873
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 |
| No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 |
| 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 |
| 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 |
| 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19 | 896513e5117a34f76e8e36f1f206c7e123f1859e | 2022-02-27T17:51:50.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19 | 4 | null | transformers | 19,065 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19
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. -->
# finetuned_sentence_itr4_3e-05_all_27_02_2022-18_46_19
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3962
- Accuracy: 0.8231
- F1: 0.8873
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 |
| No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 |
| 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 |
| 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 |
| 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09 | 409170cb78170a72e924e31bb2b909b9331ee0e2 | 2022-02-27T17:56:27.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09 | 4 | null | transformers | 19,066 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09
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. -->
# finetuned_sentence_itr1_2e-05_webDiscourse_27_02_2022-18_54_09
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6049
- Accuracy: 0.6926
- F1: 0.4160
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.5835 | 0.71 | 0.0333 |
| No log | 2.0 | 96 | 0.5718 | 0.715 | 0.3871 |
| No log | 3.0 | 144 | 0.5731 | 0.715 | 0.4 |
| No log | 4.0 | 192 | 0.6009 | 0.705 | 0.3516 |
| No log | 5.0 | 240 | 0.6122 | 0.7 | 0.4000 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 | 5086b2d3898e7a3cc18da836f21e75a62ba006ce | 2022-02-27T17:59:00.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 | 4 | null | transformers | 19,067 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32
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. -->
# finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6049
- Accuracy: 0.6926
- F1: 0.4160
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.5835 | 0.71 | 0.0333 |
| No log | 2.0 | 96 | 0.5718 | 0.715 | 0.3871 |
| No log | 3.0 | 144 | 0.5731 | 0.715 | 0.4 |
| No log | 4.0 | 192 | 0.6009 | 0.705 | 0.3516 |
| No log | 5.0 | 240 | 0.6122 | 0.7 | 0.4000 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr4_2e-05_webDiscourse_27_02_2022-19_01_41 | e494a496d83e5bd4da00240589a69a926c530f36 | 2022-02-27T18:02:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr4_2e-05_webDiscourse_27_02_2022-19_01_41 | 4 | null | transformers | 19,068 | Entry not found |
ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53 | 684c3aff18d7116cebc33f52f20b1ec9e38db0a9 | 2022-02-27T18:22:24.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53 | 4 | null | transformers | 19,069 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53
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. -->
# finetuned_sentence_itr0_3e-05_all_27_02_2022-19_16_53
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3944
- Accuracy: 0.8279
- F1: 0.8901
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3946 | 0.8012 | 0.8743 |
| No log | 2.0 | 390 | 0.3746 | 0.8329 | 0.8929 |
| 0.3644 | 3.0 | 585 | 0.4288 | 0.8268 | 0.8849 |
| 0.3644 | 4.0 | 780 | 0.5352 | 0.8232 | 0.8841 |
| 0.3644 | 5.0 | 975 | 0.5768 | 0.8268 | 0.8864 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29 | 4d5fb4df6c2e3d47f77a8fbc9c075a1153e9519a | 2022-02-27T18:25:01.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29 | 4 | null | transformers | 19,070 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29
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. -->
# finetuned_sentence_itr0_2e-05_webDiscourse_27_02_2022-19_22_29
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5819
- Accuracy: 0.7058
- F1: 0.4267
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.6110 | 0.665 | 0.0 |
| No log | 2.0 | 96 | 0.5706 | 0.685 | 0.2588 |
