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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| downloads
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| likes
int64 0
11.7k
| library_name
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hopkins/eng-kor-wsample.41 | hopkins | 2023-07-04T21:22:51Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T21:05:23Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-kor-wsample.41
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-kor-wsample.41
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9881
- Bleu: 7.0463
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
michaelsh/distilhubert-finetuned-gtzan | michaelsh | 2023-07-04T21:17:55Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-07-04T09:04:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7058
- Accuracy: 0.99
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7675 | 1.0 | 112 | 1.8184 | 0.42 |
| 1.2504 | 2.0 | 225 | 1.3015 | 0.62 |
| 1.0353 | 3.0 | 337 | 0.9890 | 0.72 |
| 0.8318 | 4.0 | 450 | 0.8237 | 0.8 |
| 0.4429 | 5.0 | 562 | 0.8123 | 0.78 |
| 0.4286 | 6.0 | 675 | 0.6820 | 0.8 |
| 0.2553 | 7.0 | 787 | 0.7826 | 0.78 |
| 0.3022 | 8.0 | 900 | 0.6811 | 0.77 |
| 0.1889 | 9.0 | 1012 | 0.6761 | 0.8 |
| 0.1073 | 9.96 | 1120 | 0.7058 | 0.79 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
|
pabloma09/alpaca-lora-spam | pabloma09 | 2023-07-04T21:12:31Z | 3 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T11:41:31Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
hopkins/eng-ind-wsample.44 | hopkins | 2023-07-04T21:05:47Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T20:51:48Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-wsample.44
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. -->
# eng-ind-wsample.44
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7625
- Bleu: 21.9586
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.45 | hopkins | 2023-07-04T20:40:12Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T20:22:09Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.45
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-deu-wsample.45
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6514
- Bleu: 20.9841
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.44 | hopkins | 2023-07-04T20:39:35Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T20:21:25Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.44
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. -->
# eng-deu-wsample.44
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6513
- Bleu: 20.8990
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.43 | hopkins | 2023-07-04T20:39:18Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T20:21:11Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.43
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-deu-wsample.43
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6511
- Bleu: 20.9323
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jackoyoungblood/TinyStories-validationset | jackoyoungblood | 2023-07-04T20:22:14Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-04T20:20:08Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: TinyStories-validationset
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. -->
# TinyStories-validationset
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hopkins/eng-kor-simcse.dev2.4440 | hopkins | 2023-07-04T20:10:34Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-03T17:39:58Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-kor-simcse.dev2.4440
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. -->
# eng-kor-simcse.dev2.4440
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9773
- Bleu: 7.5853
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pankaj10034/Sentiment_analysis | pankaj10034 | 2023-07-04T20:04:06Z | 0 | 0 | transformers | [
"transformers",
"endpoints_compatible",
"region:us"
]
| null | 2023-07-04T20:02:48Z | ---
library_name: transformers
--- |
taohungchang/candy_model | taohungchang | 2023-07-04T20:02:51Z | 207 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| object-detection | 2023-06-30T19:25:41Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: candy_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. -->
# candy_model
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 350
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ykirpichev/distilhubert-finetuned-gtzan | ykirpichev | 2023-07-04T20:01:53Z | 160 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2023-07-04T00:09:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.86
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5817
- Accuracy: 0.86
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6016 | 1.0 | 113 | 0.7182 | 0.78 |
| 0.4667 | 2.0 | 226 | 0.6449 | 0.79 |
| 0.4518 | 3.0 | 339 | 0.6754 | 0.82 |
| 0.2882 | 4.0 | 452 | 0.5906 | 0.84 |
| 0.1197 | 5.0 | 565 | 0.5817 | 0.86 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hopkins/eng-ind-simcse.dev2.4440 | hopkins | 2023-07-04T19:19:47Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-03T17:21:54Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-simcse.dev2.4440
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. -->
# eng-ind-simcse.dev2.4440
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7491
- Bleu: 22.7452
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-ind-wsample.42 | hopkins | 2023-07-04T19:18:30Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T16:00:01Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-wsample.42
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. -->
# eng-ind-wsample.42
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7642
- Bleu: 21.7118
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
luhx/dqn-SpaceInvadersNoFrameskip-v4 | luhx | 2023-07-04T19:16:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T19:16:07Z | ---
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: 257.00 +/- 38.81
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 luhx -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 luhx -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 luhx
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('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', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
rohanbalkondekar/adept-skunk | rohanbalkondekar | 2023-07-04T19:06:11Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-07-04T18:59:30Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="BeRohan/adept-skunk",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"BeRohan/adept-skunk",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"BeRohan/adept-skunk",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "BeRohan/adept-skunk" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BeRohan/adept-skunk --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
heka-ai/tasb-bert-30k | heka-ai | 2023-07-04T19:02:49Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-07-04T19:02:46Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# heka-ai/tasb-bert-30k
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('heka-ai/tasb-bert-30k')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('heka-ai/tasb-bert-30k')
model = AutoModel.from_pretrained('heka-ai/tasb-bert-30k')
