modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 00:47:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
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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| card
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|---|---|---|---|---|---|---|---|---|---|
HillZhang/real_learner_bart_CGEC
|
HillZhang
| 2023-05-27T04:43:13Z | 488 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Chinese",
"seq2seq",
"grammar",
"zh",
"arxiv:2305.16023",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-24T15:28:50Z |
---
tags:
- text2text-generation
- Chinese
- seq2seq
- grammar
language: zh
license: apache-2.0
---
# Pseudo-Native-BART-CGEC
This model is a cutting-edge CGEC model based on [Chinese BART-large](https://huggingface.co/fnlp/bart-large-chinese).
It is trained with HSK and Lang8 learner CGEC data (about 1.3M).
More details can be found in our [Github](https://github.com/HillZhang1999/NaSGEC) and the [paper](https://arxiv.org/pdf/2305.16023.pdf).
## Usage
pip install transformers
```
from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
tokenizer = BertTokenizer.from_pretrained("HillZhang/real_learner_bart_CGEC")
model = BartForConditionalGeneration.from_pretrained("HillZhang/real_learner_bart_CGEC")
encoded_input = tokenizer(["北京是中国的都。", "他说：”我最爱的运动是打蓝球“", "我每天大约喝5次水左右。", "今天，我非常开开心。"], return_tensors="pt", padding=True, truncation=True)
if "token_type_ids" in encoded_input:
del encoded_input["token_type_ids"]
output = model.generate(**encoded_input)
print(tokenizer.batch_decode(output, skip_special_tokens=True))
```
## Citation
```
@inproceedings{zhang-etal-2023-nasgec,
title = "{Na}{SGEC}: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts",
author = "Zhang, Yue and
Zhang, Bo and
Jiang, Haochen and
Li, Zhenghua and
Li, Chen and
Huang, Fei and
Zhang, Min"
booktitle = "Findings of ACL",
year = "2023"
}
```
|
HillZhang/real_learner_bart_CGEC_exam
|
HillZhang
| 2023-05-27T04:43:02Z | 120 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Chinese",
"seq2seq",
"grammar",
"zh",
"arxiv:2305.16023",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-24T15:28:28Z |
---
tags:
- text2text-generation
- Chinese
- seq2seq
- grammar
language: zh
license: apache-2.0
---
# Pseudo-Native-BART-CGEC
This model is a cutting-edge CGEC model based on [Chinese BART-large](https://huggingface.co/fnlp/bart-large-chinese).
It is trained with HSK and Lang8 learner CGEC data (about 1.3M) and human-annotated training data for the exam domain.
More details can be found in our [Github](https://github.com/HillZhang1999/NaSGEC) and the [paper](https://arxiv.org/pdf/2305.16023.pdf).
## Usage
pip install transformers
```
from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
tokenizer = BertTokenizer.from_pretrained("HillZhang/real_learner_bart_CGEC_exam")
model = BartForConditionalGeneration.from_pretrained("HillZhang/real_learner_bart_CGEC_exam")
encoded_input = tokenizer(["北京是中国的都。", "他说：”我最爱的运动是打蓝球“", "我每天大约喝5次水左右。", "今天，我非常开开心。"], return_tensors="pt", padding=True, truncation=True)
if "token_type_ids" in encoded_input:
del encoded_input["token_type_ids"]
output = model.generate(**encoded_input)
print(tokenizer.batch_decode(output, skip_special_tokens=True))
```
## Citation
```
@inproceedings{zhang-etal-2023-nasgec,
title = "{Na}{SGEC}: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts",
author = "Zhang, Yue and
Zhang, Bo and
Jiang, Haochen and
Li, Zhenghua and
Li, Chen and
Huang, Fei and
Zhang, Min"
booktitle = "Findings of ACL",
year = "2023"
}
```
|
HillZhang/pseudo_native_bart_CGEC
|
HillZhang
| 2023-05-27T04:39:23Z | 304 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"Chinese",
"seq2seq",
"grammar",
"zh",
"arxiv:2305.16023",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-24T15:19:59Z |
---
tags:
- text2text-generation
- Chinese
- seq2seq
- grammar
language: zh
license: apache-2.0
---
# Pseudo-Native-BART-CGEC
This model is a cutting-edge CGEC model based on [Chinese BART-large](https://huggingface.co/fnlp/bart-large-chinese).
It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
More details can be found in our [Github](https://github.com/HillZhang1999/NaSGEC) and the [paper](https://arxiv.org/pdf/2305.16023.pdf).
## Usage
pip install transformers
```
from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
tokenizer = BertTokenizer.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
model = BartForConditionalGeneration.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
encoded_input = tokenizer(["北京是中国的都。", "他说：”我最爱的运动是打蓝球“", "我每天大约喝5次水左右。", "今天，我非常开开心。"], return_tensors="pt", padding=True, truncation=True)
if "token_type_ids" in encoded_input:
del encoded_input["token_type_ids"]
output = model.generate(**encoded_input)
print(tokenizer.batch_decode(output, skip_special_tokens=True))
```
## Citation
```
@inproceedings{zhang-etal-2023-nasgec,
title = "{Na}{SGEC}: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts",
author = "Zhang, Yue and
Zhang, Bo and
Jiang, Haochen and
Li, Zhenghua and
Li, Chen and
Huang, Fei and
Zhang, Min"
booktitle = "Findings of ACL",
year = "2023"
}
```
|
YakovElm/Hyperledger10Classic_64
|
YakovElm
| 2023-05-27T04:27:49Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T04:27:14Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger10Classic_64
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger10Classic_64
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2869
- Train Accuracy: 0.8865
- Validation Loss: 0.4772
- Validation Accuracy: 0.8600
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3638 | 0.8831 | 0.3750 | 0.8600 | 0 |
| 0.3325 | 0.8838 | 0.3629 | 0.8600 | 1 |
| 0.2869 | 0.8865 | 0.4772 | 0.8600 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Jira20Classic_256
|
YakovElm
| 2023-05-27T04:22:30Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T04:21:54Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira20Classic_256
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira20Classic_256
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2410
- Train Accuracy: 0.8972
- Validation Loss: 0.2703
- Validation Accuracy: 0.9338
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3731 | 0.8562 | 0.2694 | 0.9338 | 0 |
| 0.3110 | 0.8772 | 0.2464 | 0.9338 | 1 |
| 0.2410 | 0.8972 | 0.2703 | 0.9338 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Varaprabha/Qtable_taxi
|
Varaprabha
| 2023-05-27T04:13:34Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-27T04:13:32Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Qtable_taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Varaprbha/Qtable_taxi", 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"])
```
|
Varaprabha/q-FrozenLake-v1-4x4-noSlippery
|
Varaprabha
| 2023-05-27T04:08:45Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-27T04:08:43Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Varaprbha/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"])
```
|
YakovElm/Hyperledger5Classic_64
|
YakovElm
| 2023-05-27T04:03:05Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T04:02:31Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger5Classic_64
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger5Classic_64
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3683
- Train Accuracy: 0.8561
- Validation Loss: 0.4172
- Validation Accuracy: 0.8351
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4207 | 0.8481 | 0.4357 | 0.8361 | 0 |
| 0.3940 | 0.8547 | 0.4199 | 0.8361 | 1 |
| 0.3683 | 0.8561 | 0.4172 | 0.8351 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BALAKA/wav2vec2-large-xlsr-53-th-swear-words
|
BALAKA
| 2023-05-27T03:59:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-25T09:32:51Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-th-main
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: th
split: validation
args: th
metrics:
- name: Wer
type: wer
value: 0.4686162624821683
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-th-main
This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1340
- Wer: 0.4686
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2524 | 3.23 | 100 | 3.3222 | 1.0 |
| 3.2913 | 6.45 | 200 | 3.1818 | 1.0 |
| 2.222 | 9.68 | 300 | 1.2497 | 0.5335 |
| 1.1558 | 12.9 | 400 | 1.0792 | 0.5214 |
| 0.934 | 16.13 | 500 | 1.0663 | 0.4986 |
| 0.8023 | 19.35 | 600 | 1.0331 | 0.4893 |
| 0.7041 | 22.58 | 700 | 1.0801 | 0.4800 |
| 0.6576 | 25.81 | 800 | 1.1123 | 0.4886 |
| 0.6061 | 29.03 | 900 | 1.0748 | 0.4829 |
| 0.5649 | 32.26 | 1000 | 1.1187 | 0.4679 |
| 0.5717 | 35.48 | 1100 | 1.1267 | 0.4715 |
| 0.5267 | 38.71 | 1200 | 1.1340 | 0.4686 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.13.3
|
YakovElm/Hyperledger20Classic_32
|
YakovElm
| 2023-05-27T03:34:15Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T03:33:41Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger20Classic_32
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger20Classic_32
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2565
- Train Accuracy: 0.9149
- Validation Loss: 0.3101
- Validation Accuracy: 0.8983
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3031 | 0.9059 | 0.3074 | 0.8983 | 0 |
| 0.2700 | 0.9149 | 0.2988 | 0.8983 | 1 |
| 0.2565 | 0.9149 | 0.3101 | 0.8983 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Hyperledger15Classic_32
|
YakovElm
| 2023-05-27T03:18:38Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T03:17:57Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_32
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_32
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2991
- Train Accuracy: 0.9028
- Validation Loss: 0.3422
- Validation Accuracy: 0.8807
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3396 | 0.8914 | 0.3557 | 0.8807 | 0 |
| 0.3083 | 0.9035 | 0.3524 | 0.8807 | 1 |
| 0.2991 | 0.9028 | 0.3422 | 0.8807 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Apache20Classic_64
|
YakovElm
| 2023-05-27T02:30:58Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T02:30:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache20Classic_64
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache20Classic_64
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1374
- Train Accuracy: 0.9624
- Validation Loss: 0.3081
- Validation Accuracy: 0.9055
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1664 | 0.9620 | 0.3171 | 0.9055 | 0 |
| 0.1522 | 0.9624 | 0.2966 | 0.9055 | 1 |
| 0.1374 | 0.9624 | 0.3081 | 0.9055 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ryosuke/an2-stable-diffusion
|
Ryosuke
| 2023-05-27T01:21:44Z | 31 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2022-11-27T04:46:50Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### an2-stable-diffusion Dreambooth model trained by Ryosuke with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb)
Sample pictures of this concept:
00074-3537062306-portrait
|
YakovElm/Jira10Classic_256
|
YakovElm
| 2023-05-27T01:19:24Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-27T01:18:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira10Classic_256
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira10Classic_256
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3342
- Train Accuracy: 0.8405
- Validation Loss: 0.7061
- Validation Accuracy: 0.6088
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5118 | 0.7817 | 0.8080 | 0.4921 | 0 |
| 0.4265 | 0.7849 | 0.8772 | 0.4921 | 1 |
| 0.3342 | 0.8405 | 0.7061 | 0.6088 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DelusionalDreams/q-FrozenLake-v1-4x4-noSlippery
|
DelusionalDreams
| 2023-05-27T01:11:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-27T01:11:22Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DelusionalDreams/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"])
```
|
stevbach/a2c-PandaReachDense-v2
|
stevbach
| 2023-05-27T01:10:22Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T23:49:28Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.06 +/- 0.17
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
sambalsquad/loraku
|
sambalsquad
| 2023-05-27T00:48:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-27T00:33:54Z |
---
license: creativeml-openrail-m
---
|
theprocess-21/xlm-roberta-base-finetuned-panx-de
|
theprocess-21
| 2023-05-27T00:34:35Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2023-05-26T23:55:09Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-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: 0.1400
- F1: 0.8609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 | 0.1584 | 0.8233 |
| 0.1252 | 2.0 | 1050 | 0.1384 | 0.8491 |
| 0.0811 | 3.0 | 1575 | 0.1400 | 0.8609 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
minosu/godot_dodo_4x_60k_starcoder_15b_1ep
|
minosu
| 2023-05-27T00:32:26Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-26T20:59:57Z |
# godot_dodo_4x_60k_starcoder_15b_1ep
## Model details
Trained in May 2023.
