modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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susank/distilbert-base-uncased-finetuned-emotion
|
susank
| 2022-08-12T05:45:28Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-12T05:33:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240247841894665
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2281
- Accuracy: 0.924
- F1: 0.9240
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8687 | 1.0 | 250 | 0.3390 | 0.9015 | 0.8984 |
| 0.2645 | 2.0 | 500 | 0.2281 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.0+cu113
- Datasets 2.0.0
- Tokenizers 0.10.3
|
User-leanring-HI/distilbert-base-uncased-finetuned-emotion
|
User-leanring-HI
| 2022-08-12T05:42:34Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-12T05:27:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9279536670242958
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2264
- Accuracy: 0.928
- F1: 0.9280
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8737 | 1.0 | 250 | 0.3305 | 0.9035 | 0.8995 |
| 0.259 | 2.0 | 500 | 0.2264 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Lvxue/distilled-mt5-small-0.005-1
|
Lvxue
| 2022-08-12T03:22:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-12T02:08:07Z |
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-0.005-1
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 7.6523
---
<!-- 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. -->
# distilled-mt5-small-0.005-1
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8123
- Bleu: 7.6523
- Gen Len: 44.3867
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Lvxue/distilled-mt5-small-1-0.25
|
Lvxue
| 2022-08-12T03:22:48Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-12T02:06:48Z |
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-1-0.25
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 4.0871
---
<!-- 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. -->
# distilled-mt5-small-1-0.25
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8599
- Bleu: 4.0871
- Gen Len: 35.3267
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Lvxue/distilled-mt5-small-1-0.5
|
Lvxue
| 2022-08-12T03:22:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-12T02:06:37Z |
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-1-0.5
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 5.3917
---
<!-- 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. -->
# distilled-mt5-small-1-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8410
- Bleu: 5.3917
- Gen Len: 40.6103
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sun1638650145/PyTorch-PPO-LunarLander-v2
|
sun1638650145
| 2022-08-12T02:48:38Z | 0 | 0 | null |
[
"tensorboard",
"model-index",
"region:us"
] | null | 2022-08-12T02:48:05Z |
---
tag:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -121.77 +/- 30.58
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# 使用PPO智能体来玩 LunarLander-v2
这是一个使用PPO训练有素的模型玩 LunarLander-v2.
要学习编写你自己的PPO智能体并训练它,
请查阅深度强化学习课程第8单元: https://github.com/huggingface/deep-rl-class/tree/main/unit8
# 超参数
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'sun1638650145/PyTorch-PPO-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Lvxue/distilled-mt5-small-0.02-0.5
|
Lvxue
| 2022-08-12T01:24:15Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-12T00:10:42Z |
---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-0.02-0.5
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 7.448
---
<!-- 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. -->
# distilled-mt5-small-0.02-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8160
- Bleu: 7.448
- Gen Len: 44.2241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
DOOGLAK/Article_500v8_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-12T00:16:26Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T00:11:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v8_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v8_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v8_wikigold_split
type: article500v8_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6780405405405405
- name: Recall
type: recall
value: 0.7117021276595744
- name: F1
type: f1
value: 0.6944636678200693
- name: Accuracy
type: accuracy
value: 0.9363021063950914
---
<!-- 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. -->
# Article_500v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v8_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1980
- Precision: 0.6780
- Recall: 0.7117
- F1: 0.6945
- Accuracy: 0.9363
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 64 | 0.2758 | 0.5405 | 0.5298 | 0.5351 | 0.9135 |
| No log | 2.0 | 128 | 0.2129 | 0.6350 | 0.6695 | 0.6518 | 0.9296 |
| No log | 3.0 | 192 | 0.1980 | 0.6780 | 0.7117 | 0.6945 | 0.9363 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_500v6_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-12T00:05:01Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-12T00:00:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v6_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v6_wikigold_split
type: article500v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6462295081967213
- name: Recall
type: recall
value: 0.6930379746835443
- name: F1
type: f1
value: 0.6688157448252461
- name: Accuracy
type: accuracy
value: 0.9318540995006005
---
<!-- 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. -->
# Article_500v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2025
- Precision: 0.6462
- Recall: 0.6930
- F1: 0.6688
- Accuracy: 0.9319
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 63 | 0.2794 | 0.3775 | 0.4525 | 0.4116 | 0.8945 |
| No log | 2.0 | 126 | 0.2119 | 0.6143 | 0.6670 | 0.6396 | 0.9266 |
| No log | 3.0 | 189 | 0.2025 | 0.6462 | 0.6930 | 0.6688 | 0.9319 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_500v4_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:53:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:48:17Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v4_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v4_wikigold_split
type: article500v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6463647959183674
- name: Recall
type: recall
value: 0.6729747675962815
- name: F1
type: f1
value: 0.6594014313597917
- name: Accuracy
type: accuracy
value: 0.9314611096204871
---
<!-- 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. -->
# Article_500v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2062
- Precision: 0.6464
- Recall: 0.6730
- F1: 0.6594
- Accuracy: 0.9315
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 58 | 0.3048 | 0.3090 | 0.2978 | 0.3033 | 0.8852 |
| No log | 2.0 | 116 | 0.2127 | 0.6096 | 0.6567 | 0.6323 | 0.9271 |
| No log | 3.0 | 174 | 0.2062 | 0.6464 | 0.6730 | 0.6594 | 0.9315 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_500v0_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:30:34Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:25:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v0_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v0_wikigold_split
type: article500v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6387981711299804
- name: Recall
type: recall
value: 0.7249814677538917
- name: F1
type: f1
value: 0.6791666666666667
- name: Accuracy
type: accuracy
value: 0.9364674441205053
---
<!-- 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. -->
# Article_500v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1853
- Precision: 0.6388
- Recall: 0.7250
- F1: 0.6792
- Accuracy: 0.9365
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 59 | 0.2886 | 0.4480 | 0.6179 | 0.5194 | 0.9012 |
| No log | 2.0 | 118 | 0.1912 | 0.6132 | 0.6946 | 0.6514 | 0.9327 |
| No log | 3.0 | 177 | 0.1853 | 0.6388 | 0.7250 | 0.6792 | 0.9365 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_250v7_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:23:06Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:17:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v7_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v7_wikigold_split
type: article250v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.4384191176470588
- name: Recall
type: recall
value: 0.4016278417064272
- name: F1
type: f1
value: 0.4192178116302914
- name: Accuracy
type: accuracy
value: 0.8915853138253821
---
<!-- 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. -->
# Article_250v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3252
- Precision: 0.4384
- Recall: 0.4016
- F1: 0.4192
- Accuracy: 0.8916
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 28 | 0.5176 | 0.0927 | 0.0039 | 0.0075 | 0.7864 |
| No log | 2.0 | 56 | 0.3592 | 0.3931 | 0.3595 | 0.3755 | 0.8807 |
| No log | 3.0 | 84 | 0.3252 | 0.4384 | 0.4016 | 0.4192 | 0.8916 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
BigSalmon/InformalToFormalLincoln61Paraphrase
|
BigSalmon
| 2022-08-11T23:21:29Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-05T22:11:12Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln61Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln61Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
```
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:
```
```
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 (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
```
|
BigSalmon/InformalToFormalLincoln63Paraphrase
|
BigSalmon
| 2022-08-11T23:20:43Z | 161 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-08T00:20:41Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs:
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
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:
```
```
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 (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
```
|
DOOGLAK/Article_250v6_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:17:21Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:12:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v6_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v6_wikigold_split
type: article250v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.3970455230630087
- name: Recall
type: recall
value: 0.3699438202247191
- name: F1
type: f1
value: 0.3830158499345645
- name: Accuracy
type: accuracy
value: 0.8862729247713839
---
<!-- 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. -->
# Article_250v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3052
- Precision: 0.3970
- Recall: 0.3699
- F1: 0.3830
- Accuracy: 0.8863
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.5222 | 0.1785 | 0.0817 | 0.1121 | 0.8202 |
| No log | 2.0 | 58 | 0.3356 | 0.3575 | 0.3357 | 0.3462 | 0.8780 |
| No log | 3.0 | 87 | 0.3052 | 0.3970 | 0.3699 | 0.3830 | 0.8863 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_250v5_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:11:37Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:06:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v5_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v5_wikigold_split
type: article250v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.3979099678456592
- name: Recall
type: recall
value: 0.4221148379761228
- name: F1
type: f1
value: 0.4096551724137931
- name: Accuracy
type: accuracy
value: 0.8778839730743538
---
<!-- 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. -->
# Article_250v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3250
- Precision: 0.3979
- Recall: 0.4221
- F1: 0.4097
- Accuracy: 0.8779
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 31 | 0.5229 | 0.1336 | 0.0344 | 0.0547 | 0.8008 |
| No log | 2.0 | 62 | 0.3701 | 0.3628 | 0.3357 | 0.3487 | 0.8596 |