# 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, cls pooling.
sentence_embeddings = cls_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=heka-ai/tasb-bert-30k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
SalehAbdelrahman/q-FrozenLake-v1-4x4-noSlippery | SalehAbdelrahman | 2023-07-04T19:00:47Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T19:00:45Z | ---
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="SalehAbdelrahman/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"])
```
|
hopkins/eng-deu-simcse.near2.4440 | hopkins | 2023-07-04T18:55:10Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T18:37:07Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-simcse.near2.4440
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. -->
# eng-deu-simcse.near2.4440
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6617
- Bleu: 21.0417
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jordyvl/LayoutLMv3_maveriq_tobacco3482_2023-07-04 | jordyvl | 2023-07-04T18:35:44Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T18:25:14Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LayoutLMv3_maveriq_tobacco3482_2023-07-04
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. -->
# LayoutLMv3_maveriq_tobacco3482_2023-07-04
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9452
- Accuracy: 0.28
## 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: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 3 | 2.1539 | 0.28 |
| No log | 1.96 | 6 | 2.0282 | 0.275 |
| No log | 2.96 | 9 | 2.0001 | 0.265 |
| No log | 3.96 | 12 | 1.9591 | 0.265 |
| No log | 4.96 | 15 | 1.9452 | 0.28 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
hopkins/eng-deu-simcse.dev2.4440 | hopkins | 2023-07-04T18:30:13Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-03T17:07:41Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-simcse.dev2.4440
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. -->
# eng-deu-simcse.dev2.4440
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6391
- Bleu: 21.6215
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.42 | hopkins | 2023-07-04T18:27:55Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T15:59:33Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.42
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. -->
# eng-deu-wsample.42
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6513
- Bleu: 20.8783
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.49 | hopkins | 2023-07-04T18:27:49Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-07-04T15:59:30Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.49
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. -->
# eng-deu-wsample.49
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6500
- Bleu: 21.1322
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
wizofavalon/my_awesome_model | wizofavalon | 2023-07-04T18:21:48Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-03T22:07:58Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.94084
---
<!-- 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_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.9408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2142 | 1.0 | 1563 | 0.1712 | 0.9356 |
| 0.1281 | 2.0 | 3126 | 0.2181 | 0.9408 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
andypyc/news_classifier | andypyc | 2023-07-04T18:06:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-07-04T18:05:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: news_classifier
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. -->
# news_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 48 | 1.2860 | 0.3646 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jordyvl/dit_maveriq_tobacco3482_2023-07-04_noaccum | jordyvl | 2023-07-04T18:04:27Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-07-04T17:26:06Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit_maveriq_tobacco3482_2023-07-04_noaccum
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. -->
# dit_maveriq_tobacco3482_2023-07-04_noaccum
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3767
- Accuracy: 0.95
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 25 | 1.6738 | 0.405 |
| No log | 2.0 | 50 | 1.2848 | 0.605 |
| No log | 3.0 | 75 | 0.9015 | 0.74 |
| No log | 4.0 | 100 | 0.6159 | 0.79 |
| No log | 5.0 | 125 | 0.4341 | 0.85 |
| No log | 6.0 | 150 | 0.3338 | 0.885 |
| No log | 7.0 | 175 | 0.3210 | 0.89 |
| No log | 8.0 | 200 | 0.3121 | 0.915 |
| No log | 9.0 | 225 | 0.3235 | 0.915 |
| No log | 10.0 | 250 | 0.3003 | 0.92 |
| No log | 11.0 | 275 | 0.2602 | 0.94 |
| No log | 12.0 | 300 | 0.2892 | 0.94 |
| No log | 13.0 | 325 | 0.2865 | 0.945 |
| No log | 14.0 | 350 | 0.3103 | 0.94 |
| No log | 15.0 | 375 | 0.2959 | 0.955 |
| No log | 16.0 | 400 | 0.3026 | 0.94 |
| No log | 17.0 | 425 | 0.3082 | 0.94 |
| No log | 18.0 | 450 | 0.2951 | 0.94 |
| No log | 19.0 | 475 | 0.3310 | 0.94 |
| 0.4279 | 20.0 | 500 | 0.3335 | 0.95 |
| 0.4279 | 21.0 | 525 | 0.3035 | 0.94 |
| 0.4279 | 22.0 | 550 | 0.3155 | 0.945 |
| 0.4279 | 23.0 | 575 | 0.3539 | 0.945 |
| 0.4279 | 24.0 | 600 | 0.3359 | 0.95 |