| No log | 3.0 | 144 | 0.5484 | 0.725 | 0.5299 |
| No log | 4.0 | 192 | 0.5585 | 0.71 | 0.4727 |
| No log | 5.0 | 240 | 0.5616 | 0.725 | 0.5133 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 | 1faaebfe4ac9422d00335662ae596643c357c5ba | 2022-02-27T18:27:34.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 | 4 | null | transformers | 19,071 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06
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. -->
# finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5777
- Accuracy: 0.6794
- F1: 0.5010
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.6059 | 0.63 | 0.4932 |
| No log | 2.0 | 96 | 0.6327 | 0.705 | 0.5630 |
| No log | 3.0 | 144 | 0.7003 | 0.695 | 0.5197 |
| No log | 4.0 | 192 | 0.9368 | 0.69 | 0.4655 |
| No log | 5.0 | 240 | 1.1935 | 0.685 | 0.4425 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41 | 681862b8b6aed5a5b6345cddd92408e19a6c8d33 | 2022-02-27T18:30:17.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41 | 4 | null | transformers | 19,072 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41
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. -->
# finetuned_sentence_itr0_3e-05_webDiscourse_27_02_2022-19_27_41
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6020
- Accuracy: 0.7032
- F1: 0.4851
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.5914 | 0.67 | 0.0294 |
| No log | 2.0 | 96 | 0.5616 | 0.695 | 0.2824 |
| No log | 3.0 | 144 | 0.5596 | 0.73 | 0.5909 |
| No log | 4.0 | 192 | 0.6273 | 0.73 | 0.5 |
| No log | 5.0 | 240 | 0.6370 | 0.71 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 | 1bed5dd40b91ae2be36fe1d5c6298f9167424d6e | 2022-02-27T18:33:05.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22 | 4 | null | transformers | 19,073 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22
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. -->
# finetuned_sentence_itr0_2e-05_essays_27_02_2022-19_30_22
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3455
- Accuracy: 0.8609
- F1: 0.9156
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 81 | 0.4468 | 0.8235 | 0.8929 |
| No log | 2.0 | 162 | 0.4497 | 0.8382 | 0.9 |
| No log | 3.0 | 243 | 0.4861 | 0.8309 | 0.8940 |
| No log | 4.0 | 324 | 0.5087 | 0.8235 | 0.8879 |
| No log | 5.0 | 405 | 0.5228 | 0.8199 | 0.8858 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 | 9cf721427043defce0015b74f7aa3d742b5e361c | 2022-02-27T18:35:51.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 | 4 | null | transformers | 19,074 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10
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. -->
# finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3358
- Accuracy: 0.8688
- F1: 0.9225
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 81 | 0.4116 | 0.8382 | 0.9027 |
| No log | 2.0 | 162 | 0.4360 | 0.8382 | 0.8952 |
| No log | 3.0 | 243 | 0.5719 | 0.8382 | 0.8995 |
| No log | 4.0 | 324 | 0.7251 | 0.8493 | 0.9021 |
| No log | 5.0 | 405 | 0.8384 | 0.8456 | 0.9019 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 | 8e559a225d5edd4e4d0547a9ae4bc47be128a5f2 | 2022-02-27T18:46:16.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 | 4 | null | transformers | 19,075 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36
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. -->
# finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0926
- Accuracy: 0.9772
- F1: 0.9883
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 104 | 0.0539 | 0.9885 | 0.9942 |
| No log | 2.0 | 208 | 0.0282 | 0.9885 | 0.9942 |
| No log | 3.0 | 312 | 0.0317 | 0.9914 | 0.9956 |
| No log | 4.0 | 416 | 0.0462 | 0.9885 | 0.9942 |
| 0.0409 | 5.0 | 520 | 0.0517 | 0.9885 | 0.9942 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22 | 33598da335cd682927cb214aa3ed216a9fb9801a | 2022-02-27T18:50:02.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22 | 4 | null | transformers | 19,076 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22
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. -->
# finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0890
- Accuracy: 0.9750
- F1: 0.9873
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 104 | 0.0485 | 0.9885 | 0.9942 |
| No log | 2.0 | 208 | 0.0558 | 0.9857 | 0.9927 |
| No log | 3.0 | 312 | 0.0501 | 0.9828 | 0.9913 |
| No log | 4.0 | 416 | 0.0593 | 0.9828 | 0.9913 |
| 0.04 | 5.0 | 520 | 0.0653 | 0.9828 | 0.9913 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 | d97e4fd19efab994e0f696296321860000bc24fc | 2022-02-27T21:30:48.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09 | 4 | null | transformers | 19,077 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09
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. -->
# finetuned_sentence_itr0_2e-05_all_27_02_2022-22_25_09
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4638
- Accuracy: 0.8247
- F1: 0.8867
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4069 | 0.7976 | 0.875 |
| No log | 2.0 | 390 | 0.4061 | 0.8134 | 0.8838 |
| 0.4074 | 3.0 | 585 | 0.4075 | 0.8134 | 0.8798 |
| 0.4074 | 4.0 | 780 | 0.4746 | 0.8256 | 0.8885 |
| 0.4074 | 5.0 | 975 | 0.4881 | 0.8220 | 0.8845 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 | 7f6b7d07609c76434f881c6c9a7c9ec97f72148c | 2022-02-27T21:36:21.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53 | 4 | null | transformers | 19,078 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53
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. -->
# finetuned_sentence_itr0_0.0002_all_27_02_2022-22_30_53
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3825
- Accuracy: 0.8144
- F1: 0.8833
## 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: 0.0002
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3975 | 0.8122 | 0.8795 |
| No log | 2.0 | 390 | 0.4376 | 0.8085 | 0.8673 |
| 0.3169 | 3.0 | 585 | 0.5736 | 0.8171 | 0.8790 |
| 0.3169 | 4.0 | 780 | 0.8178 | 0.8098 | 0.8754 |
| 0.3169 | 5.0 | 975 | 0.9244 | 0.8073 | 0.8738 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26 | 115df9b28d963f08158a8a9774c3065398a0b7af | 2022-02-27T21:41:47.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26 | 4 | null | transformers | 19,079 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_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. -->
# finetuned_sentence_itr0_3e-05_all_27_02_2022-22_36_26
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6071
- Accuracy: 0.8337
- F1: 0.8922
## 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: 3e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3920 | 0.7988 | 0.8624 |
| No log | 2.0 | 390 | 0.3873 | 0.8171 | 0.8739 |
| 0.3673 | 3.0 | 585 | 0.4354 | 0.8256 | 0.8835 |
| 0.3673 | 4.0 | 780 | 0.5358 | 0.8293 | 0.8887 |
| 0.3673 | 5.0 | 975 | 0.5616 | 0.8366 | 0.8923 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
eheitor/wav2vec2-base-xlsr53-ser_demo | 854d6159a8ff16823fc80c2e6cb2c66ca42d524b | 2022-03-01T00:11:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | eheitor | null | eheitor/wav2vec2-base-xlsr53-ser_demo | 4 | null | transformers | 19,080 | Entry not found |
ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 | 68d7230d791cf4126a12b48d4901688346410628 | 2022-03-01T02:20:45.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51 | 4 | null | transformers | 19,081 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51
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. -->
# finetuned_sentence_itr0_2e-05_all_01_03_2022-02_53_51
This model is a fine-tuned version of [siebert/sentiment-roberta-large-english](https://huggingface.co/siebert/sentiment-roberta-large-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4563
- Accuracy: 0.8440
- F1: 0.8954
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4302 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3970 | 0.8220 | 0.8875 |
| 0.3703 | 3.0 | 585 | 0.3972 | 0.8402 | 0.8934 |
| 0.3703 | 4.0 | 780 | 0.4945 | 0.8390 | 0.8935 |