| No log | 3.0 | 93 | 0.3250 | 0.3979 | 0.4221 | 0.4097 | 0.8779 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_250v4_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T23:06:11Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T23:01:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v4_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v4_wikigold_split
type: article250v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.40273125483122907
- name: Recall
type: recall
value: 0.433684794672586
- name: F1
type: f1
value: 0.4176352705410822
- name: Accuracy
type: accuracy
value: 0.8774915169033556
---
<!-- 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. -->
# Article_250v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3243
- Precision: 0.4027
- Recall: 0.4337
- F1: 0.4176
- Accuracy: 0.8775
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 28 | 0.5309 | 0.0816 | 0.0144 | 0.0245 | 0.7931 |
| No log | 2.0 | 56 | 0.3620 | 0.3795 | 0.3674 | 0.3733 | 0.8623 |
| No log | 3.0 | 84 | 0.3243 | 0.4027 | 0.4337 | 0.4176 | 0.8775 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_250v2_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:55:07Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T22:49:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v2_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v2_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v2_wikigold_split
type: article250v2_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.4664981036662453
- name: Recall
type: recall
value: 0.5280480824270177
- name: F1
type: f1
value: 0.49536850583971004
- name: Accuracy
type: accuracy
value: 0.9042507513954486
---
<!-- 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. -->
# Article_250v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v2_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2900
- Precision: 0.4665
- Recall: 0.5280
- F1: 0.4954
- Accuracy: 0.9043
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.4904 | 0.1788 | 0.0487 | 0.0765 | 0.8034 |
| No log | 2.0 | 58 | 0.3224 | 0.4091 | 0.4825 | 0.4428 | 0.8951 |
| No log | 3.0 | 87 | 0.2900 | 0.4665 | 0.5280 | 0.4954 | 0.9043 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_250v0_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:43:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T22:38:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article250v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_250v0_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article250v0_wikigold_split
type: article250v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.316
- name: Recall
type: recall
value: 0.2984349703184026
- name: F1
type: f1
value: 0.3069664168748265
- name: Accuracy
type: accuracy
value: 0.8677259136623094
---
<!-- 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. -->
# Article_250v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3397
- Precision: 0.316
- Recall: 0.2984
- F1: 0.3070
- Accuracy: 0.8677
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 28 | 0.5344 | 0.1336 | 0.0183 | 0.0323 | 0.7903 |
| No log | 2.0 | 56 | 0.3736 | 0.2753 | 0.2221 | 0.2458 | 0.8528 |
| No log | 3.0 | 84 | 0.3397 | 0.316 | 0.2984 | 0.3070 | 0.8677 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v9_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:38:08Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T22:33:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v9_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v9_wikigold_split
type: article100v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.14901960784313725
- name: Recall
type: recall
value: 0.03918535705078628
- name: F1
type: f1
value: 0.06205348030210247
- name: Accuracy
type: accuracy
value: 0.8030657373746729
---
<!-- 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. -->
# Article_100v9_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5642
- Precision: 0.1490
- Recall: 0.0392
- F1: 0.0621
- Accuracy: 0.8031
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.7073 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 2.0 | 26 | 0.6007 | 0.0734 | 0.0062 | 0.0114 | 0.7875 |
| No log | 3.0 | 39 | 0.5642 | 0.1490 | 0.0392 | 0.0621 | 0.8031 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v6_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:21:24Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T22:16:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v6_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v6_wikigold_split
type: article100v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7806604861399382
---
<!-- 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. -->
# Article_100v6_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5955
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7807
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 12 | 0.7335 | 0.0 | 0.0 | 0.0 | 0.7806 |
| No log | 2.0 | 24 | 0.6302 | 0.0 | 0.0 | 0.0 | 0.7806 |
| No log | 3.0 | 36 | 0.5955 | 0.0 | 0.0 | 0.0 | 0.7807 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v5_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:15:57Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T22:10:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v5_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v5_wikigold_split
type: article100v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.024096385542168676
- name: Recall
type: recall
value: 0.0005104645227156713
- name: F1
type: f1
value: 0.000999750062484379
- name: Accuracy
type: accuracy
value: 0.7821558918567079
---
<!-- 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. -->
# Article_100v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5958
- Precision: 0.0241
- Recall: 0.0005
- F1: 0.0010
- Accuracy: 0.7822
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.7298 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 2.0 | 26 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 3.0 | 39 | 0.5958 | 0.0241 | 0.0005 | 0.0010 | 0.7822 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v3_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T22:04:16Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:59:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v3_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v3_wikigold_split
type: article100v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7772145452862069
---
<!-- 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. -->
# Article_100v3_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6272
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7772
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 11 | 0.7637 | 0.0 | 0.0 | 0.0 | 0.7772 |
| No log | 2.0 | 22 | 0.6651 | 0.0 | 0.0 | 0.0 | 0.7772 |
| No log | 3.0 | 33 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.7772 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v1_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:53:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:48:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v1_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v1_wikigold_split
type: article100v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.06
- name: Recall
type: recall
value: 0.0015592515592515593
- name: F1
type: f1
value: 0.00303951367781155
- name: Accuracy
type: accuracy
value: 0.7832046377355834
---
<!-- 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. -->
# Article_100v1_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5783
- Precision: 0.06
- Recall: 0.0016
- F1: 0.0030
- Accuracy: 0.7832
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.7124 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 2.0 | 26 | 0.6131 | 0.0 | 0.0 | 0.0 | 0.7819 |
| No log | 3.0 | 39 | 0.5783 | 0.06 | 0.0016 | 0.0030 | 0.7832 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_100v0_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:48:03Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:43:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article100v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_100v0_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article100v0_wikigold_split
type: article100v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.25
- name: Recall
type: recall
value: 0.0002523977788995457
- name: F1
type: f1
value: 0.0005042864346949066
- name: Accuracy
type: accuracy
value: 0.7772140114046316
---
<!-- 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. -->
# Article_100v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6037
- Precision: 0.25
- Recall: 0.0003
- F1: 0.0005
- Accuracy: 0.7772
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 12 | 0.7472 | 0.0 | 0.0 | 0.0 | 0.7772 |
| No log | 2.0 | 24 | 0.6443 | 0.0 | 0.0 | 0.0 | 0.7772 |
| No log | 3.0 | 36 | 0.6037 | 0.25 | 0.0003 | 0.0005 | 0.7772 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v8_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:37:12Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:32:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v8_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v8_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v8_wikigold_split
type: article50v8_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7786409940669428
---
<!-- 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. -->
# Article_50v8_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v8_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7555
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7786
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 0.9789 | 0.1 | 0.0047 | 0.0089 | 0.7776 |
| No log | 2.0 | 12 | 0.7892 | 0.0 | 0.0 | 0.0 | 0.7786 |
| No log | 3.0 | 18 | 0.7555 | 0.0 | 0.0 | 0.0 | 0.7786 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v7_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:31:46Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:26:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v7_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v7_wikigold_split
type: article50v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.3333333333333333
- name: Recall
type: recall
value: 0.00024324981756263683
- name: F1
type: f1
value: 0.0004861448711716091
- name: Accuracy
type: accuracy
value: 0.7783221476510067
---
<!-- 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. -->
# Article_50v7_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7894
- Precision: 0.3333
- Recall: 0.0002
- F1: 0.0005
- Accuracy: 0.7783
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 1.0271 | 0.1183 | 0.0102 | 0.0188 | 0.7768 |
| No log | 2.0 | 12 | 0.8250 | 0.4 | 0.0005 | 0.0010 | 0.7783 |
| No log | 3.0 | 18 | 0.7894 | 0.3333 | 0.0002 | 0.0005 | 0.7783 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v5_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:20:35Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:15:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v5_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v5_wikigold_split
type: article50v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7765277995652466
---
<!-- 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. -->
# Article_50v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7582
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7765
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 0.9705 | 0.1634 | 0.0061 | 0.0117 | 0.7757 |
| No log | 2.0 | 12 | 0.7855 | 0.0 | 0.0 | 0.0 | 0.7765 |
| No log | 3.0 | 18 | 0.7582 | 0.0 | 0.0 | 0.0 | 0.7765 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v4_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:15:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T21:10:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v4_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v4_wikigold_split
type: article50v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7775440794773114