| 0.4279 | 25.0 | 625 | 0.3887 | 0.945 |
| 0.4279 | 26.0 | 650 | 0.3998 | 0.935 |
| 0.4279 | 27.0 | 675 | 0.4087 | 0.94 |
| 0.4279 | 28.0 | 700 | 0.4065 | 0.93 |
| 0.4279 | 29.0 | 725 | 0.3713 | 0.935 |
| 0.4279 | 30.0 | 750 | 0.3547 | 0.945 |
| 0.4279 | 31.0 | 775 | 0.3757 | 0.93 |
| 0.4279 | 32.0 | 800 | 0.3613 | 0.945 |
| 0.4279 | 33.0 | 825 | 0.3686 | 0.945 |
| 0.4279 | 34.0 | 850 | 0.3254 | 0.945 |
| 0.4279 | 35.0 | 875 | 0.3514 | 0.95 |
| 0.4279 | 36.0 | 900 | 0.3061 | 0.95 |
| 0.4279 | 37.0 | 925 | 0.3339 | 0.94 |
| 0.4279 | 38.0 | 950 | 0.3241 | 0.955 |
| 0.4279 | 39.0 | 975 | 0.2779 | 0.955 |
| 0.029 | 40.0 | 1000 | 0.2788 | 0.955 |
| 0.029 | 41.0 | 1025 | 0.2993 | 0.95 |
| 0.029 | 42.0 | 1050 | 0.3171 | 0.955 |
| 0.029 | 43.0 | 1075 | 0.3340 | 0.95 |
| 0.029 | 44.0 | 1100 | 0.3463 | 0.955 |
| 0.029 | 45.0 | 1125 | 0.3417 | 0.955 |
| 0.029 | 46.0 | 1150 | 0.3377 | 0.96 |
| 0.029 | 47.0 | 1175 | 0.3424 | 0.945 |
| 0.029 | 48.0 | 1200 | 0.3377 | 0.95 |
| 0.029 | 49.0 | 1225 | 0.3731 | 0.935 |
| 0.029 | 50.0 | 1250 | 0.3719 | 0.95 |
| 0.029 | 51.0 | 1275 | 0.3615 | 0.945 |
| 0.029 | 52.0 | 1300 | 0.3473 | 0.955 |
| 0.029 | 53.0 | 1325 | 0.3427 | 0.945 |
| 0.029 | 54.0 | 1350 | 0.4078 | 0.94 |
| 0.029 | 55.0 | 1375 | 0.3763 | 0.955 |
| 0.029 | 56.0 | 1400 | 0.3844 | 0.945 |
| 0.029 | 57.0 | 1425 | 0.3845 | 0.945 |
| 0.029 | 58.0 | 1450 | 0.3976 | 0.94 |
| 0.029 | 59.0 | 1475 | 0.3636 | 0.95 |
| 0.0115 | 60.0 | 1500 | 0.3431 | 0.95 |
| 0.0115 | 61.0 | 1525 | 0.3161 | 0.955 |
| 0.0115 | 62.0 | 1550 | 0.3482 | 0.945 |
| 0.0115 | 63.0 | 1575 | 0.3693 | 0.945 |
| 0.0115 | 64.0 | 1600 | 0.3435 | 0.95 |
| 0.0115 | 65.0 | 1625 | 0.3403 | 0.955 |
| 0.0115 | 66.0 | 1650 | 0.3644 | 0.95 |
| 0.0115 | 67.0 | 1675 | 0.3604 | 0.955 |
| 0.0115 | 68.0 | 1700 | 0.3746 | 0.945 |
| 0.0115 | 69.0 | 1725 | 0.3899 | 0.94 |
| 0.0115 | 70.0 | 1750 | 0.3684 | 0.95 |
| 0.0115 | 71.0 | 1775 | 0.4124 | 0.94 |
| 0.0115 | 72.0 | 1800 | 0.4010 | 0.95 |
| 0.0115 | 73.0 | 1825 | 0.3991 | 0.95 |
| 0.0115 | 74.0 | 1850 | 0.3859 | 0.95 |
| 0.0115 | 75.0 | 1875 | 0.3832 | 0.96 |
| 0.0115 | 76.0 | 1900 | 0.4054 | 0.955 |
| 0.0115 | 77.0 | 1925 | 0.4119 | 0.955 |
| 0.0115 | 78.0 | 1950 | 0.3724 | 0.955 |
| 0.0115 | 79.0 | 1975 | 0.3609 | 0.95 |
| 0.0116 | 80.0 | 2000 | 0.3663 | 0.955 |
| 0.0116 | 81.0 | 2025 | 0.3711 | 0.955 |
| 0.0116 | 82.0 | 2050 | 0.3730 | 0.955 |
| 0.0116 | 83.0 | 2075 | 0.3775 | 0.955 |
| 0.0116 | 84.0 | 2100 | 0.3805 | 0.96 |
| 0.0116 | 85.0 | 2125 | 0.3802 | 0.96 |
| 0.0116 | 86.0 | 2150 | 0.3773 | 0.96 |
| 0.0116 | 87.0 | 2175 | 0.3684 | 0.955 |
| 0.0116 | 88.0 | 2200 | 0.3750 | 0.95 |
| 0.0116 | 89.0 | 2225 | 0.3727 | 0.945 |
| 0.0116 | 90.0 | 2250 | 0.3742 | 0.945 |
| 0.0116 | 91.0 | 2275 | 0.3729 | 0.945 |
| 0.0116 | 92.0 | 2300 | 0.3727 | 0.945 |
| 0.0116 | 93.0 | 2325 | 0.3752 | 0.95 |
| 0.0116 | 94.0 | 2350 | 0.3726 | 0.945 |
| 0.0116 | 95.0 | 2375 | 0.3738 | 0.945 |
| 0.0116 | 96.0 | 2400 | 0.3747 | 0.945 |
| 0.0116 | 97.0 | 2425 | 0.3755 | 0.945 |
| 0.0116 | 98.0 | 2450 | 0.3757 | 0.95 |
| 0.0116 | 99.0 | 2475 | 0.3764 | 0.95 |
| 0.0061 | 100.0 | 2500 | 0.3767 | 0.95 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
andypyc/news_classifier_dummy | andypyc | 2023-07-04T17:53:33Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-06-30T03:39:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: news_classifier
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. -->
# news_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 48 | 1.3055 | 0.3073 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
AyaF/AyaF | AyaF | 2023-07-04T17:43:49Z | 233 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-12-02T10:23:03Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
- Precision
- Recall
- F1Score
model-index:
- name: ArSL VIT
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9934656620025635
- name: Precision
type: Precision
value: 0.9939382672309875
- name: Recall
type: Recall
value: 0.9934656620025635
- name: F1Score
type: F1Score
value: 0.9933341145515442
---
|
SebastianBodza/DElefant | SebastianBodza | 2023-07-04T17:34:56Z | 16 | 4 | transformers | [
"transformers",
"pytorch",
"bloom",
"text-generation",
"de",
"dataset:SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-04T07:59:22Z | ---
license: cc-by-nc-sa-4.0
datasets:
- SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0
language:
- de
---
# DElefant:
<img src="https://huggingface.co/SebastianBodza/DElefant/resolve/main/badge_gerlefant.png" style="max-width:200px">
DElefant is a LLM developed for instruction tuned German interactions. This version is built on top of the adapted BLOOM version from <a href="https://huggingface.co/malteos/bloom-6b4-clp-german">Malte Ostendorff</a> with a opus-mt translated and afterwards filtered <a href="https://huggingface.co/datasets/SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0">WizardLM</a> dataset. The evolved dataset led to SOTA english LLMs and we hope by incoperating the dataset to a german base model we can leverage the capabilities for various tasks including Code generation.