| 0.3703 | 5.0 | 975 | 0.5354 | 0.8305 | 0.8898 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 | 92823c31e4adef3e493d611534e2bcf0f5a7ad04 | 2022-03-01T04:37:52.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 | 4 | null | transformers | 19,082 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03
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. -->
# finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4208
- Accuracy: 0.8283
- F1: 0.8915
- Precision: 0.8487
- Recall: 0.9389
## 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: 32
- 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 390 | 0.4443 | 0.7768 | 0.8589 | 0.8072 | 0.9176 |
| 0.4532 | 2.0 | 780 | 0.4603 | 0.8098 | 0.8791 | 0.8302 | 0.9341 |
| 0.2608 | 3.0 | 1170 | 0.5284 | 0.8061 | 0.8713 | 0.8567 | 0.8863 |
| 0.1577 | 4.0 | 1560 | 0.6398 | 0.8085 | 0.8749 | 0.8472 | 0.9044 |
| 0.1577 | 5.0 | 1950 | 0.7089 | 0.8085 | 0.8741 | 0.8516 | 0.8979 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
armageddon/bert-large-uncased-squad2-covid-qa-deepset | 39ceab4b47372274a9909c32343693915e3b7ed5 | 2022-03-01T09:03:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:covid_qa_deepset",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | armageddon | null | armageddon/bert-large-uncased-squad2-covid-qa-deepset | 4 | null | transformers | 19,083 | ---
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: bert-large-uncased-squad2-covid-qa-deepset
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-large-uncased-squad2-covid-qa-deepset
This model is a fine-tuned version of [phiyodr/bert-large-finetuned-squad2](https://huggingface.co/phiyodr/bert-large-finetuned-squad2) on the covid_qa_deepset 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
sancharidan/scibert_expfinder_SCIS | afb66ff5a7d902875a0e0a335e8664d46a3252ff | 2022-03-01T07:06:10.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | sancharidan | null | sancharidan/scibert_expfinder_SCIS | 4 | null | transformers | 19,084 | Entry not found |
Kevincp560/bart-base-finetuned-pubmed | 782b3e093e90f15d9e677b61b0a417ec43dfef6c | 2022-03-01T12:08:00.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:pub_med_summarization_dataset",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Kevincp560 | null | Kevincp560/bart-base-finetuned-pubmed | 4 | null | transformers | 19,085 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pub_med_summarization_dataset
metrics:
- rouge
model-index:
- name: bart-base-finetuned-pubmed
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: pub_med_summarization_dataset
type: pub_med_summarization_dataset
args: document
metrics:
- name: Rouge1
type: rouge
value: 9.3963
---
<!-- 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. -->
# bart-base-finetuned-pubmed
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pub_med_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0277
- Rouge1: 9.3963
- Rouge2: 4.0473
- Rougel: 8.4526
- Rougelsum: 8.9659
- Gen Len: 20.0
## 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: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.3706 | 1.0 | 4000 | 2.1245 | 9.1644 | 3.8264 | 8.2223 | 8.718 | 20.0 |
| 2.2246 | 2.0 | 8000 | 2.0811 | 9.023 | 3.7716 | 8.1453 | 8.5998 | 20.0 |
| 2.1034 | 3.0 | 12000 | 2.0469 | 9.4412 | 4.0783 | 8.4949 | 8.9977 | 20.0 |
| 2.0137 | 4.0 | 16000 | 2.0390 | 9.2261 | 3.9307 | 8.3154 | 8.7937 | 20.0 |
| 1.9288 | 5.0 | 20000 | 2.0277 | 9.3963 | 4.0473 | 8.4526 | 8.9659 | 20.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 | 4fdaaf2517101ac528fd2dd095e11d53acdc281d | 2022-03-01T12:20:35.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55 | 4 | null | transformers | 19,086 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55
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. -->
# finetuned_sentence_itr0_2e-05_webDiscourse_01_03_2022-13_17_55
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7224
- Accuracy: 0.6979
- F1: 0.4736
- Precision: 0.5074
- Recall: 0.4440
## 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: 32
- 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 95 | 0.6009 | 0.65 | 0.2222 | 0.625 | 0.1351 |