---
<!-- 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. -->
# Article_50v4_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7543
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7775
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 0.9689 | 0.0949 | 0.0036 | 0.0070 | 0.7766 |
| No log | 2.0 | 12 | 0.7856 | 0.0 | 0.0 | 0.0 | 0.7775 |
| No log | 3.0 | 18 | 0.7543 | 0.0 | 0.0 | 0.0 | 0.7775 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v2_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T21:04:23Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:59:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v2_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v2_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v2_wikigold_split
type: article50v2_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7776133458899502
---
<!-- 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. -->
# Article_50v2_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v2_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7694
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7776
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 6 | 0.9910 | 0.1161 | 0.0044 | 0.0085 | 0.7766 |
| No log | 2.0 | 12 | 0.8031 | 0.0 | 0.0 | 0.0 | 0.7776 |
| No log | 3.0 | 18 | 0.7694 | 0.0 | 0.0 | 0.0 | 0.7776 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_50v0_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
| 2022-08-11T20:53:21Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article50v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:48:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article50v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_50v0_NER_Model_3Epochs_UNAUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article50v0_wikigold_split
type: article50v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7804070788490116
---
<!-- 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. -->
# Article_50v0_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7728
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7804
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 7 | 0.9587 | 0.0357 | 0.0022 | 0.0041 | 0.7789 |
| No log | 2.0 | 14 | 0.8053 | 0.0 | 0.0 | 0.0 | 0.7803 |
| No log | 3.0 | 21 | 0.7728 | 0.0 | 0.0 | 0.0 | 0.7804 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:42:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:37:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v9_wikigold_split
type: tagged_uni500v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7116605412629469
- name: Recall
type: recall
value: 0.717654986522911
- name: F1
type: f1
value: 0.7146451937594362
- name: Accuracy
type: accuracy
value: 0.9351089287379184
---
<!-- 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. -->
# Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2209
- Precision: 0.7117
- Recall: 0.7177
- F1: 0.7146
- Accuracy: 0.9351
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 165 | 0.2693 | 0.5953 | 0.5249 | 0.5579 | 0.9126 |
| No log | 2.0 | 330 | 0.2203 | 0.6916 | 0.6853 | 0.6884 | 0.9313 |
| No log | 3.0 | 495 | 0.2209 | 0.7117 | 0.7177 | 0.7146 | 0.9351 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:37:02Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:31:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v8_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v8_wikigold_split
type: tagged_uni500v8_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.704553603442094
- name: Recall
type: recall
value: 0.6968085106382979
- name: F1
type: f1
value: 0.7006596541272954
- name: Accuracy
type: accuracy
value: 0.9316528559681194
---
<!-- 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. -->
# Tagged_Uni_500v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v8_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2501
- Precision: 0.7046
- Recall: 0.6968
- F1: 0.7007
- Accuracy: 0.9317
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 169 | 0.2800 | 0.5648 | 0.5035 | 0.5324 | 0.9043 |
| No log | 2.0 | 338 | 0.2383 | 0.6783 | 0.6738 | 0.6760 | 0.9286 |
| 0.1144 | 3.0 | 507 | 0.2501 | 0.7046 | 0.6968 | 0.7007 | 0.9317 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:31:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:25:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v7_wikigold_split
type: tagged_uni500v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7087020648967551
- name: Recall
type: recall
value: 0.7068775285031261
- name: F1
type: f1
value: 0.7077886208801325
- name: Accuracy
type: accuracy
value: 0.9310942373735782
---
<!-- 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. -->
# Tagged_Uni_500v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2465
- Precision: 0.7087
- Recall: 0.7069
- F1: 0.7078
- Accuracy: 0.9311
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 154 | 0.3027 | 0.5778 | 0.4917 | 0.5313 | 0.9053 |
| No log | 2.0 | 308 | 0.2317 | 0.6818 | 0.6973 | 0.6895 | 0.9293 |
| No log | 3.0 | 462 | 0.2465 | 0.7087 | 0.7069 | 0.7078 | 0.9311 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:25:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:19:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v6_wikigold_split
type: tagged_uni500v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.699155524278677
- name: Recall
type: recall
value: 0.6986638537271449
- name: F1
type: f1
value: 0.6989096025325361
- name: Accuracy
type: accuracy
value: 0.9317908843795436
---
<!-- 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. -->
# Tagged_Uni_500v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2386
- Precision: 0.6992
- Recall: 0.6987
- F1: 0.6989
- Accuracy: 0.9318
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 182 | 0.2452 | 0.5956 | 0.5432 | 0.5682 | 0.9189 |
| No log | 2.0 | 364 | 0.2571 | 0.6832 | 0.6354 | 0.6584 | 0.9204 |
| 0.1093 | 3.0 | 546 | 0.2386 | 0.6992 | 0.6987 | 0.6989 | 0.9318 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:18:51Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:13:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v5_wikigold_split
type: tagged_uni500v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7004950495049505
- name: Recall
type: recall
value: 0.7075
- name: F1
type: f1
value: 0.7039800995024875
- name: Accuracy
type: accuracy
value: 0.9367615143477213
---
<!-- 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. -->
# Tagged_Uni_500v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2258
- Precision: 0.7005
- Recall: 0.7075
- F1: 0.7040
- Accuracy: 0.9368
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 164 | 0.2399 | 0.5969 | 0.5543 | 0.5748 | 0.9208 |
| No log | 2.0 | 328 | 0.2145 | 0.6931 | 0.6968 | 0.6949 | 0.9362 |
| No log | 3.0 | 492 | 0.2258 | 0.7005 | 0.7075 | 0.7040 | 0.9368 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T20:07:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T20:02:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v3_wikigold_split
type: tagged_uni500v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7143812709030101
- name: Recall
type: recall
value: 0.7115256495669554
- name: F1
type: f1
value: 0.7129506008010682
- name: Accuracy
type: accuracy
value: 0.9340035371870055
---
<!-- 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. -->
# Tagged_Uni_500v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2350
- Precision: 0.7144
- Recall: 0.7115
- F1: 0.7130
- Accuracy: 0.9340
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 172 | 0.2361 | 0.6056 | 0.5596 | 0.5817 | 0.9194 |
| No log | 2.0 | 344 | 0.2236 | 0.6872 | 0.6922 | 0.6897 | 0.9315 |
| 0.1011 | 3.0 | 516 | 0.2350 | 0.7144 | 0.7115 | 0.7130 | 0.9340 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T19:50:09Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T19:45:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni500v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni500v0_wikigold_split
type: tagged_uni500v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6686186703410265
- name: Recall
type: recall
value: 0.7194217939214232
- name: F1
type: f1
value: 0.6930905195500803
- name: Accuracy
type: accuracy
value: 0.9331875607385811
---
<!-- 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. -->
# Tagged_Uni_500v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2431
- Precision: 0.6686
- Recall: 0.7194
- F1: 0.6931
- Accuracy: 0.9332
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 170 | 0.2383 | 0.5988 | 0.5808 | 0.5897 | 0.9193 |
| No log | 2.0 | 340 | 0.2189 | 0.6711 | 0.7072 | 0.6887 | 0.9337 |
| 0.1129 | 3.0 | 510 | 0.2431 | 0.6686 | 0.7194 | 0.6931 | 0.9332 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T19:44:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T19:40:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni250v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni250v9_wikigold_split
type: tagged_uni250v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.587685364281109
- name: Recall
type: recall
value: 0.526270207852194
- name: F1
type: f1
value: 0.5552848004873592
- name: Accuracy
type: accuracy
value: 0.9092797783933518
---
<!-- 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. -->
# Tagged_Uni_250v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2786
- Precision: 0.5877
- Recall: 0.5263
- F1: 0.5553
- Accuracy: 0.9093
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 88 | 0.3533 | 0.3574 | 0.2156 | 0.2690 | 0.8658 |
| No log | 2.0 | 176 | 0.2946 | 0.5370 | 0.4529 | 0.4914 | 0.8999 |
| No log | 3.0 | 264 | 0.2786 | 0.5877 | 0.5263 | 0.5553 | 0.9093 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T19:11:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T19:06:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni250v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni250v3_wikigold_split
type: tagged_uni250v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5830763960260363
- name: Recall
type: recall
value: 0.4849002849002849
- name: F1
type: f1
value: 0.5294758127235961
- name: Accuracy
type: accuracy
value: 0.8988582871706847
---
<!-- 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. -->
# Tagged_Uni_250v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3093
- Precision: 0.5831
- Recall: 0.4849
- F1: 0.5295
- Accuracy: 0.8989
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 78 | 0.3468 | 0.3486 | 0.2362 | 0.2816 | 0.8670 |
| No log | 2.0 | 156 | 0.3071 | 0.5484 | 0.4516 | 0.4953 | 0.8943 |
| No log | 3.0 | 234 | 0.3093 | 0.5831 | 0.4849 | 0.5295 | 0.8989 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T19:06:13Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T19:01:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni250v2_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni250v2_wikigold_split
type: tagged_uni250v2_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6101747815230961
- name: Recall
type: recall
value: 0.5595306239267316
- name: F1
type: f1
value: 0.583756345177665
- name: Accuracy
type: accuracy
value: 0.9084434117141919
---
<!-- 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. -->
# Tagged_Uni_250v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v2_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3254
- Precision: 0.6102
- Recall: 0.5595
- F1: 0.5838
- Accuracy: 0.9084
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 91 | 0.3324 | 0.3097 | 0.2604 | 0.2830 | 0.8776 |
| No log | 2.0 | 182 | 0.3415 | 0.5734 | 0.4831 | 0.5244 | 0.9004 |
| No log | 3.0 | 273 | 0.3254 | 0.6102 | 0.5595 | 0.5838 | 0.9084 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T19:00:36Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T18:55:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni250v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni250v1_wikigold_split