Due to limitation in translation, the comments inside of the code blocks remained english, however the Coding was kept in working condition.
## Model Description:
Full-Finetuning of the German-BLOOM model on an RTX 3090 with the translated WizardLM Dataset.
## Roadmap:
If there is sufficient demand, additional adjustments can be made:
- Native German generated dataset
- Full Fine-Tuning of larger LLMs e.g. Falcon, Starcoderplus, ...
## How to use:
Prompt-Template:
```
{instruction}\n\n### Response:
```
Code example for inference:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("SebastianBodza/DElefant")
model = AutoModelForCausalLM.from_pretrained("SebastianBodza/DElefant", device_map="auto")
frage = "Wie heißt der Bundeskanzler?"
prompt = f"{frage}\n\n### Response:"
txt = tokenizer(prompt, return_tensors="pt").to("cuda")
txt = model.generate(**txt,
max_new_tokens=256,
eos_token_id=tokenizer.eos_token_id)
tokenizer.decode(txt[0], skip_special_tokens=True)
```
## Training:
Training was based on Llama-X with the adaptions of WizardLMs training script.
```
deepspeed Llama-X/src/train_freeform.py \
--model_name_or_path malteos/bloom-6b4-clp-german \
--data_path ger_alpaca_evol_instruct_70k_e.json \
--output_dir ./full_finetune \
--num_train_epochs 2 \
--model_max_length 2048 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 400 \
--save_total_limit 3 \
--learning_rate 2e-5 \
--warmup_steps 2 \
--logging_steps 2 \
--lr_scheduler_type "cosine" \
--report_to "tensorboard" \
--gradient_checkpointing True \
--deepspeed deepspeed.json \
--bf16 True
```
<img src="https://huggingface.co/SebastianBodza/DElefant/resolve/main/train_loss_DElefant.svg" style="max-width:350px">
|
digiplay/ShampooMix_4 | digiplay | 2023-07-04T17:28:03Z | 297 | 6 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-06-20T08:34:34Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/33918/shampoo-mix
 |
darthPanda/ppo-LunarLander-v1 | darthPanda | 2023-07-04T17:23:17Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T17:17:13Z | ---
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: 274.63 +/- 21.45
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
...
```
|
SebastianBodza/DElefant-MPT | SebastianBodza | 2023-07-04T17:20:48Z | 0 | 2 | null | [
"de",
"dataset:SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-07-04T08:00:02Z | ---
license: cc-by-nc-sa-4.0
datasets:
- SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0
language:
- de
---
# DElefant-MPT:
<img src="https://huggingface.co/SebastianBodza/DElefant-MPT/resolve/main/badge_gerlefant.png" style="max-width:200px">
DElefant is a LLM developed for instruction tuned German interactions. This version is built on top of the MPT-30B model from <a href="https://huggingface.co/mosaicml/mpt-30b">MosaicML</a> with a opus-mt translated and afterwards filtered <a href="https://huggingface.co/datasets/SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0">WizardLM</a> dataset. The evolved dataset led to SOTA english LLMs and we hope by incoperating the translated dataset to a base model we can leverage the capabilities for various tasks in german including Code generation.
Due to limitation in translation, the comments inside of the code blocks remained english, however the Coding was kept in working condition.
## Model Description:
QLoRa-Finetuning of the MPT-30B model on two RTX 3090 with the translated WizardLM Dataset.
## Roadmap:
If there is sufficient demand, additional adjustments can be made:
- Native German generated dataset
- Full Fine-Tuning of larger LLMs e.g. Falcon, Starcoderplus, ...
## How to use:
Prompt-Template:
```
{instruction}\n\n### Response:
```
Code example for inference:
```
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load peft config for pre-trained checkpoint etc.
config = PeftConfig.from_pretrained("SebastianBodza/DElefant-MPT")
# load base LLM model and tokenizer
tokenizer = AutoTokenizer.from_pretrained( "mosaicml/mpt-30b",
padding_side="right",
use_fast=True)
model = AutoModelForCausalLM.from_pretrained("mosaicml/mpt-30b", device_map="auto", load_in_8bit=True)
# Load the Lora model
model = PeftModel.from_pretrained(model, "SebastianBodza/DElefant-MPT", device_map={"":0})
model.eval()
frage = "Wie heißt der Bundeskanzler?"
prompt = f"{frage}\n\n### Response:"
txt = tokenizer(prompt, return_tensors="pt").to("cuda")
txt = model.generate(**txt,
max_new_tokens=256,
eos_token_id=tokenizer.eos_token_id)
tokenizer.decode(txt[0], skip_special_tokens=True)
```
## Limitations:
Gradient-Accumulation led to divergence after a couple of steps. Therefore we reduced the blocksize to 1024 and used two RTX 3090 to get a BS of 4. Probably too small to generalize well.
## Training:
Training was based on Llama-X with the adaptions of WizardLMs training script and additional adjustments to QLoRa tune. MPT-Code from <a href="https://huggingface.co/SebastianBodza/mpt-30B-qlora-multi_GPU">SebastianBodza/mpt-30B-qlora-multi_GPU</a>
<img src="https://huggingface.co/SebastianBodza/DElefant-MPT/resolve/main/train_loss_DElefant.svg" style="max-width:350px">
|
AIRI-Institute/gena-lm-bigbird-base-sparse | AIRI-Institute | 2023-07-04T17:20:34Z | 49 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"pretraining",
"dna",
"human_genome",
"custom_code",
"arxiv:2002.04745",
"endpoints_compatible",
"region:us"
]
| null | 2023-04-02T14:30:00Z | ---
tags:
- dna
- human_genome
---
# GENA-LM (gena-lm-bigbird-base-sparse)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
`gena-lm-bigbird-base-sparse` follows the BigBird architecture and uses sparse attention from DeepSpeed.