| No log | 2.0 | 190 | 0.6140 | 0.675 | 0.3689 | 0.6552 | 0.2568 |
| No log | 3.0 | 285 | 0.6580 | 0.67 | 0.4590 | 0.5833 | 0.3784 |
| No log | 4.0 | 380 | 0.7560 | 0.665 | 0.4806 | 0.5636 | 0.4189 |
| No log | 5.0 | 475 | 0.8226 | 0.665 | 0.464 | 0.5686 | 0.3919 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_essays_01_03_2022-13_20_40 | 43779b35e181e3a8b6eeeb350c4daad5ed46d896 | 2022-03-01T12:21:10.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | ali2066 | null | ali2066/finetuned_sentence_itr0_2e-05_essays_01_03_2022-13_20_40 | 4 | null | transformers | 19,087 | Entry not found |
ali2066/correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 | 3deaafce5526cd4493aff950e27cebcf0161bdc2 | 2022-03-01T14:43:43.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 | 4 | null | transformers | 19,088 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32
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. -->
# correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1206
- Precision: 0.0637
- Recall: 0.0080
- F1: 0.0141
- Accuracy: 0.9707
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 15 | 0.1222 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
| No log | 2.0 | 30 | 0.1159 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
| No log | 3.0 | 45 | 0.1082 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
| No log | 4.0 | 60 | 0.1042 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
| No log | 5.0 | 75 | 0.1029 | 0.12 | 0.0139 | 0.0249 | 0.9736 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
Ebtihal/AraBertMo_base_V9 | 544c64595b66e043797cce362e816d5870ae2416 | 2022-03-25T07:25:05.000Z | [
"pytorch",
"bert",
"fill-mask",
"ar",
"dataset:OSCAR",
"transformers",
"Fill-Mask",
"autotrain_compatible"
] | fill-mask | false | Ebtihal | null | Ebtihal/AraBertMo_base_V9 | 4 | null | transformers | 19,089 | Arabic Model AraBertMo_base_V9
---
language: ar
tags: Fill-Mask
datasets: OSCAR
widget:
- text: " السلام عليكم ورحمة[MASK] وبركاتة"
- text: " اهلا وسهلا بكم في [MASK] من سيربح المليون"
- text: " مرحبا بك عزيزي الزائر [MASK] موقعنا "
---
# Arabic BERT Model
**AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert).
AraBERTMo_base uses the same BERT-Base config.
AraBERTMo_base now comes in 10 new variants
All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name.
Checkpoints are available in PyTorch formats.
## Pretraining Corpus
`AraBertMo_base_V9' model was pre-trained on ~3 million words:
- [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar".
## Training results
this model achieves the following results:
| Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss|
|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|
| Fill-Mask| 30024| 9 | 64 | 4230 | 7h 57m 42s | 7.3264 |
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V9")
model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V9")
```
## This model was built for master's degree research in an organization:
- [University of kufa](https://uokufa.edu.iq/).
- [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/).
- **Department of Computer Science**
|
batterydata/bert-base-uncased-squad-v1 | ced392697ea10dda819fbffacd1a66eb741ccfb5 | 2022-03-03T19:53:31.000Z | [
"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad",
"dataset:batterydata/battery-device-data-qa",
"transformers",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | batterydata | null | batterydata/bert-base-uncased-squad-v1 | 4 | null | transformers | 19,090 | ---
language: en
tags: question answering
license: apache-2.0
datasets:
- squad
- batterydata/battery-device-data-qa
metrics: squad
---
# BERT-base-cased for QA
**Language model:** bert-base-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
## Hyperparameters
```
batch_size = 32
n_epochs = 3
base_LM_model = "bert-base-uncased"
max_seq_len = 386
learning_rate = 3e-5
doc_stride=128
max_query_length=64
```
## Performance
Evaluated on the SQuAD v1.0 dev set.
```
"exact": 80.93,
"f1": 88.20,
```
Evaluated on the battery device dataset.