type: tagged_uni250v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5971956660293181
- name: Recall
type: recall
value: 0.5290796160361377
- name: F1
type: f1
value: 0.5610778443113772
- name: Accuracy
type: accuracy
value: 0.906793008840565
---
<!-- 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. -->
# Tagged_Uni_250v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3057
- Precision: 0.5972
- Recall: 0.5291
- F1: 0.5611
- Accuracy: 0.9068
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 87 | 0.3972 | 0.2749 | 0.2081 | 0.2369 | 0.8625 |
| No log | 2.0 | 174 | 0.2895 | 0.5545 | 0.5054 | 0.5288 | 0.9059 |
| No log | 3.0 | 261 | 0.3057 | 0.5972 | 0.5291 | 0.5611 | 0.9068 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T18:38:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T18:33:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni100v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni100v7_wikigold_split
type: tagged_uni100v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.23641160949868073
- name: Recall
type: recall
value: 0.11624286455630514
- name: F1
type: f1
value: 0.15585319185945384
- name: Accuracy
type: accuracy
value: 0.8208868954036808
---
<!-- 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. -->
# Tagged_Uni_100v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5083
- Precision: 0.2364
- Recall: 0.1162
- F1: 0.1559
- Accuracy: 0.8209
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 26 | 0.5987 | 0.0582 | 0.0029 | 0.0054 | 0.7847 |
| No log | 2.0 | 52 | 0.5016 | 0.2218 | 0.1002 | 0.1380 | 0.8192 |
| No log | 3.0 | 78 | 0.5083 | 0.2364 | 0.1162 | 0.1559 | 0.8209 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
athairus/xlm-roberta-base-finetuned-panx-de
|
athairus
| 2022-08-11T18:37:59Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T18:28:06Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8663101604278075
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8663
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1690 | 0.8303 |
| 0.1305 | 2.0 | 1050 | 0.1352 | 0.8484 |
| 0.0839 | 3.0 | 1575 | 0.1339 | 0.8663 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
DOOGLAK/Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T18:15:31Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T18:10:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni100v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni100v3_wikigold_split
type: tagged_uni100v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.27637540453074433
- name: Recall
type: recall
value: 0.10801922590437642
- name: F1
type: f1
value: 0.15532921062204438
- name: Accuracy
type: accuracy
value: 0.8105687105062148
---
<!-- 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. -->
# Tagged_Uni_100v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4884
- Precision: 0.2764
- Recall: 0.1080
- F1: 0.1553
- Accuracy: 0.8106
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 26 | 0.6238 | 0.2 | 0.0089 | 0.0170 | 0.7822 |
| No log | 2.0 | 52 | 0.5210 | 0.2497 | 0.0587 | 0.0950 | 0.7971 |
| No log | 3.0 | 78 | 0.4884 | 0.2764 | 0.1080 | 0.1553 | 0.8106 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:58:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:53:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni100v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni100v0_wikigold_split
type: tagged_uni100v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.1801752464403067
- name: Recall
type: recall
value: 0.08303886925795052
- name: F1
type: f1
value: 0.11368348306841741
- name: Accuracy
type: accuracy
value: 0.8143372512510183
---
<!-- 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. -->
# Tagged_Uni_100v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4601
- Precision: 0.1802
- Recall: 0.0830
- F1: 0.1137
- Accuracy: 0.8143
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 33 | 0.5687 | 0.0882 | 0.0015 | 0.0030 | 0.7791 |
| No log | 2.0 | 66 | 0.5410 | 0.1319 | 0.0270 | 0.0448 | 0.7946 |
| No log | 3.0 | 99 | 0.4601 | 0.1802 | 0.0830 | 0.1137 | 0.8143 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:52:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:47:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v9_wikigold_split
type: tagged_uni50v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5
- name: Recall
type: recall
value: 0.000243605359317905
- name: F1
type: f1
value: 0.00048697345994643296
- name: Accuracy
type: accuracy
value: 0.7843220814175171
---
<!-- 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. -->
# Tagged_Uni_50v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6233
- Precision: 0.5
- Recall: 0.0002
- F1: 0.0005
- Accuracy: 0.7843
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 16 | 0.7531 | 0.0 | 0.0 | 0.0 | 0.7788 |
| No log | 2.0 | 32 | 0.6599 | 0.5 | 0.0002 | 0.0005 | 0.7823 |
| No log | 3.0 | 48 | 0.6233 | 0.5 | 0.0002 | 0.0005 | 0.7843 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:47:02Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:41:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v8_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v8_wikigold_split
type: tagged_uni50v8_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.15460526315789475
- name: Recall
type: recall
value: 0.023016650342801176
- name: F1
type: f1
value: 0.04006820119352089
- name: Accuracy
type: accuracy
value: 0.7925892757192432
---
<!-- 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. -->
# Tagged_Uni_50v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v8_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5527
- Precision: 0.1546
- Recall: 0.0230
- F1: 0.0401
- Accuracy: 0.7926
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 19 | 0.6981 | 0.0 | 0.0 | 0.0 | 0.7786 |
| No log | 2.0 | 38 | 0.5851 | 0.1290 | 0.0049 | 0.0094 | 0.7832 |
| No log | 3.0 | 57 | 0.5527 | 0.1546 | 0.0230 | 0.0401 | 0.7926 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:36:45Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:31:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v6_wikigold_split
type: tagged_uni50v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7775983130313839
---
<!-- 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. -->
# Tagged_Uni_50v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6142
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7776
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 17 | 0.7369 | 0.0 | 0.0 | 0.0 | 0.7773 |
| No log | 2.0 | 34 | 0.6359 | 0.0 | 0.0 | 0.0 | 0.7773 |
| No log | 3.0 | 51 | 0.6142 | 0.0 | 0.0 | 0.0 | 0.7776 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:31:02Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:26:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v5_wikigold_split
type: tagged_uni50v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.23113964686998395
- name: Recall
type: recall
value: 0.03495994173343044
- name: F1
type: f1
value: 0.06073386756642767
- name: Accuracy
type: accuracy
value: 0.7909374089595052
---
<!-- 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. -->
# Tagged_Uni_50v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6039
- Precision: 0.2311
- Recall: 0.0350
- F1: 0.0607
- Accuracy: 0.7909
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 26 | 0.6534 | 0.0 | 0.0 | 0.0 | 0.7773 |
| No log | 2.0 | 52 | 0.6056 | 0.1294 | 0.0097 | 0.0181 | 0.7846 |
| No log | 3.0 | 78 | 0.6039 | 0.2311 | 0.0350 | 0.0607 | 0.7909 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:26:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:20:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v4_wikigold_split
type: tagged_uni50v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.27169149868536374
- name: Recall
type: recall
value: 0.07535245503159942
- name: F1
type: f1
value: 0.11798287345385347
- name: Accuracy
type: accuracy
value: 0.8047749037859124
---
<!-- 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. -->
# Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5415
- Precision: 0.2717
- Recall: 0.0754
- F1: 0.1180
- Accuracy: 0.8048
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 25 | 0.6079 | 0.3333 | 0.0015 | 0.0029 | 0.7792 |
| No log | 2.0 | 50 | 0.5345 | 0.2762 | 0.0678 | 0.1089 | 0.8022 |
| No log | 3.0 | 75 | 0.5415 | 0.2717 | 0.0754 | 0.1180 | 0.8048 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:20:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:14:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v3_wikigold_split
type: tagged_uni50v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.14766839378238342
- name: Recall
type: recall
value: 0.013980868285504048
- name: F1
type: f1
value: 0.025543356486668164
- name: Accuracy
type: accuracy
value: 0.7865287304621612
---
<!-- 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. -->
# Tagged_Uni_50v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5987
- Precision: 0.1477
- Recall: 0.0140
- F1: 0.0255
- Accuracy: 0.7865
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 14 | 0.7260 | 0.0 | 0.0 | 0.0 | 0.7789 |
| No log | 2.0 | 28 | 0.6256 | 0.1436 | 0.0140 | 0.0255 | 0.7865 |
| No log | 3.0 | 42 | 0.5987 | 0.1477 | 0.0140 | 0.0255 | 0.7865 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
Yao92/distilbert-base-uncased-finetuned-cola
|
Yao92
| 2022-08-11T17:12:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-11T17:01:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5303243504311796
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8278
- Matthews Correlation: 0.5303
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5225 | 1.0 | 535 | 0.5299 | 0.3973 |
| 0.3485 | 2.0 | 1070 | 0.5279 | 0.4975 |
| 0.2375 | 3.0 | 1605 | 0.5637 | 0.5275 |
| 0.1832 | 4.0 | 2140 | 0.7995 | 0.5249 |
| 0.1301 | 5.0 | 2675 | 0.8278 | 0.5303 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DOOGLAK/Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T17:08:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_uni50v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T17:03:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_uni50v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_uni50v1_wikigold_split
type: tagged_uni50v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.14664804469273743
- name: Recall
type: recall
value: 0.025647288715192965
- name: F1
type: f1
value: 0.043659043659043655
- name: Accuracy
type: accuracy
value: 0.7940580232453374
---
<!-- 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. -->
# Tagged_Uni_50v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5851
- Precision: 0.1466
- Recall: 0.0256
- F1: 0.0437
- Accuracy: 0.7941
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 24 | 0.6704 | 0.0 | 0.0 | 0.0 | 0.7775 |
| No log | 2.0 | 48 | 0.5824 | 0.1479 | 0.0154 | 0.0279 | 0.7895 |
| No log | 3.0 | 72 | 0.5851 | 0.1466 | 0.0256 | 0.0437 | 0.7941 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:57:16Z | 96 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T16:52:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v9_wikigold_split
type: tagged_one500v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7016183412002697
- name: Recall
type: recall
value: 0.7011455525606469
- name: F1
type: f1
value: 0.7013818672059319
- name: Accuracy
type: accuracy
value: 0.9284582154955403
---
<!-- 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. -->
# Tagged_One_500v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2469
- Precision: 0.7016
- Recall: 0.7011
- F1: 0.7014
- Accuracy: 0.9285
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 170 | 0.2908 | 0.5414 | 0.4538 | 0.4938 | 0.9011 |