Godot-Dodo models are instruction-following models finetuned from open-source base models.
Please refer to the README of the [GitHub repository](https://github.com/minosvasilias/godot-dodo) for detailed information.
### Evaluation datasets
The model was evaluated using code instruction prompts. More details in the [GitHub repository](https://github.com/minosvasilias/godot-dodo).
### Training dataset
The model was trained on a 60k rows instruction following dataset, which is released in the [Github repository](https://github.com/minosvasilias/godot-dodo).
### Training parameters
For exact parameters used, please refer to [this page](https://github.com/minosvasilias/godot-dodo/tree/main/models/godot_dodo_4x_60k_starcoder_15b_1ep) in the GitHub repository.
|
aalonso-developer/vit-base-clothing-leafs-example
|
aalonso-developer
| 2023-05-27T00:26:33Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-21T21:39:12Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-clothing-leafs-example
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. -->
# vit-base-clothing-leafs-example
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1420
- Accuracy: 0.0448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 8.6059 | 0.14 | 1000 | 8.5844 | 0.0002 |
| 8.5506 | 0.28 | 2000 | 8.5189 | 0.0010 |
| 8.4931 | 0.41 | 3000 | 8.4641 | 0.0012 |
| 8.4223 | 0.55 | 4000 | 8.3495 | 0.0016 |
| 8.3144 | 0.69 | 5000 | 8.2552 | 0.0021 |
| 8.1936 | 0.83 | 6000 | 8.1385 | 0.0024 |
| 8.0638 | 0.97 | 7000 | 7.9924 | 0.0028 |
| 7.8485 | 1.11 | 8000 | 7.8366 | 0.0036 |
| 7.6933 | 1.24 | 9000 | 7.6595 | 0.0045 |
| 7.5808 | 1.38 | 10000 | 7.5232 | 0.0062 |
| 7.4352 | 1.52 | 11000 | 7.3816 | 0.0070 |
| 7.3279 | 1.66 | 12000 | 7.2853 | 0.0084 |
| 7.2141 | 1.8 | 13000 | 7.1553 | 0.0105 |
| 7.151 | 1.94 | 14000 | 7.0853 | 0.0119 |
| 6.9695 | 2.07 | 15000 | 7.0088 | 0.0134 |
| 6.8563 | 2.21 | 16000 | 6.9409 | 0.0139 |
| 6.8019 | 2.35 | 17000 | 6.8634 | 0.0158 |
| 6.7372 | 2.49 | 18000 | 6.8001 | 0.0175 |
| 6.6903 | 2.63 | 19000 | 6.7323 | 0.0191 |
| 6.6482 | 2.77 | 20000 | 6.6638 | 0.0207 |
| 6.5669 | 2.9 | 21000 | 6.6090 | 0.0239 |
| 6.4484 | 3.04 | 22000 | 6.5441 | 0.0240 |
| 6.2568 | 3.18 | 23000 | 6.5015 | 0.0273 |
| 6.2452 | 3.32 | 24000 | 6.4589 | 0.0304 |
| 6.2002 | 3.46 | 25000 | 6.4312 | 0.0310 |
| 6.1699 | 3.6 | 26000 | 6.3723 | 0.0319 |
| 6.1284 | 3.73 | 27000 | 6.3324 | 0.0343 |
| 6.1186 | 3.87 | 28000 | 6.3029 | 0.0350 |
| 6.0611 | 4.01 | 29000 | 6.2723 | 0.0381 |
| 5.7883 | 4.15 | 30000 | 6.2527 | 0.0383 |
| 5.7684 | 4.29 | 31000 | 6.2186 | 0.0392 |
| 5.7701 | 4.43 | 32000 | 6.2031 | 0.0403 |
| 5.7473 | 4.56 | 33000 | 6.1777 | 0.0430 |
| 5.735 | 4.7 | 34000 | 6.1634 | 0.0442 |
| 5.7324 | 4.84 | 35000 | 6.1494 | 0.0443 |
| 5.6949 | 4.98 | 36000 | 6.1420 | 0.0448 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jackoyoungblood/pegasus-samsum
|
jackoyoungblood
| 2023-05-27T00:23:07Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-25T14:45:35Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7116 | 0.54 | 500 | 1.4856 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
xzuyn/Alpacino-13B-GGML
|
xzuyn
| 2023-05-27T00:01:38Z | 0 | 2 | null |
[
"llama",
"alpaca",
"region:us"
] | null | 2023-05-22T22:34:38Z |
---
tags:
- llama
- alpaca
---
# For use with [KoboldCPP](https://github.com/LostRuins/koboldcpp)
Original Model: https://huggingface.co/digitous/Alpacino13b
|
maharishiva/ppo-LunarLander-v2
|
maharishiva
| 2023-05-26T23:51:01Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T23:50:48Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 280.63 +/- 14.41
name: mean_reward
verified: false
---
# **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
...
```
|
YakovElm/Jira5Classic_256
|
YakovElm
| 2023-05-26T23:47:50Z | 64 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T23:47:14Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira5Classic_256
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Jira5Classic_256
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4111
- Train Accuracy: 0.8090
- Validation Loss: 1.1085
- Validation Accuracy: 0.5237
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5551 | 0.7429 | 0.8002 | 0.4858 | 0 |
| 0.4860 | 0.7712 | 0.7765 | 0.4890 | 1 |
| 0.4111 | 0.8090 | 1.1085 | 0.5237 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
NajiAboo/prognosis-distilbert-base-uncased-finetuned-cardio-qa
|
NajiAboo
| 2023-05-26T23:47:29Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-26T17:23:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: NajiAboo/prognosis-distilbert-base-uncased-finetuned-cardio-qa
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# NajiAboo/prognosis-distilbert-base-uncased-finetuned-cardio-qa
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:
- Train Loss: 0.1314
- Train End Logits Accuracy: 0.9575
- Train Start Logits Accuracy: 0.9591
- Validation Loss: 1.5573
- Validation End Logits Accuracy: 0.7503
- Validation Start Logits Accuracy: 0.7457
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 494400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.4969 | 0.6659 | 0.6647 | 1.0266 | 0.7456 | 0.7428 | 0 |
| 0.8820 | 0.7667 | 0.7702 | 0.9726 | 0.7573 | 0.7542 | 1 |
| 0.6269 | 0.8266 | 0.8287 | 1.0440 | 0.7601 | 0.7528 | 2 |
| 0.4406 | 0.8711 | 0.8748 | 1.0837 | 0.7590 | 0.7540 | 3 |
| 0.2999 | 0.9087 | 0.9122 | 1.1957 | 0.7572 | 0.7510 | 4 |
| 0.2168 | 0.9317 | 0.9347 | 1.4545 | 0.7465 | 0.7428 | 5 |
| 0.1623 | 0.9485 | 0.9501 | 1.4684 | 0.7560 | 0.7529 | 6 |
| 0.1314 | 0.9575 | 0.9591 | 1.5573 | 0.7503 | 0.7457 | 7 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Apache15Classic_32
|
YakovElm
| 2023-05-26T23:27:43Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T23:27:09Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_32
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache15Classic_32
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1627
- Train Accuracy: 0.9533
- Validation Loss: 0.4178
- Validation Accuracy: 0.8924
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1997 | 0.9498 | 0.3502 | 0.8924 | 0 |
| 0.1803 | 0.9542 | 0.3673 | 0.8924 | 1 |
| 0.1627 | 0.9533 | 0.4178 | 0.8924 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Rodrigopiva/my_awesome_model
|
Rodrigopiva
| 2023-05-26T23:09:38Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-15T16:15:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rodrigopiva/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rodrigopiva/my_awesome_model
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:
- Train Loss: 0.1326
- Train Accuracy: 0.9783
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3420, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Epoch |
|:----------:|:--------------:|:-----:|
| 0.3647 | 0.9274 | 0 |
| 0.2203 | 0.9632 | 1 |
| 0.1326 | 0.9783 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Multi-Domain-Expert-Learning/REDPAJAMA-3B-expert-arxiv
|
Multi-Domain-Expert-Learning
| 2023-05-26T22:58:50Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-26T22:38:55Z |
---
tags:
- generated_from_trainer
datasets:
- /pfs/lustrep4/scratch/project_462000259/ajunior/data/arxiv
metrics:
- accuracy
model-index:
- name: layer_9,10,11,12,13
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. -->
# layer_9,10,11,12,13
This model is a fine-tuned version of [/pfs/lustrep4/scratch/project_462000259/ajunior/models/rallio3b](https://huggingface.co//pfs/lustrep4/scratch/project_462000259/ajunior/models/rallio3b) on the /pfs/lustrep4/scratch/project_462000259/ajunior/data/arxiv dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3984
- Accuracy: 0.5176
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5584 | 0.05 | 500 | 2.5312 | 0.4997 |
| 2.3975 | 0.1 | 1000 | 2.3984 | 0.5176 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+gitcb6c422
- Datasets 2.11.0
- Tokenizers 0.12.1
|
mfidabel/a2c-PandaReachDense-v2
|
mfidabel
| 2023-05-26T22:41:15Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:42:57Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.47 +/- 0.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
YakovElm/Apache5Classic_32
|
YakovElm
| 2023-05-26T22:37:57Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T22:37:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache5Classic_32
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache5Classic_32
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2422
- Train Accuracy: 0.9181
- Validation Loss: 0.6553
- Validation Accuracy: 0.8129
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3112 | 0.9049 | 0.4947 | 0.8233 | 0 |
| 0.2848 | 0.9120 | 0.4767 | 0.8233 | 1 |
| 0.2422 | 0.9181 | 0.6553 | 0.8129 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigSalmon/InformalToFormalLincoln100Paraphrase
|
BigSalmon
| 2023-05-26T22:31:47Z | 221 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-26T21:35:02Z |
data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln100Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln100Paraphrase")
```
```
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 (Disclaimer: I highly recommend using this over just generating):
```
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:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
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 (Works pretty decently actually, especially when you use logprobs code from above):
```
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]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
```
```
Leadership | Lecture 17: Worker Morale
What Workers Look for in Companies:
• Benefits
o Tuition reimbursement
o Paid parental leave
o 401K matching
o Profit sharing
o Pension plans
o Free meals
• Social responsibility
o Environmental stewardship
o Charitable contributions
o Diversity
• Work-life balance
o Telecommuting
o Paid holidays and vacation
o Casual dress
• Growth opportunities
• Job security
• Competitive compensation
• Recognition
o Open-door policies
o Whistleblower protection
o Employee-of-the-month awards
o Positive performance reviews
o Bonuses
```
```
description: business
keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification
```
```
3. In this task, you are given a company name and you need to find its industry.
McDonalds -- Restaurant
Facebook -- Social Network
IKEA -- Furniture
American Express -- Credit Services
Nokia -- Telecom
Nintendo -- Entertainment
4. In this task, you are given a Month and you need to convert it to its corresponding season
April -- Spring
December -- Winter
July -- Summer
October -- Fall
February -- Winter
5. In this task, you are given a sentence with a missing word and you need to predict the correct word.
Managers should set an _____ for their employees. -- example
Some people spend more than four _____ in the gym. -- hours
The police were on the _____ of arresting the suspect. -- verge
They were looking for _____ on how to solve the problem. -- guidance
What is the _____ of the coffee? -- price
6. In this task, you are given a paragraph and you need to reorder it to make it logical.
It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters.
It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman.
It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth.
```
```
trivia: What is the population of South Korea?
response: 51 million.
***
trivia: What is the minimum voting age in the US?
response: 18.
***
trivia: What are the first ten amendments of the US constitution called?
response: Bill of Rights.
```
```
ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences
related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions
***
ideas: i know this one guy who retired so young, attesting to how careful they were with money.
related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion
```
```
less specific: actors and musicians should ( support democracy ).
clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ).
***
less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ).
clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ).
```
```
dull: clean
emotional heft: spotless, immaculate, pristine
***
dull: hot
emotional heft: scorching, searing, blistering
***
dull: happy
emotional heft: euphoric
```
```
text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ).
```
```
system: i'm not sure i agree with your assessment of the situation.
user: replace "assessment" with a more casual term.
chatbot: ( take / read / view ).
system: i've been feeling really busy lately, trying to balance work and family responsibilities.
user: replace "busy" with a more intense word that conveys it is too much.
chatbot: overwhelmed.
system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected.
user: replace "nebulous" with a word that conveys a lack of detail.
chatbot: ( vague / ambiguous ).
```
```
user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression]
chatbot: intellectual horizons.
user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”?
chatbot: academic enlightenment.
```
```
key: calculate.
syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute.
ant: guess, conjecture, hit, chance, risk, stake, miscalculate.
```
```
description: more forceful version of curious that is less forceful than nosy
answer: inquisitive
description: more forceful version of hopeful that is less forceful than overconfident
answer: optimistic
```
```
key: inquisitive
positive: curious, interested
negative: nosy, prying
***
key: witty
positive: clever, humorous
negative: sarcastic, caustic
***
key: influential
positive: impactful, powerful
negative: overbearing, domineering
```
|
goodacheez/q-Taxi-v3
|
goodacheez
| 2023-05-26T21:54:46Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:52:58Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="goodacheez/q-Taxi-v3", 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"])
```
|
optimopium/ppo-LunarLander-v2
|
optimopium
| 2023-05-26T21:46:41Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:46:20Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.30 +/- 18.57
name: mean_reward
verified: false
---
# **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
...
```
|
goodacheez/q-FrozenLake-v1-4x4-noSlippery
|
goodacheez
| 2023-05-26T21:43:42Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:43:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="goodacheez/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"])
```
|
orepin/Reinforce-Pixelcopter-35000iter
|
orepin
| 2023-05-26T21:31:39Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:31:34Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-35000iter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 30.80 +/- 19.19
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mfidabel/a2c-AntBulletEnv-v0
|
mfidabel
| 2023-05-26T21:10:48Z | 5 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T21:10:05Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1058.68 +/- 401.07
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
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
...
```
|
LarryAIDraw/Takamine_Takane
|
LarryAIDraw
| 2023-05-26T21:02:20Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-26T20:52:03Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/74350/takamine-takane-haite-kudasai-takamine-san
|
LarryAIDraw/honjo_nia_v1
|
LarryAIDraw
| 2023-05-26T21:01:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-26T20:50:44Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/74261/honjou-nia-date-a-live
|
JCTN/pygmalion-13b-4bit-128g
|
JCTN
| 2023-05-26T20:57:57Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-26T19:58:05Z |
---
language: en
license: other
commercial: no
inference: false
---
# pygmalion-13b-4bit-128g
## Model description
**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.**
Quantized from the decoded pygmalion-13b xor format.
**https://huggingface.co/PygmalionAI/pygmalion-13b**
In safetensor format.
### Quantization Information
GPTQ CUDA quantized with: https://github.com/0cc4m/GPTQ-for-LLaMa
```
python llama.py --wbits 4 models/pygmalion-13b c4 --true-sequential --groupsize 128 --save_safetensors models/pygmalion-13b/4bit-128g.safetensors
```
|
tmilushev/Reinforce-U8-LunarLander-v2
|
tmilushev
| 2023-05-26T20:42:49Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T20:21:28Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -182.44 +/- 110.69
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
joshmazen/ppo-LunarLander-v2
|
joshmazen
| 2023-05-26T20:28:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T20:28:03Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.88 +/- 42.31
name: mean_reward
verified: false
---
# **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
...
```
|
sagnikrayc/bert-large-cased-fever
|
sagnikrayc
| 2023-05-26T20:14:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T20:01:44Z |
---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: eval/f1 0.87196
wandb: eval/loss 0.73371
wandb: eval/p 0.87077
wandb: eval/r 0.8753
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens.
|
sagnikrayc/roberta-large-fever
|
sagnikrayc
| 2023-05-26T20:12:44Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:copenlu/fever_gold_evidence",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T19:52:33Z |
---
license: afl-3.0
datasets:
- copenlu/fever_gold_evidence
language:
- en
metrics:
- precision
- recall
- f1
---
```
wandb: eval/f1 0.88556
wandb: eval/loss 0.62762
wandb: eval/p 0.88384
wandb: eval/r 0.8891
```
**Note**:
1. `[evidence_text][SEP][claim]`
2. Only trained/validated on instances length <= 512 tokens.
|
laurenmit/pegasus-project_7_V2
|
laurenmit
| 2023-05-26T20:05:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-26T19:49:52Z |
---
tags:
- generated_from_trainer
model-index:
- name: pegasus-project_7_V2
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. -->
# pegasus-project_7_V2
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Ziyu23/poca-SoccerTwos
|
Ziyu23
| 2023-05-26T19:41:27Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-26T19:41:19Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** 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-SoccerTwos
2. Step 1: Write your model_id: Ziyu23/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
minosu/godot_dodo_4x_60k_starcoder_15b_3ep
|
minosu
| 2023-05-26T19:34:02Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-22T12:10:20Z |
# godot_dodo_4x_60k_starcoder_15b_3ep
## Model details
Trained in May 2023.
Godot-Dodo models are instruction-following models finetuned from open-source base models.
Please refer to the README of the [GitHub repository](https://github.com/minosvasilias/godot-dodo) for detailed information.
### Evaluation datasets
The model was evaluated using code instruction prompts. More details in the [GitHub repository](https://github.com/minosvasilias/godot-dodo).
### Training dataset
The model was trained on a 60k rows instruction following dataset, which is released in the [Github repository](https://github.com/minosvasilias/godot-dodo).