```
"precision": 62.19,
"recall": 75.00,
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "batterydata/bert-base-uncased-squad-v1"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Shu Huang: `sh2009 [at] cam.ac.uk`
Jacqueline Cole: `jmc61 [at] cam.ac.uk`
## Citation
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement |
aaraki/marian-finetuned-kde4-en-to-fr | ca878625037784a80830d46c1faa8b5e424c68e4 | 2022-03-02T01:54:57.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | aaraki | null | aaraki/marian-finetuned-kde4-en-to-fr | 4 | null | transformers | 19,091 | ---
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
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.94560734092563
---
<!-- 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: 0.8559
- Bleu: 52.9456
## 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Akash7897/distilbert-base-uncased-finetuned-cola | e25f95dffc22db6cbe5102f5f59aeeba04e901b0 | 2022-03-02T08:29:47.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | Akash7897 | null | Akash7897/distilbert-base-uncased-finetuned-cola | 4 | null | transformers | 19,092 | ---
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
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.522211073949747
---
<!-- 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: 1.0789
- Matthews Correlation: 0.5222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.1472 | 1.0 | 535 | 0.8407 | 0.4915 |
| 0.1365 | 2.0 | 1070 | 0.9236 | 0.4990 |
| 0.1194 | 3.0 | 1605 | 0.8753 | 0.4953 |
| 0.1313 | 4.0 | 2140 | 0.9684 | 0.5013 |
| 0.0895 | 5.0 | 2675 | 1.0789 | 0.5222 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
luffycodes/reg-roberta-base-mrpc | 18b1244164f24ba71b70794013788c2515378449 | 2022-04-06T03:32:27.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | luffycodes | null | luffycodes/reg-roberta-base-mrpc | 4 | null | transformers | 19,093 | Entry not found |
vuiseng9/bert-squad-nncf-qat-mvmt-hybrid-filled-lt | 48eabb46113dfc7e8ed718bf365af798d5bfb650 | 2022-03-03T05:00:27.000Z | [
"pytorch",
"onnx",
"bert",
"transformers"
] | null | false | vuiseng9 | null | vuiseng9/bert-squad-nncf-qat-mvmt-hybrid-filled-lt | 4 | null | transformers | 19,094 | |
carolEileen/distilbert-base-uncased-finetuned-imdb | 20abf2a876143f2452db0b25a53a7adac60636b1 | 2022-03-03T09:07:29.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | fill-mask | false | carolEileen | null | carolEileen/distilbert-base-uncased-finetuned-imdb | 4 | null | transformers | 19,095 | ---
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: 2.4725
## 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 | 2.4897 |
| 2.5756 | 2.0 | 314 | 2.4230 |
| 2.5395 | 3.0 | 471 | 2.4358 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Aktsvigun/bart-base_tapt_email1e4 | cd664740e8f593d54705c116d64128709fea5e8f | 2022-03-03T11:51:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Aktsvigun | null | Aktsvigun/bart-base_tapt_email1e4 | 4 | null | transformers | 19,096 | Entry not found |
Anthos23/FS-finbert-fine-tuned | b2153d532c679117d9074803bafb9fca4fec3810 | 2022-03-04T13:00:29.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Anthos23 | null | Anthos23/FS-finbert-fine-tuned | 4 | null | transformers | 19,097 | Entry not found |
mmaguero/beto-gn-base-cased | fd977a4c505169e11f3ef07791a815e695c6d265 | 2022-03-06T08:06:11.000Z | [
"pytorch",
"bert",
"fill-mask",
"gn",
"es",
"dataset:wikipedia",
"dataset:wiktionary",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | mmaguero | null | mmaguero/beto-gn-base-cased | 4 | null | transformers | 19,098 | ---
language:
- gn
- es
license: mit
datasets:
- wikipedia
- wiktionary
widget:
- text: "Paraguay ha'e peteĩ táva oĩva [MASK] retãme "
---
# BETO+gn-base-cased
[BETO-base-cased (pre-trained Spanish BERT model)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) fine-tuned for **Guarani** language modeling (Spanish + Guarani). Trained on Wikipedia + Wiktionary (~800K tokens).
|
mmaguero/multilingual-bert-gn-base-cased | 10ac6d4faec3774cacff34af52e917fd030828fc | 2022-03-06T08:08:41.000Z | [
"pytorch",
"bert",
"fill-mask",
"gn",
"multilingual",
"dataset:wikipedia",
"dataset:wiktionary",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | mmaguero | null | mmaguero/multilingual-bert-gn-base-cased | 4 | null | transformers | 19,099 | ---
language:
- gn
- multilingual
license: mit
datasets:
- wikipedia
- wiktionary
widget:
- text: "Paraguay ha'e peteĩ táva oĩva [MASK] retãme "
---
# mBERT+gn-base-cased (multilingual-BERT+gn-base-cased)
[BERT multilingual base model (cased, pre-trained BERT model)](https://huggingface.co/bert-base-multilingual-cased) fine-tuned for **Guarani** language modeling (104 languages + gn). Trained on Wikipedia + Wiktionary (~800K tokens).
|
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