| No log | 2.0 | 340 | 0.2680 | 0.6629 | 0.6253 | 0.6436 | 0.9172 |
| 0.1121 | 3.0 | 510 | 0.2469 | 0.7016 | 0.7011 | 0.7014 | 0.9285 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
waynedsouza/distilbert-base-uncased-gc-art3e
|
waynedsouza
| 2022-08-11T16:52:03Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-11T16:46:49Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-gc-art3e
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-gc-art3e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0841
- Accuracy: 0.983
- F1: 0.9755
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0576 | 1.0 | 32 | 0.0846 | 0.982 | 0.9731 |
| 0.0388 | 2.0 | 64 | 0.0878 | 0.98 | 0.9737 |
| 0.0372 | 3.0 | 96 | 0.0841 | 0.983 | 0.9755 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
waynedsouza/distilbert-base-uncased-gc-art2e
|
waynedsouza
| 2022-08-11T16:45:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-11T16:39:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-gc-art2e
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-gc-art2e
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0863
- Accuracy: 0.982
- F1: 0.9731
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0875 | 1.0 | 32 | 0.0874 | 0.982 | 0.9731 |
| 0.0711 | 2.0 | 64 | 0.0863 | 0.982 | 0.9731 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
DOOGLAK/Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:45:22Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T16:40:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v7_wikigold_split
type: tagged_one500v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6700655498907502
- name: Recall
type: recall
value: 0.6767193821257815
- name: F1
type: f1
value: 0.6733760292772187
- name: Accuracy
type: accuracy
value: 0.9237216043353603
---
<!-- 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. -->
# Tagged_One_500v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2679
- Precision: 0.6701
- Recall: 0.6767
- F1: 0.6734
- Accuracy: 0.9237
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 156 | 0.3336 | 0.5893 | 0.4855 | 0.5324 | 0.8955 |
| No log | 2.0 | 312 | 0.2580 | 0.6617 | 0.6561 | 0.6589 | 0.9215 |
| No log | 3.0 | 468 | 0.2679 | 0.6701 | 0.6767 | 0.6734 | 0.9237 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:39:36Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T16:33:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v6_wikigold_split
type: tagged_one500v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6866690621631333
- name: Recall
type: recall
value: 0.6719409282700421
- name: F1
type: f1
value: 0.679225164385996
- name: Accuracy
type: accuracy
value: 0.9239838169290094
---
<!-- 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. -->
# Tagged_One_500v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2690
- Precision: 0.6867
- Recall: 0.6719
- F1: 0.6792
- Accuracy: 0.9240
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 189 | 0.2819 | 0.6009 | 0.5352 | 0.5661 | 0.9105 |
| No log | 2.0 | 378 | 0.2614 | 0.6743 | 0.6406 | 0.6571 | 0.9201 |
| 0.11 | 3.0 | 567 | 0.2690 | 0.6867 | 0.6719 | 0.6792 | 0.9240 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
succinctly/dalle-mini-finetuned-medium
|
succinctly
| 2022-08-11T16:25:00Z | 5 | 1 |
transformers
|
[
"transformers",
"jax",
"dallebart",
"text-to-image",
"dalle-mini",
"en",
"dataset:succinctly/medium-titles-and-images",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2022-07-31T18:47:24Z |
---
inference: false
language:
- "en"
thumbnail: "https://drive.google.com/uc?export=view&id=1_n2kT6lBBs8C3rf8xfNURr_N2Ccx-A1S"
tags:
- text-to-image
- dalle-mini
license: "apache-2.0"
datasets:
- "succinctly/medium-titles-and-images"
---
This is the [dalle-mini/dalle-mini](https://huggingface.co/dalle-mini/dalle-mini) text-to-image model fine-tuned on 120k <title, image> pairs from the [Medium](https://medium.com) blogging platform. The full dataset can be found on Kaggle: [Medium Articles Dataset (128k): Metadata + Images](https://www.kaggle.com/datasets/succinctlyai/medium-data).
The goal of this model is to probe the ability of text-to-image models of operating on text prompts that are abstract (like the titles on Medium usually are), as opposed to concrete descriptions of the envisioned visual scene.
[More context here](https://medium.com/@turc.raluca/fine-tuning-dall-e-mini-craiyon-to-generate-blogpost-images-32903cc7aa52).
|
DOOGLAK/Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:21:20Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T16:16:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v3_wikigold_split
type: tagged_one500v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.697499143542309
- name: Recall
type: recall
value: 0.6782145236508994
- name: F1
type: f1
value: 0.6877216686370546
- name: Accuracy
type: accuracy
value: 0.9245400105495051
---
<!-- 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. -->
# Tagged_One_500v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2659
- Precision: 0.6975
- Recall: 0.6782
- F1: 0.6877
- Accuracy: 0.9245
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 175 | 0.2990 | 0.5405 | 0.4600 | 0.4970 | 0.9007 |
| No log | 2.0 | 350 | 0.2789 | 0.6837 | 0.6236 | 0.6523 | 0.9157 |
| 0.1081 | 3.0 | 525 | 0.2659 | 0.6975 | 0.6782 | 0.6877 | 0.9245 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:09:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T16:03:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v1_wikigold_split
type: tagged_one500v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7131782945736435
- name: Recall
type: recall
value: 0.6693121693121693
- name: F1
type: f1
value: 0.690549300580007
- name: Accuracy
type: accuracy
value: 0.9232131948686622
---
<!-- 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. -->
# Tagged_One_500v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2834
- Precision: 0.7132
- Recall: 0.6693
- F1: 0.6905
- Accuracy: 0.9232
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 164 | 0.2830 | 0.4758 | 0.4064 | 0.4384 | 0.9032 |
| No log | 2.0 | 328 | 0.2631 | 0.6901 | 0.6716 | 0.6807 | 0.9232 |
| No log | 3.0 | 492 | 0.2834 | 0.7132 | 0.6693 | 0.6905 | 0.9232 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T16:03:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one500v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:57:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one500v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one500v0_wikigold_split
type: tagged_one500v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.6663055254604551
- name: Recall
type: recall
value: 0.683839881393625
- name: F1
type: f1
value: 0.6749588439729285
- name: Accuracy
type: accuracy
value: 0.9260204081632653
---
<!-- 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. -->
# Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2679
- Precision: 0.6663
- Recall: 0.6838
- F1: 0.6750
- Accuracy: 0.9260
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 173 | 0.2827 | 0.5972 | 0.5556 | 0.5757 | 0.9079 |
| No log | 2.0 | 346 | 0.2668 | 0.6442 | 0.6383 | 0.6412 | 0.9204 |
| 0.1142 | 3.0 | 519 | 0.2679 | 0.6663 | 0.6838 | 0.6750 | 0.9260 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
TheJarmanitor/q-FrozenLake-v1-4x4-noSlippery
|
TheJarmanitor
| 2022-08-11T15:55:03Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T15:51:58Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="TheJarmanitor/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DOOGLAK/Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:45:15Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:40:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v7_wikigold_split
type: tagged_one250v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5509259259259259
- name: Recall
type: recall
value: 0.4675834970530452
- name: F1
type: f1
value: 0.5058448459086079
- name: Accuracy
type: accuracy
value: 0.8893517705222476
---
<!-- 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. -->
# Tagged_One_250v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3809
- Precision: 0.5509
- Recall: 0.4676
- F1: 0.5058
- Accuracy: 0.8894
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 87 | 0.4450 | 0.1912 | 0.1047 | 0.1353 | 0.8278 |
| No log | 2.0 | 174 | 0.3903 | 0.4992 | 0.4176 | 0.4548 | 0.8820 |
| No log | 3.0 | 261 | 0.3809 | 0.5509 | 0.4676 | 0.5058 | 0.8894 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:39:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:33:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v6_wikigold_split
type: tagged_one250v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5705062861026163
- name: Recall
type: recall
value: 0.47162921348314607
- name: F1
type: f1
value: 0.5163770567430417
- name: Accuracy
type: accuracy
value: 0.8943313292578184
---
<!-- 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. -->
# Tagged_One_250v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3273
- Precision: 0.5705
- Recall: 0.4716
- F1: 0.5164
- Accuracy: 0.8943
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 74 | 0.4157 | 0.3169 | 0.1621 | 0.2145 | 0.8462 |
| No log | 2.0 | 148 | 0.3477 | 0.5106 | 0.3941 | 0.4448 | 0.8842 |
| No log | 3.0 | 222 | 0.3273 | 0.5705 | 0.4716 | 0.5164 | 0.8943 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:21:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:16:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v3_wikigold_split
type: tagged_one250v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5783339046966061
- name: Recall
type: recall
value: 0.4806267806267806
- name: F1
type: f1
value: 0.5249727711218297
- name: Accuracy
type: accuracy
value: 0.8981560947699669
---
<!-- 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. -->
# Tagged_One_250v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3179
- Precision: 0.5783
- Recall: 0.4806
- F1: 0.5250
- Accuracy: 0.8982
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 81 | 0.3974 | 0.2778 | 0.1869 | 0.2235 | 0.8530 |
| No log | 2.0 | 162 | 0.3095 | 0.5594 | 0.4470 | 0.4969 | 0.8944 |
| No log | 3.0 | 243 | 0.3179 | 0.5783 | 0.4806 | 0.5250 | 0.8982 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:16:03Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v2_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:10:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v2_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v2_wikigold_split
type: tagged_one250v2_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5859220092531394
- name: Recall
type: recall
value: 0.5074413279908414
- name: F1
type: f1
value: 0.5438650306748466
- name: Accuracy
type: accuracy
value: 0.8979617609173338
---
<!-- 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. -->
# Tagged_One_250v2_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v2_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3573
- Precision: 0.5859
- Recall: 0.5074
- F1: 0.5439
- Accuracy: 0.8980
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 93 | 0.3884 | 0.2899 | 0.2006 | 0.2371 | 0.8583 |
| No log | 2.0 | 186 | 0.3502 | 0.5467 | 0.4705 | 0.5058 | 0.8937 |
| No log | 3.0 | 279 | 0.3573 | 0.5859 | 0.5074 | 0.5439 | 0.8980 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
huggingtweets/pilgrimbeart
|
huggingtweets
| 2022-08-11T15:11:35Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-11T15:10:14Z |
---
language: en
thumbnail: http://www.huggingtweets.com/pilgrimbeart/1660230691248/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/433603570/Pilgrim_Beart_headshot_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Pilgrim Beart</div>
<div style="text-align: center; font-size: 14px;">@pilgrimbeart</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Pilgrim Beart.