### Training parameters
For exact parameters used, please refer to [this page](https://github.com/minosvasilias/godot-dodo/tree/main/models/godot_dodo_4x_60k_starcoder_15b_3ep) in the GitHub repository.
|
orepin/Reinforce-Pixelcopter-20000iter
|
orepin
| 2023-05-26T19:34:01Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T19:33:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-20000iter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 39.70 +/- 49.64
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ambientocclusion/foodmanpaul-lora-1.5
|
ambientocclusion
| 2023-05-26T19:26:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-26T19:21:37Z |
The foodman paul model we've all been waiting for
Use "_foodmanpaul_" to summon the foodman
|
Zeta611/easyword-model-peft-distilled-1.3B
|
Zeta611
| 2023-05-26T19:22:35Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-05-26T18:40:19Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: easyword-model-peft-distilled-1.3B
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. -->
# easyword-model-peft-distilled-1.3B
This model is a fine-tuned version of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1607
- Bleu: 0.0
- Gen Len: 5.9876
## 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: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 31 | 4.9650 | 0.0 | 9.3975 |
| No log | 2.0 | 62 | 4.4353 | 0.1522 | 8.6957 |
| No log | 3.0 | 93 | 3.8967 | 1.2792 | 6.8137 |
| No log | 4.0 | 124 | 3.6053 | 2.6004 | 6.0062 |
| No log | 5.0 | 155 | 3.5239 | 2.9339 | 5.8571 |
| No log | 6.0 | 186 | 3.4692 | 2.6031 | 5.8261 |
| No log | 7.0 | 217 | 3.4244 | 2.6536 | 5.795 |
| No log | 8.0 | 248 | 3.3865 | 2.6445 | 5.8509 |
| No log | 9.0 | 279 | 3.3555 | 2.5482 | 5.9193 |
| No log | 10.0 | 310 | 3.3325 | 3.087 | 5.913 |
| No log | 11.0 | 341 | 3.3141 | 3.3511 | 5.9006 |
| No log | 12.0 | 372 | 3.2986 | 3.3511 | 5.8944 |
| No log | 13.0 | 403 | 3.2871 | 3.9871 | 5.8758 |
| No log | 14.0 | 434 | 3.2787 | 3.3083 | 5.882 |
| No log | 15.0 | 465 | 3.2738 | 3.3083 | 5.882 |
| No log | 16.0 | 496 | 3.2720 | 3.3083 | 5.882 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
orepin/Reinforce-Pixelcopter-15000iter
|
orepin
| 2023-05-26T19:03:26Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T19:03:20Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-15000iter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 39.10 +/- 34.44
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Monero/WizardLM-OpenAssistant-30b-Uncensored-4bit
|
Monero
| 2023-05-26T18:58:48Z | 10 | 7 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-26T06:15:01Z |
WizardLM 30b Native merged 50%/50% with Open Assistant 30b Native
# Use:
Add ```### Certainly!``` to the end of your prompt for uncensored output
|
cotran2/my_awesome_qa_model
|
cotran2
| 2023-05-26T18:32:01Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-17T16:40:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_model
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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6628
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3128 |
| 2.7075 | 2.0 | 500 | 1.7293 |
| 2.7075 | 3.0 | 750 | 1.6628 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
darthrevenge/a2c-PandaReachDense-v2
|
darthrevenge
| 2023-05-26T18:21:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T18:18:59Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.54 +/- 0.62
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
CodyKilpatrick/q-taxi-v3
|
CodyKilpatrick
| 2023-05-26T18:07:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T18:07:02Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.75
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="CodyKilpatrick/q-taxi-v3", 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"])
```
|
jobeid1/q-FrozenLake-v1-4x4-noSlippery
|
jobeid1
| 2023-05-26T18:06:55Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T18:06:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jobeid1/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"])
```
|
CodyKilpatrick/q-FrozenLake-v1-4x4-noSlippery
|
CodyKilpatrick
| 2023-05-26T18:04:32Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T18:04:29Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="CodyKilpatrick/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"])
```
|
orepin/Reinforce-Pixelcopter-5000iter
|
orepin
| 2023-05-26T18:04:28Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T18:04:25Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-5000iter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.10 +/- 27.97
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
YakovElm/IntelDAOS5Classic_256
|
YakovElm
| 2023-05-26T17:28:21Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T17:27:43Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS5Classic_256
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# IntelDAOS5Classic_256
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3729
- Train Accuracy: 0.8740
- Validation Loss: 0.4307
- Validation Accuracy: 0.8438
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4026 | 0.8740 | 0.4333 | 0.8438 | 0 |
| 0.3844 | 0.8740 | 0.4434 | 0.8438 | 1 |
| 0.3729 | 0.8740 | 0.4307 | 0.8438 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Apache15Classic_512
|
YakovElm
| 2023-05-26T17:04:32Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T17:03:55Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Apache15Classic_512
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Apache15Classic_512
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1701
- Train Accuracy: 0.9542
- Validation Loss: 0.3117
- Validation Accuracy: 0.8924
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1982 | 0.9540 | 0.3463 | 0.8924 | 0 |
| 0.1791 | 0.9542 | 0.3394 | 0.8924 | 1 |
| 0.1701 | 0.9542 | 0.3117 | 0.8924 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nixtasy/diaster_distilbert_base_uncased
|
nixtasy
| 2023-05-26T17:02:42Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T16:46:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: diaster_distilbert_base_uncased
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. -->
# diaster_distilbert_base_uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0345
- Accuracy: 0.8076
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 381 | 0.3926 | 0.8372 |
| 0.4214 | 2.0 | 762 | 0.4764 | 0.8234 |
| 0.3014 | 3.0 | 1143 | 0.4208 | 0.8352 |
| 0.2051 | 4.0 | 1524 | 0.5139 | 0.8280 |
| 0.2051 | 5.0 | 1905 | 0.8480 | 0.7840 |
| 0.1424 | 6.0 | 2286 | 0.8045 | 0.8155 |
| 0.1042 | 7.0 | 2667 | 0.9295 | 0.8188 |
| 0.075 | 8.0 | 3048 | 0.9241 | 0.8142 |
| 0.075 | 9.0 | 3429 | 1.0063 | 0.8083 |
| 0.0614 | 10.0 | 3810 | 1.0345 | 0.8076 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
raghvendramall/esm2_t6_8M_UR50D-localization-mc-finetuned-localization
|
raghvendramall
| 2023-05-26T17:00:35Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"esm",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T12:40:30Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: esm2_t6_8M_UR50D-localization-mc-finetuned-localization
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. -->
# esm2_t6_8M_UR50D-localization-mc-finetuned-localization
This model is a fine-tuned version of [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5055
- F1: 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9359 | 1.0 | 1411 | 0.8535 | 0.7208 |
| 0.7693 | 2.0 | 2822 | 0.7625 | 0.7293 |
| 0.619 | 3.0 | 4233 | 0.7689 | 0.7555 |
| 0.4961 | 4.0 | 5644 | 0.8251 | 0.7592 |
| 0.3477 | 5.0 | 7055 | 0.9720 | 0.7717 |
| 0.219 | 6.0 | 8466 | 1.0984 | 0.7800 |
| 0.1293 | 7.0 | 9877 | 1.2810 | 0.7781 |
| 0.068 | 8.0 | 11288 | 1.4495 | 0.7752 |
| 0.0318 | 9.0 | 12699 | 1.5142 | 0.7771 |
| 0.0178 | 10.0 | 14110 | 1.5055 | 0.7776 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rahular/varta-bert
|
rahular
| 2023-05-26T16:29:46Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"as",
"bh",
"bn",
"en",
"gu",
"hi",
"kn",
"ml",
"mr",
"ne",
"or",
"pa",
"ta",
"te",
"ur",
"dataset:rahular/varta",
"arxiv:2305.05858",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-12-11T17:11:16Z |
---
license: apache-2.0
datasets:
- rahular/varta
language:
- as
- bh
- bn
- en
- gu
- hi
- kn
- ml
- mr
- ne
- or
- pa
- ta
- te
- ur
---
# Varta-BERT
<!-- Provide a quick summary of what the model is/does. -->
### Model Description
<!-- Provide a longer summary of what this model is. -->
Varta-BERT is a model pre-trained on the `full` training set of [Varta](https://huggingface.co/datasets/rahular/varta) in 14 Indic languages (Assamese, Bhojpuri, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu) and English, using a masked language modeling (MLM) objective.
[Varta](https://huggingface.co/datasets/rahular/varta) is a large-scale news corpus for Indic languages, including 41.8 million news articles in 14 different Indic languages (and English), which come from a variety of high-quality sources.
The dataset and the model are introduced in [this paper](https://arxiv.org/abs/2305.05858). The code is released in [this repository](https://github.com/rahular/varta).
## Uses
You can use the raw model for masked language modeling, but it is mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at our [Varta-T5](https://huggingface.co/rahular/varta-t5) model.
## Bias, Risks, and Limitations
This work is mainly dedicated to the curation of a new multilingual dataset for Indic languages, many of which are low-resource languages. During data collection, we face several limitations that can potentially result in ethical concerns. Some of the important ones are mentioned below: <br>
- Our dataset contains only those articles written by DailyHunt's partner publishers. This has the potential to result in a bias towards a particular narrative or ideology that can affect the representativeness and diversity of the dataset.
- Another limitation is the languages represented in Varta. Out of 22 languages with official status in India, our dataset has only 13. There are 122 major languages spoken by at least 10,000 people and 159 other languages which are extremely low-resourced. None of these languages are represented in our dataset.
- We do not perform any kind of debiasing on Varta. This means that societal and cultural biases may exist in the dataset, which can adversely affect the fairness and inclusivity of the models trained on it.
## How to Get Started with the Model
You can use this model directly for masked language modeling.
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("rahular/varta-bert")
model = AutoModelForMaskedLM.from_pretrained("rahular/varta-bert")
```
## Training Details
### Training Data
Varta contains 41.8 million high-quality news articles in 14 Indic languages and English.
With 34.5 million non-English article-headline pairs, it is the largest document-level dataset of its kind.
### Pretraining
- We pretrain the Varta-BERT model using the standard BERT-Base architecture with 12 encoder layers.
- We train with a maximum sequence length of 512 tokens with an embedding dimension of 768.
- We use 12 attention heads with feed-forward width of 3072.
- To support all the 15 languages in dataset we use a wordpiece vocabulary of size 128K.
- In total, the model has 184M parameters. The model is trained with AdamW optimizer with alpha=0.9 and beta=0.98.
- We use an initial learning rate of 1e-4 with a warm-up of 10K steps and linearly decay the learning rate till the end of training.
- We train the model for a total of 1M steps which takes 10 days to finish.
- We use an effective batch size of 4096 and train the model on TPU v3-128 chips.
Since data sizes across languages in Varta vary from 1.5K (Bhojpuri) to 14.4M articles (Hindi), we use standard temperature-based sampling to upsample data when necessary.
### Evaluation Results
Please see [the paper](https://arxiv.org/pdf/2305.05858.pdf).