Differences between GENA-LM (`gena-lm-bigbird-base-sparse`) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
## Installation
`gena-lm-bigbird-base-sparse` sparse ops require DeepSpeed.
### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).
```bash
pip install triton==1.0.0
DS_BUILD_SPARSE_ATTN=1 pip install deepspeed==0.6.0 --global-option="build_ext" --global-option="-j8" --no-cache
```
and check installation with
```bash
ds_report
```
### APEX for FP16
Install APEX https://github.com/NVIDIA/apex#quick-start
```
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
## Examples
### How to load pre-trained model for Masked Language Modeling
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)
```
### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:
```bash
git clone https://github.com/AIRI-Institute/GENA_LM.git
```
```python
from GENA_LM.src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
```
or you can just download [modeling_bert.py](https://github.com/AIRI-Institute/GENA_LM/tree/main/src/gena_lm) and put it close to your code.
OR you can get model class from HuggingFace AutoModel:
```python
from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)
gena_module_name = model.__class__.__module__
print(gena_module_name)
import importlib
# available class names:
# - BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
# - BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
# - BertForQuestionAnswering
# check https://huggingface.co/docs/transformers/model_doc/bert
cls = getattr(importlib.import_module(gena_module_name), 'BertForSequenceClassification')
print(cls)
model = cls.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', num_labels=2)
```
## Model description
GENA-LM (`gena-lm-bigbird-base-sparse`) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for `gena-lm-bigbird-base-sparse` is similar to the `google/bigbird-roberta-base`:
- 4096 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- sparse config:
- block size: 64
- random blocks: 3
- global blocks: 2
- sliding window blocks: 3
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.
We pre-trained `gena-lm-bigbird-base-sparse` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 810,000 iterations with batch size 256. We modified Transformer with [Pre-Layer normalization](https://arxiv.org/abs/2002.04745).
## Evaluation
For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
## Citation
```bibtex
@article{GENA_LM,
author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences},
elocation-id = {2023.06.12.544594},
year = {2023},
doi = {10.1101/2023.06.12.544594},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594},
eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
journal = {bioRxiv}
}
``` |
RajkNakka/Reinforce-CartPole-v1 | RajkNakka | 2023-07-04T17:14:28Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T17:14:19Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 474.90 +/- 23.77
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Babaili/videomae-base-finetuned-ucf101-subset | Babaili | 2023-07-04T16:35:36Z | 59 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| video-classification | 2023-06-21T08:51:24Z | ---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3724
- Accuracy: 0.8387
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5571 | 0.26 | 38 | 1.2529 | 0.5429 |
| 0.5959 | 1.26 | 76 | 0.5709 | 0.7857 |
| 0.3211 | 2.26 | 114 | 0.4260 | 0.8143 |
| 0.2013 | 3.23 | 148 | 0.3246 | 0.9 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
zwangab91/ppo-LunarLander-v2 | zwangab91 | 2023-07-04T16:34:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T16:33:34Z | ---
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: 254.72 +/- 17.78
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
...
```
|
breadlicker45/musenet-untrained | breadlicker45 | 2023-07-04T16:20:42Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"big_bird",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-28T21:08:49Z | this is untrained meaning it will not do ANYTHING, DO NOT DOWNLOAD UNLESS YOU ARE GOING TO TRAIN IT. |
breadlicker45/neox-musenet-untrained | breadlicker45 | 2023-07-04T16:20:29Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-06-29T16:54:09Z | this is untrained meaning it will not do ANYTHING, DO NOT DOWNLOAD UNLESS YOU ARE GOING TO TRAIN IT. |
khalidalt/m2m100_418M-finetuned-en-to-ar | khalidalt | 2023-07-04T16:18:34Z | 102 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"ar",
"dataset:opus100",
"dataset:un_multi",
"arxiv:2010.11125",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-07-04T13:06:30Z | ---
license: mit
datasets:
- opus100
- un_multi
language:
- en
- ar
---
M2M100 418M
M2M100 is a multilingual encoder-decoder transformer model trained for Many-to-Many multilingual translation. The model, originally introduced by researchers at Facebook, demonstrates impressive performance in cross-lingual translation tasks.
For a better understanding of M2M100 you can look into the [paper](https://arxiv.org/abs/2010.11125) and the associated [repository](https://github.com/facebookresearch/fairseq/tree/main/examples/m2m_100).