| Data | Pilgrim Beart |
| --- | --- |
| Tweets downloaded | 3202 |
| Retweets | 1238 |
| Short tweets | 188 |
| Tweets kept | 1776 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/23t6x9nz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @pilgrimbeart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2tsil6bf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2tsil6bf/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/pilgrimbeart')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DOOGLAK/Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:10:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T15:05:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v1_wikigold_split
type: tagged_one250v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5896180215475024
- name: Recall
type: recall
value: 0.5098814229249012
- name: F1
type: f1
value: 0.5468584405753218
- name: Accuracy
type: accuracy
value: 0.8999339498018494
---
<!-- 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. -->
# Tagged_One_250v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3321
- Precision: 0.5896
- Recall: 0.5099
- F1: 0.5469
- Accuracy: 0.8999
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 89 | 0.3518 | 0.3537 | 0.2945 | 0.3214 | 0.8761 |
| No log | 2.0 | 178 | 0.3115 | 0.5583 | 0.4867 | 0.5201 | 0.8974 |
| No log | 3.0 | 267 | 0.3321 | 0.5896 | 0.5099 | 0.5469 | 0.8999 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
huggingtweets/henryfarrell
|
huggingtweets
| 2022-08-11T15:08:57Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-08-11T15:08:06Z |
---
language: en
thumbnail: http://www.huggingtweets.com/henryfarrell/1660230533136/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1161630886963683328/SgNq1g_6_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Henry Farrell</div>
<div style="text-align: center; font-size: 14px;">@henryfarrell</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Henry Farrell.
| Data | Henry Farrell |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 1491 |
| Short tweets | 120 |
| Tweets kept | 1636 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3s3w7i53/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @henryfarrell's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/aifgbb0k) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/aifgbb0k/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/henryfarrell')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DOOGLAK/Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T15:04:33Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one250v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:59:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one250v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one250v0_wikigold_split
type: tagged_one250v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.5125421190565331
- name: Recall
type: recall
value: 0.3694009713977334
- name: F1
type: f1
value: 0.4293554963148816
- name: Accuracy
type: accuracy
value: 0.8786972744569918
---
<!-- 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. -->
# Tagged_One_250v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one250v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4287
- Precision: 0.5125
- Recall: 0.3694
- F1: 0.4294
- Accuracy: 0.8787
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 96 | 0.4352 | 0.3056 | 0.1692 | 0.2178 | 0.8448 |
| No log | 2.0 | 192 | 0.3881 | 0.4394 | 0.3295 | 0.3766 | 0.8773 |
| No log | 3.0 | 288 | 0.4287 | 0.5125 | 0.3694 | 0.4294 | 0.8787 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:58:20Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v9_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:53:00Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v9_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v9_wikigold_split
type: tagged_one100v9_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.3040441176470588
- name: Recall
type: recall
value: 0.21319927816447537
- name: F1
type: f1
value: 0.2506440369752993
- name: Accuracy
type: accuracy
value: 0.8538912172644546
---
<!-- 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. -->
# Tagged_One_100v9_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v9_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4255
- Precision: 0.3040
- Recall: 0.2132
- F1: 0.2506
- Accuracy: 0.8539
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 40 | 0.5167 | 0.1936 | 0.0376 | 0.0630 | 0.8004 |
| No log | 2.0 | 80 | 0.4406 | 0.2405 | 0.1441 | 0.1802 | 0.8385 |
| No log | 3.0 | 120 | 0.4255 | 0.3040 | 0.2132 | 0.2506 | 0.8539 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:35:28Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:30:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v5_wikigold_split
type: tagged_one100v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.27906976744186046
- name: Recall
type: recall
value: 0.21439509954058192
- name: F1
type: f1
value: 0.24249422632794454
- name: Accuracy
type: accuracy
value: 0.8484087686263571
---
<!-- 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. -->
# Tagged_One_100v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4636
- Precision: 0.2791
- Recall: 0.2144
- F1: 0.2425
- Accuracy: 0.8484
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 41 | 0.5040 | 0.2172 | 0.1266 | 0.1599 | 0.8226 |
| No log | 2.0 | 82 | 0.4381 | 0.2656 | 0.2154 | 0.2379 | 0.8475 |
| No log | 3.0 | 123 | 0.4636 | 0.2791 | 0.2144 | 0.2425 | 0.8484 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:30:11Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:25:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v4_wikigold_split
type: tagged_one100v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.16494312306101344
- name: Recall
type: recall
value: 0.08177390412714688
- name: F1
type: f1
value: 0.10934018851756641
- name: Accuracy
type: accuracy
value: 0.8299042951592769
---
<!-- 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. -->
# Tagged_One_100v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4506
- Precision: 0.1649
- Recall: 0.0818
- F1: 0.1093
- Accuracy: 0.8299
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 34 | 0.5649 | 0.0 | 0.0 | 0.0 | 0.7875 |
| No log | 2.0 | 68 | 0.4687 | 0.1197 | 0.0400 | 0.0600 | 0.8147 |
| No log | 3.0 | 102 | 0.4506 | 0.1649 | 0.0818 | 0.1093 | 0.8299 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:24:44Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v3_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:19:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v3_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v3_wikigold_split
type: tagged_one100v3_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.20557491289198607
- name: Recall
type: recall
value: 0.08955223880597014
- name: F1
type: f1
value: 0.12475770925110131
- name: Accuracy
type: accuracy
value: 0.8123509941439252
---
<!-- 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. -->
# Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v3_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4863
- Precision: 0.2056
- Recall: 0.0896
- F1: 0.1248
- Accuracy: 0.8124
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 26 | 0.6246 | 0.1111 | 0.0003 | 0.0005 | 0.7773 |
| No log | 2.0 | 52 | 0.5272 | 0.1238 | 0.0296 | 0.0478 | 0.7948 |
| No log | 3.0 | 78 | 0.4863 | 0.2056 | 0.0896 | 0.1248 | 0.8124 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
harish/t5-e2e-10epochs-lr1e4-alpha0-9
|
harish
| 2022-08-11T14:15:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-11T14:10:36Z |
---
license: cc-by-nc-sa-4.0
---
|
DOOGLAK/Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:13:25Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v1_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:08:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v1_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v1_wikigold_split
type: tagged_one100v1_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.23249893932965635
- name: Recall
type: recall
value: 0.14241164241164242
- name: F1
type: f1
value: 0.17663174858984693
- name: Accuracy
type: accuracy
value: 0.8347454643603164
---
<!-- 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. -->
# Tagged_One_100v1_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v1_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4613
- Precision: 0.2325
- Recall: 0.1424
- F1: 0.1766
- Accuracy: 0.8347
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 39 | 0.5179 | 0.1311 | 0.0398 | 0.0610 | 0.8044 |
| No log | 2.0 | 78 | 0.4609 | 0.2297 | 0.1351 | 0.1702 | 0.8327 |