## Citation
```
@misc{aralikatte2023varta,
title={V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages},
author={Rahul Aralikatte and Ziling Cheng and Sumanth Doddapaneni and Jackie Chi Kit Cheung},
year={2023},
eprint={2305.05858},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
hosst/FridgeLLM
|
hosst
| 2023-05-26T16:15:55Z | 0 | 1 |
adapter-transformers
|
[
"adapter-transformers",
"technicians",
"appliances",
"manufacturers",
"troubleshooting",
"conversational",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-05-26T15:44:41Z |
---
license: apache-2.0
library_name: adapter-transformers
pipeline_tag: conversational
tags:
- technicians
- appliances
- manufacturers
- troubleshooting
datasets:
- OpenAssistant/oasst1
---
|
Benned/Milkbreast
|
Benned
| 2023-05-26T16:12:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-26T16:10:27Z |
---
license: creativeml-openrail-m
---
|
alendra1945/tes-alpaca-lora7b
|
alendra1945
| 2023-05-26T16:03:18Z | 0 | 0 | null |
[
"dataset:yahma/alpaca-cleaned",
"license:mit",
"region:us"
] | null | 2023-05-20T20:08:34Z |
---
license: mit
datasets:
- yahma/alpaca-cleaned
---
|
Evuv/bart_base
|
Evuv
| 2023-05-26T15:55:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-19T10:07:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart_base
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. -->
# bart_base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5401
- Rouge1: 0.2403
- Rouge2: 0.0714
- Rougel: 0.1924
- Rougelsum: 0.1922
- Gen Len: 18.1163
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 99 | 2.5104 | 0.189 | 0.053 | 0.154 | 0.1535 | 13.3023 |
| No log | 2.0 | 198 | 2.4941 | 0.2498 | 0.0781 | 0.1972 | 0.197 | 18.2326 |
| No log | 3.0 | 297 | 2.5144 | 0.2394 | 0.0652 | 0.1914 | 0.192 | 18.4419 |
| No log | 4.0 | 396 | 2.5401 | 0.2403 | 0.0714 | 0.1924 | 0.1922 | 18.1163 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BlazingFringe/mpnet-mnr-v2-fine-tuned
|
BlazingFringe
| 2023-05-26T15:49:56Z | 3 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:anli",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-05-23T11:43:43Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- anli
- glue
---
# Mpnet-v2-MNR-Fine-Tuned
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('BlazingFringe/mpnet-mnr-v2-fine-tuned')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BlazingFringe/mpnet-mnr-v2-fine-tuned')
model = AutoModel.from_pretrained('BlazingFringe/mpnet-mnr-v2-fine-tuned')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1009 with parameters:
```
{'batch_size': 32}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
GGunjan/ppo-LunarLander
|
GGunjan
| 2023-05-26T15:43:29Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T15:43:14Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 289.05 +/- 14.24
name: mean_reward
verified: false
---
# **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
...
```
|
ShayDuane/distilbert-base-uncased_emotion_ft_0526
|
ShayDuane
| 2023-05-26T15:27:38Z | 107 | 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
| 2023-05-26T14:57:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0526
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9375
- name: F1
type: f1
value: 0.937552703246777
- name: Precision
type: precision
value: 0.9169515578018389
---
<!-- 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_emotion_ft_0526
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: 0.2275
- Accuracy: 0.9375
- F1: 0.9376
- Precision: 0.9170
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.2131 | 1.0 | 2000 | 0.2301 | 0.93 | 0.9305 | 0.9008 |
| 0.1881 | 2.0 | 4000 | 0.1854 | 0.9385 | 0.9388 | 0.9080 |
| 0.1012 | 3.0 | 6000 | 0.2200 | 0.935 | 0.9353 | 0.9066 |
| 0.0642 | 4.0 | 8000 | 0.2275 | 0.9375 | 0.9376 | 0.9170 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
temnoed/Dandelions
|
temnoed
| 2023-05-26T15:12:51Z | 0 | 0 | null |
[
"en",
"ru",
"dataset:temnoed/Dandelions",
"license:openrail",
"region:us"
] | null | 2023-05-25T21:44:46Z |
---
license: openrail
datasets:
- temnoed/Dandelions
language:
- en
- ru
---
|
jonastokoliu/token_classification_finetune
|
jonastokoliu
| 2023-05-26T15:08:52Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-26T15:03:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_classification_finetune
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5759878419452887
- name: Recall
type: recall
value: 0.35125115848007415
- name: F1
type: f1
value: 0.436384571099597
- name: Accuracy
type: accuracy
value: 0.9444206926036768
---
<!-- 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. -->
# token_classification_finetune
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2489
- Precision: 0.5760
- Recall: 0.3513
- F1: 0.4364
- Accuracy: 0.9444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 107 | 0.2573 | 0.6011 | 0.3003 | 0.4005 | 0.9409 |
| No log | 2.0 | 214 | 0.2489 | 0.5760 | 0.3513 | 0.4364 | 0.9444 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Mizuiro-sakura/deberta-v2-base-juman-finetuned-commonsenseqa
|
Mizuiro-sakura
| 2023-05-26T15:07:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"deberta",
"commonsenseqa",
"commonsense_qa",
"commonsense-qa",
"CommonsenseQA",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-02-03T04:49:19Z |
---
license: mit
language: ja
library_name: transformers
tags:
- pytorch
- deberta
- deberta-v2
- commonsenseqa
- commonsense_qa
- commonsense-qa
- CommonsenseQA
datasets:
- wikipedia
- cc100
- oscar
metrics:
- accuracy
---
# このモデルはdeberta-v2-base-japaneseをファインチューニングしてCommonsenseQA(選択式の質問)に用いれるようにしたものです。
このモデルはdeberta-v2-base-japaneseをyahoo japan/JGLUEのJCommonsenseQA( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。
形態素解析のためにJumanを用いるバージョンです。
このモデルを利用する際はJumanをインストールしてください。
JUMANのインストール方法は（ https://qiita.com/Helmet/items/b76ae8abc47186e24401 ）を参考にしてください。
# This model is fine-tuned model for CommonsenseQA which is based on deberta-v2-base-japanese
This model is fine-tuned by using JGLUE/JCommonsenseQA dataset.
You could use this model for CommonsenseQA tasks.
You need to install Juman.
So, please check out this site ( https://qiita.com/Helmet/items/b76ae8abc47186e24401 ) to install Juman
# How to use 使い方
transformersおよびpytorch、knp、pyknp、Juman、textspanをインストールしてください。
以下のコードを実行することで、CommonsenseQAタスクを解かせることができます。 please execute this code.
```python
from transformers import AutoModelForMultipleChoice
import torch
import json
import numpy as np
# 初回はこちらを実行してください
#model=AutoModelForMultipleChoice.from_pretrained('Mizuiro-sakura/deberta-v2-base-juman-finetuned-commonsenseqa')
# 二回目以降はこちらを実行してください
# modelフォルダをダウンロードしたパスを入力してください。defaultだとC:\Users\[ユーザー名]\.cache\huggingface\hubにあります。
model=AutoModelForMultipleChoice.from_pretrained('C:\\Users\\.cache\\huggingface\\hub\\models--Mizuiro-sakura--deberta-v2-base-juman-finetuned-commonsenseqa')
from transformers import DebertaV2TokenizerFast
tkz=DebertaV2TokenizerFast.from_pretrained("Mizuiro-sakura/deberta-v2-base-juman-finetuned-commonsenseqa")
tkz.__class__.__name__="JumanppDebertaV2TokenizerFast"
tkz.init_kwargs["auto_map"]={"AutoTokenizer":[None,"tokenizer.JumanppDebertaV2TokenizerFast"]}
tkz.save_pretrained("Mizuiro-sakura/deberta-v2-base-juman-finetuned-commonsenseqa")
from transformers.models.bert_japanese.tokenization_bert_japanese import JumanppTokenizer
class JumanppPreTokenizer(JumanppTokenizer):
def jumanpp_split(self,i,normalized_string):
import textspan
t=str(normalized_string)
k=self.tokenize(t)
return [normalized_string[s:e] for c in textspan.get_original_spans(k,t) for s,e in c]
def pre_tokenize(self,pretok):
pretok.split(self.jumanpp_split)
class JumanppDebertaV2TokenizerFast(DebertaV2TokenizerFast):
def __init__(self,**kwargs):
from tokenizers.pre_tokenizers import PreTokenizer,Metaspace,Sequence
super().__init__(**kwargs)
self._tokenizer.pre_tokenizer=Sequence([PreTokenizer.custom(JumanppPreTokenizer()),Metaspace()])
def save_pretrained(self,save_directory,**kwargs):
import os
import shutil
from tokenizers.pre_tokenizers import PreTokenizer,Metaspace,Sequence
self._auto_map={"AutoTokenizer":[None,"tokenizer.JumanppDebertaV2TokenizerFast"]}
self._tokenizer.pre_tokenizer=Metaspace()
super().save_pretrained(save_directory,**kwargs)
self._tokenizer.pre_tokenizer=Sequence([PreTokenizer.custom(JumanppPreTokenizer()),Metaspace()])
shutil.copy(os.path.abspath(__file__),os.path.join(save_directory,"tokenizer.py"))
question ="主に子ども向けのもので、イラストのついた物語が書かれているものはどれ？"
choice1 = "世界"
choice2 = "写真集"
choice3 = "絵本"
choice4 = "論文"
choice5 = "図鑑"
x1=tkz([question,question,question,question,question],[choice1,choice2,choice3,choice4,choice5],
max_length=64, truncation=True, padding=True)
leng=len(x1['input_ids'][0])
leng2=len(x1['attention_mask'][0])
# モデルに入力するための前処理
X1 = np.empty(shape=(1, 5, leng))
X2 = np.empty(shape=(1, 5, leng))
X1[0, :, :] = x1['input_ids']
X2[0, :, :] = x1['attention_mask']
# モデルにトークンを入力し、最も確率が高い選択肢を抽出する
results = model(torch.tensor(X1).to(torch.int64),torch.tensor(X2).to(torch.int64))
print(torch.argmax(results.logits)+1)
```
# モデルの精度 accuracy of model
eval_accuracy = 86.51 (日本語baseモデルとしては最高の精度)
eval_loss = 0.5917
（参考 BERT : 72.0, XLM RoBERTa base : 68.7, LUKE : 80.0)
# deberta-v2-base-japaneseとは？
日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。
京都大学黒橋研究室が公表されました。
# Model description
This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
# Acknowledgments 謝辞
モデルを公開してくださった京都大学黒橋研究室には感謝いたします。
またコードを作成するにあたり、KoichiYasuokaさんの日記( https://srad.jp/~yasuoka/journal/659881/ )を参考にさせて頂きました。
深く感謝いたします。
I would like to thank Kurohashi Lab at Kyoto University.