To further enhance the capabilities of M2M100, we conducted finetuning experiments on English-to-Arabic parallel text. The finetuning process involved training the model for 1000K steps using a batch size of 8. |
LarryAIDraw/fgoYangguifeiv1 | LarryAIDraw | 2023-07-04T16:18:21Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T15:43:05Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/102725/yangguifei-4-outfits-fate-grand-order-4-riuki-lora |
LarryAIDraw/Cecily_v1.0 | LarryAIDraw | 2023-07-04T16:17:33Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T16:01:51Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/100322?modelVersionId=107380 |
Shresthadev403/codeparrot-ds | Shresthadev403 | 2023-07-04T15:58:39Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-04T14:25:16Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dp15/cartProb | dp15 | 2023-07-04T15:52:29Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T15:52:10Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartProb
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
nomad-ai/dqn-SpaceInvadersNoFrameskip-v4 | nomad-ai | 2023-07-04T15:43:24Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T15:42:48Z | ---
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: 910.00 +/- 325.08
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 nomad-ai -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 nomad-ai -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 nomad-ai
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 500000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gamma', 0.99),
('gradient_steps', 1),
('learning_rate', 0.00015),
('learning_starts', 100000),
('n_timesteps', 10000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
HilbertS/poca-SoccerTwos | HilbertS | 2023-07-04T15:40:20Z | 48 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-07-04T14:29:40Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: HilbertS/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zwich07/NovariaLora | zwich07 | 2023-07-04T15:39:48Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-07-04T15:22:23Z | ---
license: creativeml-openrail-m
---
|
Word2vec/nlpl_113 | Word2vec | 2023-07-04T15:33:20Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T14:23:18Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 1487995 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_113", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/113.zip |
Word2vec/nlpl_110 | Word2vec | 2023-07-04T15:31:40Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T14:20:00Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 4428648 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_110", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/110.zip
|
wqewq/zhangjingyi | wqewq | 2023-07-04T15:31:40Z | 0 | 0 | null | [
"arxiv:1910.09700",
"region:us"
]
| null | 2023-07-01T07:06:48Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
Word2vec/nlpl_106 | Word2vec | 2023-07-04T15:30:51Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:NoWaC",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T14:16:31Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NoWaC
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 1356632 corresponding to 687209465 tokens from the dataset `NoWaC`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_106", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/106.zip |
Word2vec/nlpl_99 | Word2vec | 2023-07-04T15:29:20Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:50:31Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 4031460 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_99", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/99.zip
|
Word2vec/nlpl_98 | Word2vec | 2023-07-04T15:29:11Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:50:29Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 2390583 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_98", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/98.zip |
maxkskhor/dqn-SpaceInvadersNoFrameskip-v4 | maxkskhor | 2023-07-04T15:28:37Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-07-04T15:28:00Z | ---
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: 552.00 +/- 182.39
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 maxkskhor -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 maxkskhor -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 maxkskhor
```
## 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', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Word2vec/nlpl_94 | Word2vec | 2023-07-04T15:28:18Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:48:07Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 1728100 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_94", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/94.zip |
Word2vec/nlpl_93 | Word2vec | 2023-07-04T15:28:08Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:47:35Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 1487994 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_93", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/93.zip |
Word2vec/nlpl_91 | Word2vec | 2023-07-04T15:27:49Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:45:37Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 2239664 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_91", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/91.zip |
Word2vec/nlpl_90 | Word2vec | 2023-07-04T15:27:30Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:39:39Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 2390584 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: no lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_90", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/90.zip |
Word2vec/nlpl_89 | Word2vec | 2023-07-04T15:27:20Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:38:43Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 2187703 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_89", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/89.zip
|
Word2vec/nlpl_87 | Word2vec | 2023-07-04T15:26:55Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:NoWaC",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:37:31Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NoWaC
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 1199275 corresponding to 687209465 tokens from the dataset `NoWaC`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_87", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/87.zip |
Word2vec/nlpl_83 | Word2vec | 2023-07-04T15:26:08Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:30:59Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 2239665 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_83", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/83.zip |
Word2vec/nlpl_80 | Word2vec | 2023-07-04T15:25:25Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:21:17Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 3998140 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_80", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/80.zip |
Word2vec/nlpl_78 | Word2vec | 2023-07-04T15:25:05Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:18:02Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 4031461 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_78", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/78.zip |
Word2vec/nlpl_77 | Word2vec | 2023-07-04T15:24:55Z | 0 | 0 | null | [
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:16:01Z | ---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes ([email protected]) on a vocabulary of size 4480046 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_77", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/77.zip |