| No log | 3.0 | 117 | 0.4613 | 0.2325 | 0.1424 | 0.1766 | 0.8347 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T14:07:39Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one100v0_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T14:02:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one100v0_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one100v0_wikigold_split
type: tagged_one100v0_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.16896060749881348
- name: Recall
type: recall
value: 0.08985360928823827
- name: F1
type: f1
value: 0.11731751524139067
- name: Accuracy
type: accuracy
value: 0.8183405097172117
---
<!-- 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. -->
# Tagged_One_100v0_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v0_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4700
- Precision: 0.1690
- Recall: 0.0899
- F1: 0.1173
- Accuracy: 0.8183
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 32 | 0.5975 | 0.1034 | 0.0015 | 0.0030 | 0.7790 |
| No log | 2.0 | 64 | 0.4756 | 0.1607 | 0.0765 | 0.1036 | 0.8137 |
| No log | 3.0 | 96 | 0.4700 | 0.1690 | 0.0899 | 0.1173 | 0.8183 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T13:51:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v7_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T13:46:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one50v7_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one50v7_wikigold_split
type: tagged_one50v7_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.0
- name: Recall
type: recall
value: 0.0
- name: F1
type: f1
value: 0.0
- name: Accuracy
type: accuracy
value: 0.7785234899328859
---
<!-- 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. -->
# Tagged_One_50v7_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v7_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6441
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.7785
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.0 | 13 | 0.7609 | 0.0 | 0.0 | 0.0 | 0.7783 |
| No log | 2.0 | 26 | 0.6742 | 0.0 | 0.0 | 0.0 | 0.7783 |
| No log | 3.0 | 39 | 0.6441 | 0.0 | 0.0 | 0.0 | 0.7785 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T13:40:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v5_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T13:36:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one50v5_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one50v5_wikigold_split
type: tagged_one50v5_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.11643835616438356
- name: Recall
type: recall
value: 0.008254430687059966
- name: F1
type: f1
value: 0.015416005440943096
- name: Accuracy
type: accuracy
value: 0.7840127288617977
---
<!-- 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. -->
# Tagged_One_50v5_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6440
- Precision: 0.1164
- Recall: 0.0083
- F1: 0.0154
- Accuracy: 0.7840
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 26 | 0.6934 | 0.0 | 0.0 | 0.0 | 0.7768 |
| No log | 2.0 | 52 | 0.6426 | 0.0855 | 0.0024 | 0.0047 | 0.7799 |
| No log | 3.0 | 78 | 0.6440 | 0.1164 | 0.0083 | 0.0154 | 0.7840 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T13:35:54Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:tagged_one50v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T13:31:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tagged_one50v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tagged_one50v4_wikigold_split
type: tagged_one50v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.3559670781893004
- name: Recall
type: recall
value: 0.04205153135634419
- name: F1
type: f1
value: 0.07521739130434783
- name: Accuracy
type: accuracy
value: 0.7920209433455652
---
<!-- 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. -->
# Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5788
- Precision: 0.3560
- Recall: 0.0421
- F1: 0.0752
- Accuracy: 0.7920
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 22 | 0.6655 | 0.0 | 0.0 | 0.0 | 0.7775 |
| No log | 2.0 | 44 | 0.5894 | 0.4073 | 0.0272 | 0.0510 | 0.7856 |
| No log | 3.0 | 66 | 0.5788 | 0.3560 | 0.0421 | 0.0752 | 0.7920 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
mrm8488/Worm_v2
|
mrm8488
| 2022-08-11T13:35:34Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Worm",
"region:us"
] |
reinforcement-learning
| 2022-08-11T13:35:19Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Worm
library_name: ml-agents
---
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Worm
2. Step 1: Write your model_id: mrm8488/Worm_v2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
carted-nlp/categorization-finetuned-20220721-164940-distilled-20220811-074207
|
carted-nlp
| 2022-08-11T13:09:13Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-08-11T07:43:56Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: categorization-finetuned-20220721-164940-distilled-20220811-074207
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. -->
# categorization-finetuned-20220721-164940-distilled-20220811-074207
This model is a fine-tuned version of [carted-nlp/categorization-finetuned-20220721-164940](https://huggingface.co/carted-nlp/categorization-finetuned-20220721-164940) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Accuracy: 0.8771
- F1: 0.8763
## 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: 96
- seed: 314
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1500
- num_epochs: 30.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.5644 | 0.56 | 2500 | 0.2739 | 0.7822 | 0.7774 |
| 0.2658 | 1.12 | 5000 | 0.2288 | 0.8159 | 0.8127 |
| 0.2307 | 1.69 | 7500 | 0.2082 | 0.8298 | 0.8273 |
| 0.2126 | 2.25 | 10000 | 0.1970 | 0.8389 | 0.8370 |
| 0.2012 | 2.81 | 12500 | 0.1888 | 0.8450 | 0.8433 |
| 0.1903 | 3.37 | 15000 | 0.1829 | 0.8496 | 0.8485 |
| 0.1846 | 3.94 | 17500 | 0.1783 | 0.8529 | 0.8511 |
| 0.1771 | 4.5 | 20000 | 0.1750 | 0.8548 | 0.8537 |
| 0.1726 | 5.06 | 22500 | 0.1727 | 0.8577 | 0.8564 |
| 0.1673 | 5.62 | 25000 | 0.1683 | 0.8602 | 0.8591 |
| 0.1648 | 6.19 | 27500 | 0.1675 | 0.8608 | 0.8597 |
| 0.1596 | 6.75 | 30000 | 0.1657 | 0.8630 | 0.8620 |
| 0.1563 | 7.31 | 32500 | 0.1635 | 0.8646 | 0.8639 |
| 0.154 | 7.87 | 35000 | 0.1613 | 0.8656 | 0.8647 |
| 0.1496 | 8.43 | 37500 | 0.1611 | 0.8666 | 0.8656 |
| 0.1496 | 9.0 | 40000 | 0.1598 | 0.8676 | 0.8669 |
| 0.1445 | 9.56 | 42500 | 0.1594 | 0.8681 | 0.8671 |
| 0.1435 | 10.12 | 45000 | 0.1588 | 0.8688 | 0.8679 |
| 0.1407 | 10.68 | 47500 | 0.1568 | 0.8703 | 0.8695 |
| 0.1382 | 11.25 | 50000 | 0.1564 | 0.8708 | 0.8700 |
| 0.1372 | 11.81 | 52500 | 0.1550 | 0.8720 | 0.8713 |
| 0.1344 | 12.37 | 55000 | 0.1559 | 0.8718 | 0.8708 |
| 0.1337 | 12.93 | 57500 | 0.1540 | 0.8735 | 0.8729 |
| 0.1303 | 13.5 | 60000 | 0.1541 | 0.8729 | 0.8721 |
| 0.1304 | 14.06 | 62500 | 0.1531 | 0.8735 | 0.8727 |
| 0.1274 | 14.62 | 65000 | 0.1535 | 0.8736 | 0.8727 |
| 0.1266 | 15.18 | 67500 | 0.1527 | 0.8750 | 0.8742 |
| 0.1251 | 15.74 | 70000 | 0.1525 | 0.8755 | 0.8748 |
| 0.1234 | 16.31 | 72500 | 0.1528 | 0.8753 | 0.8745 |
| 0.1229 | 16.87 | 75000 | 0.1516 | 0.8760 | 0.8753 |
| 0.121 | 17.43 | 77500 | 0.1523 | 0.8759 | 0.8752 |
| 0.1212 | 17.99 | 80000 | 0.1515 | 0.8760 | 0.8754 |
| 0.1185 | 18.56 | 82500 | 0.1514 | 0.8765 | 0.8757 |
| 0.1186 | 19.12 | 85000 | 0.1516 | 0.8766 | 0.8760 |
| 0.1172 | 19.68 | 87500 | 0.1506 | 0.8774 | 0.8767 |
| 0.1164 | 20.24 | 90000 | 0.1513 | 0.8770 | 0.8763 |
| 0.116 | 20.81 | 92500 | 0.1507 | 0.8774 | 0.8767 |
| 0.1145 | 21.37 | 95000 | 0.1507 | 0.8777 | 0.8770 |
| 0.1143 | 21.93 | 97500 | 0.1506 | 0.8776 | 0.8770 |
| 0.1131 | 22.49 | 100000 | 0.1507 | 0.8779 | 0.8772 |
| 0.1131 | 23.05 | 102500 | 0.1505 | 0.8779 | 0.8772 |
| 0.1123 | 23.62 | 105000 | 0.1506 | 0.8781 | 0.8774 |
| 0.1117 | 24.18 | 107500 | 0.1504 | 0.8783 | 0.8776 |
| 0.1118 | 24.74 | 110000 | 0.1503 | 0.8784 | 0.8777 |
| 0.1111 | 25.3 | 112500 | 0.1503 | 0.8783 | 0.8776 |
| 0.1111 | 25.87 | 115000 | 0.1502 | 0.8784 | 0.8777 |
| 0.1105 | 26.43 | 117500 | 0.1504 | 0.8783 | 0.8776 |
| 0.1105 | 26.99 | 120000 | 0.1502 | 0.8786 | 0.8779 |
| 0.1104 | 27.55 | 122500 | 0.1503 | 0.8786 | 0.8779 |
| 0.1096 | 28.12 | 125000 | 0.1502 | 0.8785 | 0.8779 |
| 0.1101 | 28.68 | 127500 | 0.1501 | 0.8786 | 0.8779 |
| 0.1101 | 29.24 | 130000 | 0.1502 | 0.8786 | 0.8779 |
| 0.1094 | 29.8 | 132500 | 0.1501 | 0.8786 | 0.8779 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
ClementRomac/TA_ALP-GMM_SAC_spider_s1
|
ClementRomac
| 2022-08-11T12:59:15Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"region:us"
] |
reinforcement-learning
| 2022-08-11T12:48:21Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo).
