modelId stringlengths 6 107 | label list | readme stringlengths 0 56.2k | readme_len int64 0 56.2k |
|---|---|---|---|
connectivity/feather_berts_98 | [
"LABEL_0",
"LABEL_1",
"LABEL_2"
] | Entry not found | 15 |
connectivity/bert_ft_qqp-18 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-19 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-20 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-21 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-23 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-25 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-26 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-27 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-29 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-32 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-33 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-34 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-37 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-38 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-39 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-41 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-42 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-46 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-47 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-49 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-50 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-51 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-52 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-53 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-54 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-55 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-56 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-57 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-58 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-59 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-60 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-61 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-62 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-63 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-64 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-65 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-67 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-68 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-69 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-71 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-72 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-73 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-1 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-74 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-2 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-3 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-4 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-5 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-6 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-7 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-76 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-8 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-9 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-10 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-11 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-12 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-13 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-14 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-15 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-77 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-16 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-17 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-18 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-19 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-20 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-21 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-22 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-23 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-78 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-24 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-25 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-26 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-27 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-28 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-29 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-30 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-31 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-79 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-32 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-33 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-34 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-35 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-36 | null | Entry not found | 15 |
connectivity/cola_6ep_ft-37 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-82 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-84 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-87 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-88 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-89 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-93 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-94 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-95 | null | Entry not found | 15 |
connectivity/bert_ft_qqp-99 | null | Entry not found | 15 |
joaobarroca/distilbert-base-uncased-finetuned-massive-intent-detection-english | [
"alarm_query",
"alarm_remove",
"alarm_set",
"audio_volume_down",
"audio_volume_mute",
"audio_volume_other",
"audio_volume_up",
"calendar_query",
"calendar_remove",
"calendar_set",
"cooking_query",
"cooking_recipe",
"datetime_convert",
"datetime_query",
"email_addcontact",
"email_query"... | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- massive
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-massive-intent-detection-english
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: massive
type: massive
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.886684599865501
---
<!-- 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-massive-intent-detection-english
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4873
- Accuracy: 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5849 | 1.0 | 360 | 1.3826 | 0.7359 |
| 1.0662 | 2.0 | 720 | 0.7454 | 0.8357 |
| 0.5947 | 3.0 | 1080 | 0.5668 | 0.8642 |
| 0.3824 | 4.0 | 1440 | 0.5007 | 0.8770 |
| 0.2649 | 5.0 | 1800 | 0.4829 | 0.8824 |
| 0.1877 | 6.0 | 2160 | 0.4843 | 0.8824 |
| 0.1377 | 7.0 | 2520 | 0.4858 | 0.8834 |
| 0.1067 | 8.0 | 2880 | 0.4924 | 0.8864 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 2,122 |
joebobby/finetuning-sentiment-model-5000-samples | [
"LABEL_0",
"LABEL_1",
"LABEL_2",
"LABEL_3",
"LABEL_4"
] | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-5000-samples
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. -->
# finetuning-sentiment-model-5000-samples
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0701
- Accuracy: 0.758
- F1: 0.7580
## 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 313 | 1.0216 | 0.744 | 0.744 |
| 0.2263 | 2.0 | 626 | 1.0701 | 0.758 | 0.7580 |
| 0.2263 | 3.0 | 939 | 1.3097 | 0.723 | 0.723 |
| 0.1273 | 4.0 | 1252 | 1.4377 | 0.743 | 0.743 |
| 0.051 | 5.0 | 1565 | 1.4884 | 0.739 | 0.739 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,700 |
aliosm/sha3bor-footer-101-arabertv02-base | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_100",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28"... | ---
language: ar
license: mit
widget:
- text: "إن العيون التي في طرفها حور"
- text: "إذا ما فعلت الخير ضوعف شرهم"
- text: "واحر قلباه ممن قلبه شبم"
---
| 152 |
Abdelrahman-Rezk/bert-base-arabic-camelbert-mix-poetry-finetuned-qawaf2 | [
"LABEL_0",
"LABEL_1",
"LABEL_10",
"LABEL_11",
"LABEL_12",
"LABEL_13",
"LABEL_14",
"LABEL_15",
"LABEL_16",
"LABEL_17",
"LABEL_18",
"LABEL_19",
"LABEL_2",
"LABEL_20",
"LABEL_21",
"LABEL_22",
"LABEL_23",
"LABEL_24",
"LABEL_25",
"LABEL_26",
"LABEL_27",
"LABEL_28",
"LABEL_29",... | Entry not found | 15 |
PDRES/roberta-base-bne-finetuned-amazon_reviews_multi | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,130 |
GioReg/dbmdzBERTnews | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: dbmdzBERTnews
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. -->
# dbmdzBERTnews
This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0960
- Accuracy: 0.9733
- F1: 0.9730
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
| 1,171 |
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