And I would like to thank KoichiYasuoka.
|
dian34323/marsha
|
dian34323
| 2023-05-26T15:06:05Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-26T15:04:32Z |
---
license: creativeml-openrail-m
---
|
Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa
|
Mizuiro-sakura
| 2023-05-26T15:05:18Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"deberta",
"commonsenseqa",
"commonsense_qa",
"commonsense-qa",
"CommonsenseQA",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-02-01T01:02:44Z |
---
license: mit
language: ja
library_name: transformers
tags:
- pytorch
- deberta
- deberta-v2
- commonsenseqa
- commonsense_qa
- commonsense-qa
- CommonsenseQA
datasets:
- wikipedia
- cc100
- oscar
metrics:
- accuracy
---
# このモデルはdeberta-v2-base-japaneseをファインチューニングしてCommonsenseQA(選択式の質問)に用いれるようにしたものです。
このモデルはdeberta-v2-base-japaneseをyahoo japan/JGLUEのJCommonsenseQA( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。
# This model is fine-tuned model for CommonsenseQA which is based on deberta-v2-base-japanese
This model is fine-tuned by using JGLUE/JCommonsenseQA dataset.
You could use this model for CommonsenseQA tasks.
# How to use 使い方
transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。
以下のコードを実行することで、CommonsenseQAタスクを解かせることができます。 please execute this code.
```python
from transformers import AutoTokenizer, AutoModelForMultipleChoice
import torch
import numpy as np
# modelのロード
tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa')
model = AutoModelForMultipleChoice.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-base-finetuned-commonsenseqa')
# 質問と選択肢の代入
question = '電子機器で使用される最も主要な電子回路基板の事をなんと言う？'
choice1 = '掲示板'
choice2 = 'パソコン'
choice3 = 'マザーボード'
choice4 = 'ハードディスク'
choice5 = 'まな板'
# トークン化（エンコーディング・形態素解析）する
token = tokenizer([question,question,question,question,question],[choice1,choice2,choice3,choice4,choice5],return_tensors='pt',padding=True)
leng=len(token['input_ids'][0])
# modelに入力するための下準備
X1 = np.empty(shape=(1, 5, leng))
X2 = np.empty(shape=(1, 5, leng))
X1[0, :, :] = token['input_ids']
X2[0, :, :] = token['attention_mask']
# modelにトークンを入力する
results = model(torch.tensor(X1).to(torch.int64),torch.tensor(X2).to(torch.int64))
# 最も高い値のインデックスを取得する
max_result=torch.argmax(results.logits)
print(max_result+1)
```
# モデルの精度 accuracy of model
79.80339588918764
（参考 BERT : 72.0, XLM RoBERTa base : 68.7, LUKE : 80.0)
# deberta-v2-base-japaneseとは？
日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。
京都大学黒橋研究室が公表されました。
# Model description
This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
# Acknowledgments 謝辞
モデルを公開してくださった京都大学黒橋研究室には感謝いたします。
I would like to thank Kurohashi Lab at Kyoto University.
|
Mizuiro-sakura/deberta-v2-japanese-tiny-finetuned-commonsenseqa
|
Mizuiro-sakura
| 2023-05-26T15:01:57Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"multiple-choice",
"deberta",
"commonsenseqa",
"commonsense_qa",
"commonsense-qa",
"CommonsenseQA",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:oscar",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-05-11T10:28:33Z |
---
license: mit
language: ja
library_name: transformers
tags:
- pytorch
- deberta
- deberta-v2
- commonsenseqa
- commonsense_qa
- commonsense-qa
- CommonsenseQA
datasets:
- wikipedia
- cc100
- oscar
metrics:
- accuracy
---
# このモデルはdeberta-v2-tiny-japaneseをファインチューニングしてCommonsenseQA(選択式の質問)に用いれるようにしたものです。
このモデルはdeberta-v2-tiny-japaneseをyahoo japan/JGLUEのJCommonsenseQA( https://github.com/yahoojapan/JGLUE ) を用いてファインチューニングしたものです。
# This model is fine-tuned model for CommonsenseQA which is based on deberta-v2-tiny-japanese
This model is fine-tuned by using JGLUE/JCommonsenseQA dataset.
You could use this model for CommonsenseQA tasks.
# How to use 使い方
transformersおよびpytorchをインストールしてください。
以下のコードを実行することで、CommonsenseQAタスクを解かせることができます。 please execute this code.
```python
from transformers import AutoTokenizer, AutoModelForMultipleChoice
import torch
import numpy as np
# modelのロード
tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-tiny-finetuned-commonsenseqa')
model = AutoModelForMultipleChoice.from_pretrained('Mizuiro-sakura/deberta-v2-japanese-tiny-finetuned-commonsenseqa')
# 質問と選択肢の代入
question = '電子機器で使用される最も主要な電子回路基板の事をなんと言う？'
choice1 = '掲示板'
choice2 = 'パソコン'
choice3 = 'マザーボード'
choice4 = 'ハードディスク'
choice5 = 'まな板'
# トークン化（エンコーディング・形態素解析）する
token = tokenizer([question,question,question,question,question],[choice1,choice2,choice3,choice4,choice5],return_tensors='pt',padding=True)
leng=len(token['input_ids'][0])
# modelに入力するための下準備
X1 = np.empty(shape=(1, 5, leng))
X2 = np.empty(shape=(1, 5, leng))
X1[0, :, :] = token['input_ids']
X2[0, :, :] = token['attention_mask']
# modelにトークンを入力する
results = model(torch.tensor(X1).to(torch.int64),torch.tensor(X2).to(torch.int64))
# 最も高い値のインデックスを取得する
max_result=torch.argmax(results.logits)
print(max_result+1)
```
# モデルの精度 accuracy of model
51.1
（参考 BERT : 72.0, XLM RoBERTa base : 68.7, LUKE : 80.0)
# deberta-v2-base-japaneseとは？
日本語Wikipedeia（3.2GB）および、cc100(85GB)、oscar(54GB)を用いて訓練されたモデルです。
京都大学黒橋研究室が公表されました。
# Model description
This is a Japanese DeBERTa V2 base model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
# Acknowledgments 謝辞
モデルを公開してくださった京都大学黒橋研究室には感謝いたします。
I would like to thank Kurohashi Lab at Kyoto University.
|
hny17/finetune_ner
|
hny17
| 2023-05-26T14:58:53Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-08T07:37:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetune_ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune_ner
This model is a fine-tuned version of [deprem-ml/deprem-ner](https://huggingface.co/deprem-ml/deprem-ner) on a custom private dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2600
- Precision: 0.6071
- Recall: 0.68
- F1: 0.6415
- Accuracy: 0.9055
## Training procedure
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 2 | 0.3447 | 0.4828 | 0.56 | 0.5185 | 0.8740 |
| No log | 2.0 | 4 | 0.3079 | 0.4688 | 0.6 | 0.5263 | 0.9055 |
| No log | 3.0 | 6 | 0.2849 | 0.5312 | 0.68 | 0.5965 | 0.9055 |
| No log | 4.0 | 8 | 0.2796 | 0.5484 | 0.68 | 0.6071 | 0.8976 |
| No log | 5.0 | 10 | 0.2741 | 0.6071 | 0.68 | 0.6415 | 0.9055 |
| No log | 6.0 | 12 | 0.2705 | 0.6071 | 0.68 | 0.6415 | 0.9055 |
| No log | 7.0 | 14 | 0.2685 | 0.5862 | 0.68 | 0.6296 | 0.9055 |
| No log | 8.0 | 16 | 0.2636 | 0.6071 | 0.68 | 0.6415 | 0.9055 |
| No log | 9.0 | 18 | 0.2611 | 0.6071 | 0.68 | 0.6415 | 0.9055 |
| No log | 10.0 | 20 | 0.2600 | 0.6071 | 0.68 | 0.6415 | 0.9055 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tmilushev/Reinforce-Pixelcopter-PLE-v1
|
tmilushev
| 2023-05-26T14:45:27Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T13:49:28Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 27.40 +/- 34.49
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
mfaiq2307/faiq-wav2vec2-large-xlsr-indo-demo-a100-runpod-wandb
|
mfaiq2307
| 2023-05-26T14:34:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-26T12:26:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: faiq-wav2vec2-large-xlsr-indo-demo-a100-runpod-wandb
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 0.4316088210663441
---
<!-- 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. -->
# faiq-wav2vec2-large-xlsr-indo-demo-a100-runpod-wandb
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Wer: 0.4316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1582 | 2.92 | 400 | 2.8179 | 1.0 |
| 1.529 | 5.84 | 800 | 0.4891 | 0.6339 |
| 0.4003 | 8.76 | 1200 | 0.4013 | 0.5365 |
| 0.2715 | 11.68 | 1600 | 0.3853 | 0.4893 |
| 0.2153 | 14.6 | 2000 | 0.3898 | 0.4738 |
| 0.1704 | 17.52 | 2400 | 0.4153 | 0.4593 |
| 0.1545 | 20.44 | 2800 | 0.4112 | 0.4515 |
| 0.1317 | 23.36 | 3200 | 0.3920 | 0.4460 |
| 0.1199 | 26.28 | 3600 | 0.4068 | 0.4347 |
| 0.1073 | 29.2 | 4000 | 0.4018 | 0.4316 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.3
|
wakakaka/first-lunar-landing
|
wakakaka
| 2023-05-26T14:29:55Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T14:29:32Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.68 +/- 19.52
name: mean_reward
verified: false
---
# **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
...