Word2vec/nlpl_75 | Word2vec | 2023-07-04T15:23:47Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Oil_and_Gas_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:13:00Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Oil_and_Gas_corpus
---
## Information
A word2vec model trained by Farhad Nooralahzadeh ([email protected]) on a vocabulary of size 285055 corresponding to 108000000 tokens from the dataset `Oil_and_Gas_corpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 400.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_75", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/75.zip |
Word2vec/nlpl_74 | Word2vec | 2023-07-04T15:23:35Z | 0 | 0 | null | [
"word2vec",
"vie",
"dataset:Vietnamese_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:11:45Z | ---
language: vie
license: cc-by-4.0
tags:
- word2vec
datasets: Vietnamese_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 3847942 corresponding to 4233272187 tokens from the dataset `Vietnamese_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_74", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/74.zip |
Word2vec/nlpl_72 | Word2vec | 2023-07-04T15:23:15Z | 0 | 0 | null | [
"word2vec",
"urd",
"dataset:Urdu_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:11:37Z | ---
language: urd
license: cc-by-4.0
tags:
- word2vec
datasets: Urdu_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 108310 corresponding to 47959384 tokens from the dataset `Urdu_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_72", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/72.zip |
Word2vec/nlpl_71 | Word2vec | 2023-07-04T15:23:04Z | 0 | 0 | null | [
"word2vec",
"ukr",
"dataset:Ukrainian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T13:11:18Z | ---
language: ukr
license: cc-by-4.0
tags:
- word2vec
datasets: Ukrainian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 942071 corresponding to 574319117 tokens from the dataset `Ukrainian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_71", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/71.zip |
Word2vec/nlpl_70 | Word2vec | 2023-07-04T15:22:43Z | 0 | 0 | null | [
"word2vec",
"tur",
"dataset:Turkish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:51:26Z | ---
language: tur
license: cc-by-4.0
tags:
- word2vec
datasets: Turkish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 3633786 corresponding to 3668140172 tokens from the dataset `Turkish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_70", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/70.zip |
Word2vec/nlpl_66 | Word2vec | 2023-07-04T15:21:53Z | 0 | 0 | null | [
"word2vec",
"slk",
"dataset:Slovak_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:49:41Z | ---
language: slk
license: cc-by-4.0
tags:
- word2vec
datasets: Slovak_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 1188804 corresponding to 855770850 tokens from the dataset `Slovak_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_66", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/66.zip
|
Word2vec/nlpl_62 | Word2vec | 2023-07-04T15:20:23Z | 0 | 0 | null | [
"word2vec",
"pol",
"dataset:Polish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:44:03Z | ---
language: pol
license: cc-by-4.0
tags:
- word2vec
datasets: Polish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 4420598 corresponding to 5489171333 tokens from the dataset `Polish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_62", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/62.zip |
Khushnur/t5-base-end2end-questions-generation_eli_aug_squad | Khushnur | 2023-07-04T15:19:38Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5_cleaned_datav3_60k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-07-04T09:44:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5_cleaned_datav3_60k
model-index:
- name: t5-base-end2end-questions-generation_eli_aug_squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-end2end-questions-generation_eli_aug_squad
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5_cleaned_datav3_60k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3334
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7135 | 0.26 | 100 | 2.4710 |
| 2.2744 | 0.51 | 200 | 2.4154 |
| 2.1936 | 0.77 | 300 | 2.3894 |
| 2.1313 | 1.03 | 400 | 2.3738 |
| 2.0684 | 1.28 | 500 | 2.3643 |
| 2.0603 | 1.54 | 600 | 2.3511 |
| 2.0437 | 1.8 | 700 | 2.3461 |
| 2.0114 | 2.06 | 800 | 2.3412 |
| 1.9976 | 2.31 | 900 | 2.3381 |
| 1.9883 | 2.57 | 1000 | 2.3349 |
| 1.9869 | 2.83 | 1100 | 2.3334 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Word2vec/nlpl_60 | Word2vec | 2023-07-04T15:18:03Z | 0 | 0 | null | [
"word2vec",
"chu",
"dataset:Old_Church_Slavonic_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:34:55Z | ---
language: chu
license: cc-by-4.0
tags:
- word2vec
datasets: Old_Church_Slavonic_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 357 corresponding to 21380 tokens from the dataset `Old_Church_Slavonic_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_60", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/60.zip |
Word2vec/nlpl_57 | Word2vec | 2023-07-04T15:17:08Z | 0 | 0 | null | [
"word2vec",
"lav",
"dataset:Latvian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:33:34Z | ---
language: lav
license: cc-by-4.0
tags:
- word2vec
datasets: Latvian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 560445 corresponding to 289095637 tokens from the dataset `Latvian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_57", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/57.zip |
Word2vec/nlpl_55 | Word2vec | 2023-07-04T15:16:24Z | 0 | 0 | null | [
"word2vec",
"kor",
"dataset:Korean_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:32:24Z | ---
language: kor
license: cc-by-4.0
tags:
- word2vec
datasets: Korean_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 1780757 corresponding to 551643170 tokens from the dataset `Korean_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_55", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/55.zip
|
Word2vec/nlpl_54 | Word2vec | 2023-07-04T15:16:11Z | 0 | 0 | null | [
"word2vec",
"kaz",
"dataset:Kazakh_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:32:08Z | ---
language: kaz
license: cc-by-4.0
tags:
- word2vec
datasets: Kazakh_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 176643 corresponding to 57048825 tokens from the dataset `Kazakh_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_54", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/54.zip |
Word2vec/nlpl_52 | Word2vec | 2023-07-04T15:15:46Z | 0 | 0 | null | [
"word2vec",
"ita",
"dataset:Italian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:28:47Z | ---
language: ita
license: cc-by-4.0
tags:
- word2vec
datasets: Italian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 2469122 corresponding to 5364254134 tokens from the dataset `Italian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_52", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/52.zip
|
Word2vec/nlpl_51 | Word2vec | 2023-07-04T15:15:31Z | 0 | 0 | null | [
"word2vec",
"gle",
"dataset:Irish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:28:40Z | ---
language: gle
license: cc-by-4.0
tags:
- word2vec
datasets: Irish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 87115 corresponding to 25270102 tokens from the dataset `Irish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_51", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/51.zip |
Word2vec/nlpl_50 | Word2vec | 2023-07-04T15:15:03Z | 0 | 0 | null | [
"word2vec",
"ind",
"dataset:Indonesian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:27:36Z | ---
language: ind
license: cc-by-4.0