*This policy was not part of TeachMyAgent's benchmark*
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ALP-GMM'
'morphology': 'spider'}
```
|
DOOGLAK/Article_500v8_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T12:58:50Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v8_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T12:53:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v8_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v8_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v8_wikigold_split
type: article500v8_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7349189934505344
- name: Recall
type: recall
value: 0.7560283687943262
- name: F1
type: f1
value: 0.7453242440132843
- name: Accuracy
type: accuracy
value: 0.9421215763172877
---
<!-- 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. -->
# Article_500v8_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v8_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2113
- Precision: 0.7349
- Recall: 0.7560
- F1: 0.7453
- Accuracy: 0.9421
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 191 | 0.1914 | 0.7105 | 0.7181 | 0.7143 | 0.9382 |
| No log | 2.0 | 382 | 0.2045 | 0.7283 | 0.7574 | 0.7426 | 0.9408 |
| 0.1441 | 3.0 | 573 | 0.2113 | 0.7349 | 0.7560 | 0.7453 | 0.9421 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_500v6_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T12:46:58Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v6_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T12:41:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v6_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v6_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v6_wikigold_split
type: article500v6_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7276069518716578
- name: Recall
type: recall
value: 0.7654711673699015
- name: F1
type: f1
value: 0.7460589444825222
- name: Accuracy
type: accuracy
value: 0.944971237119919
---
<!-- 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. -->
# Article_500v6_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v6_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2052
- Precision: 0.7276
- Recall: 0.7655
- F1: 0.7461
- Accuracy: 0.9450
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 209 | 0.1846 | 0.7211 | 0.7472 | 0.7339 | 0.9434 |
| No log | 2.0 | 418 | 0.2111 | 0.7114 | 0.7384 | 0.7246 | 0.9410 |
| 0.1368 | 3.0 | 627 | 0.2052 | 0.7276 | 0.7655 | 0.7461 | 0.9450 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
DOOGLAK/Article_500v4_NER_Model_3Epochs_AUGMENTED
|
DOOGLAK
| 2022-08-11T12:34:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:article500v4_wikigold_split",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-11T12:29:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- article500v4_wikigold_split
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Article_500v4_NER_Model_3Epochs_AUGMENTED
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: article500v4_wikigold_split
type: article500v4_wikigold_split
args: default
metrics:
- name: Precision
type: precision
value: 0.7284386021160628
- name: Recall
type: recall
value: 0.7543160690571049
- name: F1
type: f1
value: 0.7411515250366988
- name: Accuracy
type: accuracy
value: 0.9409116656232299
---
<!-- 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. -->
# Article_500v4_NER_Model_3Epochs_AUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v4_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2097
- Precision: 0.7284
- Recall: 0.7543
- F1: 0.7412
- Accuracy: 0.9409
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 211 | 0.1880 | 0.7139 | 0.7480 | 0.7305 | 0.9400 |
| No log | 2.0 | 422 | 0.2043 | 0.7266 | 0.7367 | 0.7316 | 0.9388 |
| 0.135 | 3.0 | 633 | 0.2097 | 0.7284 | 0.7543 | 0.7412 | 0.9409 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
Eylul/ppo-LunarLander-v2
|
Eylul
| 2022-08-11T12:25:34Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:22:27Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 180.17 +/- 95.47
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
harish/t5-e2e-10epochs-lr1e4-alpha0-1PLUSalpha0-9-e30
|
harish
| 2022-08-11T12:17:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-11T12:14:25Z |
---
license: cc-by-nc-sa-4.0
---
|
flowers-team/TA_ALP-GMM_SAC_chimpanzee_s28
|
flowers-team
| 2022-08-11T12:07:02Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T10:12:39Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ALP-GMM_SAC_chimpanzee_s28
results:
- metrics:
- type: mean_reward
value: -53.98 +/- 7.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ALP-GMM'
'morphology': 'climbing_profile_chimpanzee'}
```
|
harish/t5-e2e-10epochs-lr1e4-alpha0-1PLUSalpha0-9-e20
|
harish
| 2022-08-11T12:04:57Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-11T12:01:05Z |
---
license: cc-by-nc-sa-4.0
---
|
flowers-team/TA_ADR_SAC_bipedal_s2
|
flowers-team
| 2022-08-11T11:58:49Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:58:38Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ADR_SAC_bipedal_s2
results:
- metrics:
- type: mean_reward
value: 189.10 +/- 122.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ADR'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_ADR_SAC_bipedal_s1
|
flowers-team
| 2022-08-11T11:58:36Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:58:26Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ADR_SAC_bipedal_s1
results:
- metrics:
- type: mean_reward
value: 212.60 +/- 137.22
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ADR'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_ALP-GMM_SAC_fish_s37
|
flowers-team
| 2022-08-11T11:57:39Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T10:13:56Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ALP-GMM_SAC_fish_s37
results:
- metrics:
- type: mean_reward
value: 242.06 +/- 143.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ALP-GMM'
'morphology': 'fish'}
```
|
flowers-team/TA_Random_SAC_bipedal_s1
|
flowers-team
| 2022-08-11T11:57:10Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:57:00Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: Random_SAC_bipedal_s1
results:
- metrics:
- type: mean_reward
value: 169.61 +/- 124.96
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'Random'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_Random_SAC_bipedal_s5
|
flowers-team
| 2022-08-11T11:56:45Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:56:35Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: Random_SAC_bipedal_s5
results:
- metrics:
- type: mean_reward
value: 188.35 +/- 145.54
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'Random'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_ALP-GMM_SAC_bipedal_s2
|
flowers-team
| 2022-08-11T11:56:33Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T10:13:32Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ALP-GMM_SAC_bipedal_s2
results:
- metrics:
- type: mean_reward
value: 222.77 +/- 137.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ALP-GMM'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_ALP-GMM_SAC_bipedal_s12
|
flowers-team
| 2022-08-11T11:56:23Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T10:13:19Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: ALP-GMM_SAC_bipedal_s12
results:
- metrics:
- type: mean_reward
value: 229.56 +/- 132.91
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'ALP-GMM'
'morphology': 'old_classic_bipedal'}
```
|
flowers-team/TA_GoalGAN_SAC_chimpanzee_s15
|
flowers-team
| 2022-08-11T11:56:03Z | 0 | 0 | null |
[
"sac",
"deep-reinforcement-learning",
"reinforcement-learning",
"teach-my-agent-parkour",
"arxiv:2103.09815",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-08-11T11:55:52Z |
---
tags:
- sac
- deep-reinforcement-learning
- reinforcement-learning
- teach-my-agent-parkour
model-index:
- name: GoalGAN_SAC_chimpanzee_s15
results:
- metrics:
- type: mean_reward
value: -48.56 +/- 77.61
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: teach-my-agent-parkour
type: teach-my-agent-parkour
---
# Deep RL Agent Playing TeachMyAgent's parkour.
You can find more info about TeachMyAgent [here](https://developmentalsystems.org/TeachMyAgent/).
Results of our benchmark can be found in our [paper](https://arxiv.org/pdf/2103.09815.pdf).
You can test this policy [here](https://huggingface.co/spaces/flowers-team/Interactive_DeepRL_Demo)
## Results
Percentage of mastered tasks (i.e. reward >= 230) after 20 millions steps on the Parkour track.
Results shown are averages over 16 seeds along with the standard deviation for each morphology as well as the aggregation of the 48 seeds in the *Overall* column.
We highlight the best results in bold.
| Algorithm | BipedalWalker | Fish | Climber | Overall |
|---------------|----------------|---------------|--------------|---------------|
| Random | 27.25 (± 10.7) | 23.6 (± 21.3) | 0.0 (± 0.0) | 16.9 (± 18.3) |
| ADR | 14.7 (± 19.4) | 5.3 (± 20.6) | 0.0 (± 0.0) | 6.7 (± 17.4) |
| ALP-GMM | **42.7** (± 11.2) | 36.1 (± 28.5) | 0.4 (± 1.2) | **26.4** (± 25.7) |
| Covar-GMM | 35.7 (± 15.9) | 29.9 (± 27.9) | 0.5 (± 1.9) | 22.1 (± 24.2) |
| GoalGAN | 25.4 (± 24.7) | 34.7 ± 37.0) | 0.8 (± 2.7) | 20.3 (± 29.5) |
| RIAC | 31.2 (± 8.2) | **37.4** (± 25.4) | 0.4 (± 1.4) | 23.0 (± 22.4) |
| SPDL | 30.6 (± 22.8) | 9.0 (± 24.2) | **1.0** (± 3.4) | 13.5 (± 23.0) |
| Setter-Solver | 28.75 (± 20.7) | 5.1 (± 7.6) | 0.0 (± 0.0) | 11.3 (± 17.9) |
# Hyperparameters
```python
{'student': 'SAC'
'environment': 'parkour'
'training_steps': 20000000
'n_evaluation_tasks': 100
'teacher': 'GoalGAN'
'morphology': 'climbing_profile_chimpanzee'}
```
|
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