```
|
sambalsquad/xxxxx
|
sambalsquad
| 2023-05-26T14:28:17Z | 0 | 0 | null |
[
"safetensors",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-26T14:26:32Z |
---
license: creativeml-openrail-m
---
|
jeysshon/Pajaros-PIB-parcial69
|
jeysshon
| 2023-05-26T14:27:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-26T14:20:37Z |
---
title: Bird Classification
emoji: ⚡
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 3.14.0
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
AustinCarthy/Onlyphish_10K_fromB_BFall_40KGen_topP_0.75_noaddedB
|
AustinCarthy
| 2023-05-26T14:22:32Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-25T23:18:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Onlyphish_10K_fromB_BFall_40KGen_topP_0.75_noaddedB
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. -->
# Onlyphish_10K_fromB_BFall_40KGen_topP_0.75_noaddedB
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0500
- Accuracy: 0.9943
- F1: 0.9371
- Precision: 0.9955
- Recall: 0.8852
- Roc Auc Score: 0.9425
- Tpr At Fpr 0.01: 0.8404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0145 | 1.0 | 7813 | 0.0237 | 0.9946 | 0.9415 | 0.9760 | 0.9094 | 0.9541 | 0.8006 |
| 0.007 | 2.0 | 15626 | 0.0356 | 0.9943 | 0.9365 | 0.9953 | 0.8842 | 0.9420 | 0.8444 |
| 0.0023 | 3.0 | 23439 | 0.0402 | 0.9949 | 0.9435 | 0.9927 | 0.899 | 0.9493 | 0.8434 |
| 0.0019 | 4.0 | 31252 | 0.0453 | 0.9947 | 0.9412 | 0.9955 | 0.8924 | 0.9461 | 0.8592 |
| 0.0 | 5.0 | 39065 | 0.0500 | 0.9943 | 0.9371 | 0.9955 | 0.8852 | 0.9425 | 0.8404 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
yanezh/twiiter_try15_fold4
|
yanezh
| 2023-05-26T14:14:19Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T13:41:31Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twiiter_try15_fold4
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. -->
# twiiter_try15_fold4
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1791
- F1: 0.9805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2113 | 1.0 | 500 | 0.1149 | 0.9642 |
| 0.0638 | 2.0 | 1000 | 0.1456 | 0.9646 |
| 0.0179 | 3.0 | 1500 | 0.1507 | 0.9737 |
| 0.0171 | 4.0 | 2000 | 0.1835 | 0.9737 |
| 0.0096 | 5.0 | 2500 | 0.2713 | 0.9613 |
| 0.0072 | 6.0 | 3000 | 0.2221 | 0.9695 |
| 0.0073 | 7.0 | 3500 | 0.1639 | 0.9775 |
| 0.0049 | 8.0 | 4000 | 0.2184 | 0.9737 |
| 0.0018 | 9.0 | 4500 | 0.2568 | 0.9723 |
| 0.0062 | 10.0 | 5000 | 0.2106 | 0.9753 |
| 0.0001 | 11.0 | 5500 | 0.2204 | 0.9763 |
| 0.0 | 12.0 | 6000 | 0.2195 | 0.9761 |
| 0.0015 | 13.0 | 6500 | 0.1732 | 0.9795 |
| 0.0 | 14.0 | 7000 | 0.1739 | 0.9810 |
| 0.0011 | 15.0 | 7500 | 0.1791 | 0.9805 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AustinCarthy/Onlyphish_10K_fromB_BFall_20KGen_topP_0.75_noaddedB
|
AustinCarthy
| 2023-05-26T14:09:19Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-25T20:57:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Onlyphish_10K_fromB_BFall_20KGen_topP_0.75_noaddedB
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. -->
# Onlyphish_10K_fromB_BFall_20KGen_topP_0.75_noaddedB
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9951
- F1: 0.9454
- Precision: 0.9973
- Recall: 0.8986
- Roc Auc Score: 0.9492
- Tpr At Fpr 0.01: 0.8868
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------:|:---------------:|
| 0.0132 | 1.0 | 7188 | 0.0420 | 0.9915 | 0.9029 | 0.9945 | 0.8268 | 0.9133 | 0.7952 |
| 0.0034 | 2.0 | 14376 | 0.0398 | 0.9939 | 0.9322 | 0.9950 | 0.8768 | 0.9383 | 0.8162 |
| 0.0022 | 3.0 | 21564 | 0.0348 | 0.9955 | 0.9512 | 0.9937 | 0.9122 | 0.9560 | 0.886 |
| 0.0 | 4.0 | 28752 | 0.0360 | 0.9955 | 0.9507 | 0.9840 | 0.9196 | 0.9594 | 0.0 |
| 0.0 | 5.0 | 35940 | 0.0515 | 0.9951 | 0.9454 | 0.9973 | 0.8986 | 0.9492 | 0.8868 |
### Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
|
davidwtan/my_food_model
|
davidwtan
| 2023-05-26T13:55:14Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-26T13:20:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.915
---
<!-- 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. -->
# my_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5005
- Accuracy: 0.915
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2078 | 0.99 | 62 | 2.9572 | 0.787 |
| 1.7221 | 2.0 | 125 | 1.6469 | 0.861 |
| 1.2109 | 2.99 | 187 | 1.1555 | 0.894 |
| 0.81 | 4.0 | 250 | 0.8631 | 0.91 |
| 0.6486 | 4.99 | 312 | 0.7190 | 0.908 |
| 0.5162 | 6.0 | 375 | 0.6194 | 0.91 |
| 0.4567 | 6.99 | 437 | 0.5399 | 0.924 |
| 0.43 | 8.0 | 500 | 0.5146 | 0.922 |
| 0.3723 | 8.99 | 562 | 0.4914 | 0.922 |
| 0.3938 | 9.92 | 620 | 0.5005 | 0.915 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
teasan/endlessMix
|
teasan
| 2023-05-26T13:50:08Z | 4 | 68 |
diffusers
|
[
"diffusers",
"art",
"stable-diffusion",
"ja",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-01T15:34:02Z |
---
license: creativeml-openrail-m
language:
- ja
tags:
- art
- stable-diffusion
library_name: diffusers
---
# ■endlessMixシリーズについて
## 概要
このモデルはDefactaをベースにした階層マージモデルです。
モデル作者である私が勉強用と自分使用目的に制作しました。
なお、VAEは導入されていないので別途DLしてください。
## 使い方
モデルをcloneもしくはDLした後、以下に格納してください。
```
webui\models\Stable-diffusion\
```
### 推奨設定（作者の設定）
#### V9シリーズ
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 50
- CFG scale: 7～15
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13 or 0
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
#### V8シリーズ
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 50
- CFG scale: 7～15
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13 or 0
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
#### V7
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 50
- CFG scale: 7～15
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13 or 0
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
#### V6
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 50
- CFG scale: 7～15
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13 or 0
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
#### V3
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 30
- CFG scale: 7～15
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13 or 0
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
#### V2
- Sampler: DPM++ 2M Karras
- Step: 20 ~ 30
- CFG scale: 11
- Denoising strength: 0.55 ~ 0.6
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10 ~ 13
- Hires upscaler: Latent (nearest) or R-ESRGAN 4x+
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a> or <a href="https://huggingface.co/sp8999/test_VAE/blob/main/mse840000_klf8anime_klf8anime2.vae.pt" target="_blank">mse840000_klf8anime_klf8anime2</a>
#### V1
- Sampler: DPM++ 2M Karras
- Step: 30
- CFG scale: 11
- Denoising strength: 0.55
- Clip skip: 2
- Hires upscale: 2
- Hires steps: 10
- Hires upscaler: Latent (nearest)
- VAE: <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/tree/main/vae" target="_blank">kl-f8-anime2</a>
### 出力サンプル
<details>
<summary>endlessMixV9</summary>
<div>
ALL
2dMix
realMix
oldFix
```
▲プロンプト
absurdres, highres,
1girl,
Negative prompt: EasyNegative, [ :(negative_hand-neg:1.2):15 ], (worst quality, low quality:1.4), text, monochrome, nsfw
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV8</summary>
<div>
通常版
kawaii_fix
oldFix
```
▲プロンプト
absurdres, highres,
(1girl, solo), big eyes,
townscape,
(Water Effects, Light Effects, Fluttering Feathers:1.2),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
通常版
```
▲プロンプト
absurdres, highres, (official art, beautiful and aesthetic:1.2),
(1girl:1.45), (fractal art:1.3), zentangle, kaleidoscope, (colorfield painting:1.15), (flower effects:0.8), wild style art,
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
kawaii_fix
```
▲プロンプト
absurdres, highres,
(1girl, solo),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
通常版
```
▲プロンプト
absurdres, highres,
1girl, big eyes, high ponytail,☺, [ red | orange ] hair,
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV7</summary>
<div>
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo), big eyes, kawaii, toddler, loli,
townscape,
(Water Effects, Light Effects, Fluttering Feathers:1.2),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo:1.2), 🥰,
(colorfield painting:1.3), (zentangle:1.3), flower effects, (fractal art:1.15),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo:1.2), 🥰,
(colorfield painting:1.3), (zentangle:1.3), flower effects, (fractal art:1.15),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
1gir, artist style, (Fashion Magazine Cover:1.2), (zentangle:1.3), material effects,
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV6</summary>
<div>
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo), (kawaii:1.2),
([ water effects | Light Effects | Feathers effects ]:1.3),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, ultra detailed, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo:1.2), 🥰,
(colorfield painting:1.3), (zentangle:1.3), flower effects, (fractal art:1.15),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, ultra detailed,
1girl, full body, big eye, (toddler, loli, kawaii, expressionless face, cute girl, ( [blue | shining] eye ), small face:1.2), Ruffled Dresses, BREAK
cel shading, (bold outlines):1.2, flat colors, sharp shadows, graphic style, manga influence, clean
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, ultra detailed,
1girl, full body, big eye, (toddler, loli, kawaii, expressionless face, cute girl, ( [blue | shining] eye ), small face:1.2), Ruffled Dresses, BREAK
cel shading, (bold outlines):1.2, flat colors, sharp shadows, graphic style, manga influence, clean
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV3.5</summary>
<div>
```
▲プロンプト
absurdres, highres, ultra detailed, Ultra-precise depiction, Ultra-detailed depiction, (ultra-detailed background, detailed background), extremly detailed,
1gir, solo, zentangle, (Fashion Magazine Cover:1.4),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, text, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
1gir, artist style, (Fashion Magazine Cover:1.5), (zentangle:1.3), material effects,
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo:1.2), 🥰,
(colorfield painting:1.3), (zentangle:1.3), flower effects, (fractal art:1.15),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
```
▲プロンプト
absurdres, highres, (ultra-detailed background, detailed background), extremly detailed,
(1girl, solo), big eyes, kawaii, toddler, loli,
townscape,
(Water Effects, Light Effects, Fluttering Feathers:1.2),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:2), Bad Anatomy, crumpled limbs, bad hands, bad fingers, missing fingers, missing arms, missing legs, extra digit, watermark, username, artist name, signature, grid view, nsfw,
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV2シリーズ</summary>
<div>
```
▲プロンプト
(masterpiece, top quality, best quality, official art, beautiful and aesthetic:1.2),
(1girl, solo), (kawaii:1.2),
([ water effects | Light Effects | Feathers effects ]:1.3),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:1.4), (low quality:1.4), text, grid view, nsfw, crumpled limbs, Bad Anatomy,
```
```
▲プロンプト
(masterpiece, top quality, best quality, official art, beautiful and aesthetic:1.2),
(1girl, solo),
Negative prompt: EasyNegative, bad-hands-5, (worst quality:1.4), (low quality:1.4), text, grid view, nsfw, crumpled limbs, Bad Anatomy,
```
～～～～～～～～～
</div>
</details>
<details>
<summary>endlessMixV1</summary>
<div>
～～～～～～～～～
</div>
</details>
----
# 免責事項
- SFWおよびNSFW画像の作成は、個々のクリエイターの判断によります。モデル製作者は責任を負いません。
- このモデルは、公共の場などでNSFWコンテンツを公開するために作られたモデルではありません。
---
# 当モデルの使用にあたって
以下の事項守っていただき、<strong>常識の範囲内</strong>でご使用ください。
✅ 本モデルで生成した画像を商用利用する行為
✅ 本モデルを使用したマージモデルを使用または再配布する行為
✅ 本モデルのクレジット表記をせずに使用する行為
❌ 本モデルを商用の画像生成サービスで利用する行為
❌ 本モデルや本モデルをマージしたモデルを販売する行為
❌ 本モデルを使用し意図的に違法な出力をする行為
❌ 本モデルをマージしたモデルに異なる権限を与える行為
❌ 本モデルをマージしたモデルを配布または本モデルを再配布した際に同じ使用制限を含め、CreativeML OpenRAIL-M のコピーをすべてのユーザーと共有しない行為
---
# Stable Diffusionのライセンスについて
- このモデルはオープンアクセスで誰でも利用可能であり、CreativeML OpenRAIL-Mライセンスでさらに権利と使用方法が規定されています。
- CreativeML OpenRAILライセンスでは、次のように規定されています。
1. このモデルを使用して、違法または有害な出力やコンテンツを意図的に作成したり、共有したりすることはできません。
2. 作者はあなたが生成した出力に対していかなる権利も主張しません。あなたはそれらを自由に使用することができますが、ライセンスで定められた規定を守ってください。利用は自己責任でお願いします。
3. あなたはウェイトを再配布し、モデルを商業的またはサービスとして使用することができます。その場合、ライセンスにあるものと同じ使用制限を含め、CreativeML OpenRAIL-Mのコピーをあなたのすべてのユーザーに共有しなければならないことに注意してください（ライセンスを完全にかつ注意深く読んでください）。
- (ライセンスの全文: [https://huggingface.co/spaces/CompVis/stable-diffusion-license](https://huggingface.co/spaces/CompVis/stable-diffusion-license))
---
# 作者について
twitter：<a href="https://twitter.com/wims_Tea" target="_blank"> https://twitter.com/wims_Tea</a>
---
|
yanezh/twiiter_try15_fold3
|
yanezh
| 2023-05-26T13:41:22Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T13:08:34Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twiiter_try15_fold3
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. -->
# twiiter_try15_fold3
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1796
- F1: 0.9805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2022 | 1.0 | 500 | 0.1547 | 0.9636 |
| 0.0612 | 2.0 | 1000 | 0.2014 | 0.9660 |
| 0.0211 | 3.0 | 1500 | 0.1204 | 0.9776 |
| 0.0107 | 4.0 | 2000 | 0.1797 | 0.9745 |
| 0.0073 | 5.0 | 2500 | 0.1931 | 0.9752 |
| 0.0128 | 6.0 | 3000 | 0.1808 | 0.9741 |
| 0.0088 | 7.0 | 3500 | 0.1756 | 0.9750 |
| 0.0088 | 8.0 | 4000 | 0.1726 | 0.9781 |
| 0.0012 | 9.0 | 4500 | 0.1707 | 0.9785 |
| 0.0004 | 10.0 | 5000 | 0.1794 | 0.9780 |
| 0.0031 | 11.0 | 5500 | 0.2156 | 0.9743 |
| 0.0012 | 12.0 | 6000 | 0.2106 | 0.9741 |
| 0.0 | 13.0 | 6500 | 0.1925 | 0.9796 |
| 0.0 | 14.0 | 7000 | 0.1903 | 0.9789 |
| 0.0008 | 15.0 | 7500 | 0.1796 | 0.9805 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Evuv/mt5-base-torch
|
Evuv
| 2023-05-26T13:39:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-26T03:38:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-torch
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. -->
# mt5-base-torch
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0425
- Rouge1: 0.0824
- Rouge2: 0.011
- Rougel: 0.0721
- Rougelsum: 0.0721
- Gen Len: 18.2575
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 4.199 | 1.0 | 1600 | 3.1896 | 0.0865 | 0.0116 | 0.0746 | 0.0746 | 18.06 |
| 3.8499 | 2.0 | 3200 | 3.1142 | 0.0905 | 0.0109 | 0.0773 | 0.0774 | 18.1925 |
| 3.6877 | 3.0 | 4800 | 3.0694 | 0.083 | 0.0113 | 0.0719 | 0.0719 | 18.44 |
| 3.571 | 4.0 | 6400 | 3.0425 | 0.0824 | 0.011 | 0.0721 | 0.0721 | 18.2575 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ThomasSimonini/dqn-SpaceInvadersNoFrameskip-v4
|
ThomasSimonini
| 2023-05-26T13:03:28Z | 48 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-06-07T12:25:18Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 329.00 +/- 157.97
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ThomasSimonini -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ThomasSimonini
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
DionnisB/LyCORIS
|
DionnisB
| 2023-05-26T12:45:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-09T23:27:56Z |
---
license: creativeml-openrail-m
---
|
yuval6967/rl_course_vizdoom_health_gathering_supreme
|
yuval6967
| 2023-05-26T12:34:20Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T12:34:09Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.50 +/- 4.98
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r yuval6967/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
yanezh/twiiter_try15_fold1
|
yanezh
| 2023-05-26T12:33:14Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T11:59:47Z |
---
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twiiter_try15_fold1
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. -->
# twiiter_try15_fold1
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1718
- F1: 0.9816
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2154 | 1.0 | 500 | 0.0921 | 0.9763 |
| 0.0689 | 2.0 | 1000 | 0.1517 | 0.9646 |
| 0.0329 | 3.0 | 1500 | 0.0965 | 0.9821 |
| 0.0102 | 4.0 | 2000 | 0.1161 | 0.9819 |
| 0.0097 | 5.0 | 2500 | 0.1399 | 0.9784 |
| 0.0028 | 6.0 | 3000 | 0.2075 | 0.9725 |
| 0.006 | 7.0 | 3500 | 0.1767 | 0.9768 |
| 0.0059 | 8.0 | 4000 | 0.1750 | 0.9775 |
| 0.0001 | 9.0 | 4500 | 0.2467 | 0.9724 |
| 0.0073 | 10.0 | 5000 | 0.1923 | 0.9754 |
| 0.0026 | 11.0 | 5500 | 0.1645 | 0.9790 |
| 0.002 | 12.0 | 6000 | 0.1862 | 0.9801 |
| 0.0008 | 13.0 | 6500 | 0.1643 | 0.98 |
| 0.0 | 14.0 | 7000 | 0.1708 | 0.9816 |
| 0.0 | 15.0 | 7500 | 0.1718 | 0.9816 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rawkul/ppo-LunarLander-v2
|
rawkul
| 2023-05-26T11:53:50Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-26T10:48:53Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.20 +/- 22.96
name: mean_reward
verified: false
---
# **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
...
```
|
gsotnikov/ppo-Pyramids
|
gsotnikov
| 2023-05-26T11:40:53Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-26T11:40:14Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: gsotnikov/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
GraydientPlatformAPI/con-dep
|
GraydientPlatformAPI
| 2023-05-26T11:37:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:openrail",
"region:us"
] |
text-to-image
| 2023-05-23T12:03:58Z |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
zrthxn/vicuna-embedding
|
zrthxn
| 2023-05-26T11:32:19Z | 0 | 0 | null |
[
"embeddings",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-04-18T15:43:56Z |
---
license: apache-2.0
language:
- en
tags:
- embeddings
---
Word-embedding layer stolen from a LLaMA 13B model. The vocab size is 32K.
|
YakovElm/Hyperledger15Classic_256
|
YakovElm
| 2023-05-26T11:12:44Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-26T11:12:07Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger15Classic_256
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Hyperledger15Classic_256
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2409
- Train Accuracy: 0.9097
- Validation Loss: 0.4492
- Validation Accuracy: 0.8766
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3241 | 0.8955 | 0.3393 | 0.8807 | 0 |
| 0.2856 | 0.9035 | 0.3414 | 0.8797 | 1 |
| 0.2409 | 0.9097 | 0.4492 | 0.8766 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
PeiBolio/bolio_xrz
|
PeiBolio
| 2023-05-26T10:31:31Z | 0 | 1 | null |
[
"cactus",
"lora",
"region:us"
] | null | 2023-05-23T10:35:48Z |
---
tags:
- cactus
- lora
---
这是一个lora模型，使用112张高质量仙人掌图片训练，训练图片尺寸768x512，主要包含：裸萼属、圆盘玉属、极光球属、智利球属、龙爪球属，以及少量乳突属、兜、鱼、顶花、多棱、等。避免使用存在花苞的图片。
调用方式：全部属通用：cactus；
特定属裸萼：bolio_luoe；特定属智利：bolio_zhili；特定属智利：bolio_zhili；特定属兜：bolio_dou；特定属极光：bolio_jiguang；
特定属顶花：bolio_dinghua；特定属圆盘玉：bolio_yuanpan；特定属龙爪：bolio_longzhua；特定属鱼：bolio_yu；特定属多棱：bolio_duoleng；
|
sarahpuspdew/DeepRLCourse_Unit5-ppo-Pyramids
|
sarahpuspdew
| 2023-05-26T10:20:10Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-26T10:20:03Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: sarahpuspdew/DeepRLCourse_Unit5-ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yash-srivastava19/hindi_udpipe
|
yash-srivastava19
| 2023-05-26T10:17:32Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-05-26T10:09:33Z |
---
license: mit
---
Hindi UDPIPE model based on the data provided by IIIT Hyderabad
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.