tags:
- word2vec
datasets: Indonesian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 2899107 corresponding to 5455674387 tokens from the dataset `Indonesian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_50", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/50.zip
|
Word2vec/nlpl_46 | Word2vec | 2023-07-04T15:14:03Z | 0 | 0 | null | [
"word2vec",
"ell",
"dataset:Greek_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:19:22Z | ---
language: ell
license: cc-by-4.0
tags:
- word2vec
datasets: Greek_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 1183194 corresponding to 770507143 tokens from the dataset `Greek_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_46", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/46.zip |
davanstrien/convnext_manuscript_iiif | davanstrien | 2023-07-04T15:13:12Z | 253 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-base-224-22k",
"base_model:finetune:facebook/convnext-base-224-22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- f1
base_model: facebook/convnext-base-224-22k
model-index:
- name: convnext_manuscript_iiif
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. -->
# convnext_manuscript_iiif
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5856
- F1: 0.0037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.5753 | 1.0 | 2038 | 6.4121 | 0.0016 |
| 5.9865 | 2.0 | 4076 | 5.9466 | 0.0021 |
| 5.6521 | 3.0 | 6114 | 5.7645 | 0.0029 |
| 5.3123 | 4.0 | 8152 | 5.6890 | 0.0033 |
| 5.0337 | 5.0 | 10190 | 5.6692 | 0.0034 |
| 4.743 | 6.0 | 12228 | 5.5856 | 0.0037 |
| 4.4387 | 7.0 | 14266 | 5.5969 | 0.0042 |
| 4.1422 | 8.0 | 16304 | 5.6711 | 0.0043 |
| 3.8372 | 9.0 | 18342 | 5.6761 | 0.0044 |
| 3.5244 | 10.0 | 20380 | 5.8469 | 0.0042 |
| 3.2321 | 11.0 | 22418 | 5.8774 | 0.0045 |
| 2.9004 | 12.0 | 24456 | 6.1186 | 0.0047 |
| 2.5937 | 13.0 | 26494 | 6.2398 | 0.0046 |
| 2.2983 | 14.0 | 28532 | 6.3732 | 0.0049 |
| 2.0611 | 15.0 | 30570 | 6.5024 | 0.0045 |
| 1.8153 | 16.0 | 32608 | 6.6585 | 0.0047 |
| 1.6075 | 17.0 | 34646 | 6.8333 | 0.0043 |
| 1.4342 | 18.0 | 36684 | 6.9529 | 0.0044 |
| 1.2614 | 19.0 | 38722 | 7.1129 | 0.0046 |
| 1.1463 | 20.0 | 40760 | 7.1977 | 0.0039 |
| 1.0387 | 21.0 | 42798 | 7.2700 | 0.0044 |
| 0.9635 | 22.0 | 44836 | 7.3375 | 0.0040 |
| 0.8872 | 23.0 | 46874 | 7.4003 | 0.0039 |
| 0.8156 | 24.0 | 48912 | 7.4884 | 0.0039 |
| 0.7544 | 25.0 | 50950 | 7.4764 | 0.0039 |
| 0.6893 | 26.0 | 52988 | 7.5153 | 0.0042 |
| 0.6767 | 27.0 | 55026 | 7.5427 | 0.0043 |
| 0.6098 | 28.0 | 57064 | 7.5547 | 0.0042 |
| 0.5871 | 29.0 | 59102 | 7.5533 | 0.0041 |
| 0.5696 | 30.0 | 61140 | 7.5595 | 0.0041 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Word2vec/nlpl_42 | Word2vec | 2023-07-04T15:12:50Z | 0 | 0 | null | [
"word2vec",
"fin",
"dataset:Finnish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:16:45Z | ---
language: fin
license: cc-by-4.0
tags:
- word2vec
datasets: Finnish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 2433286 corresponding to 1052546686 tokens from the dataset `Finnish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_42", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/42.zip |
Word2vec/nlpl_40 | Word2vec | 2023-07-04T15:12:08Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T12:00:54Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 4027169 corresponding to 9974357994 tokens from the dataset `English_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_40", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/40.zip |
Word2vec/nlpl_36 | Word2vec | 2023-07-04T15:11:04Z | 0 | 0 | null | [
"word2vec",
"hrv",
"dataset:Croatian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T11:57:51Z | ---
language: hrv
license: cc-by-4.0
tags:
- word2vec
datasets: Croatian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 928316 corresponding to 605193347 tokens from the dataset `Croatian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_36", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/36.zip |
Word2vec/nlpl_35 | Word2vec | 2023-07-04T15:10:53Z | 0 | 0 | null | [
"word2vec",
"zho",
"dataset:ChineseT_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T11:57:08Z | ---
language: zho
license: cc-by-4.0
tags:
- word2vec
datasets: ChineseT_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 1935503 corresponding to 1608425218 tokens from the dataset `ChineseT_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_35", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/35.zip |
Word2vec/nlpl_34 | Word2vec | 2023-07-04T15:10:36Z | 0 | 0 | null | [
"word2vec",
"cat",
"dataset:Catalan_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T11:56:51Z | ---
language: cat
license: cc-by-4.0
tags:
- word2vec
datasets: Catalan_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 799020 corresponding to 897648446 tokens from the dataset `Catalan_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_34", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/34.zip |
Word2vec/nlpl_31 | Word2vec | 2023-07-04T15:03:04Z | 0 | 0 | null | [
"word2vec",
"ara",
"dataset:Arabic_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:11:17Z | ---
language: ara
license: cc-by-4.0
tags:
- word2vec
datasets: Arabic_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 1071056 corresponding to 1009356735 tokens from the dataset `Arabic_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_31", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/31.zip |
EllaHong/datamap_polyglot_12.8b_exp1_0704 | EllaHong | 2023-07-04T15:02:50Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-07-04T15:02:42Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Word2vec/nlpl_26 | Word2vec | 2023-07-04T15:01:41Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:10:14Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 209512 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_26", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/26.zip |
Word2vec/nlpl_23 | Word2vec | 2023-07-04T15:00:56Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:09:31Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 228670 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_23", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/23.zip |
Word2vec/nlpl_22 | Word2vec | 2023-07-04T15:00:44Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:09:13Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 291392 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_22", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/22.zip |
Word2vec/nlpl_19 | Word2vec | 2023-07-04T14:58:28Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:08:19Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 260073 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_19", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/19.zip |
Word2vec/nlpl_18 | Word2vec | 2023-07-04T14:58:14Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:08:01Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 291186 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_18", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/18.zip |
Word2vec/nlpl_17 | Word2vec | 2023-07-04T14:57:55Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:07:44Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 259882 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_17", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/17.zip |
snousias/distilbert-base-uncased-finetuned-imdb | snousias | 2023-07-04T14:57:31Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-07-04T14:55:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4742
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7069 | 1.0 | 157 | 2.4947 |
| 2.5792 | 2.0 | 314 | 2.4235 |
| 2.5259 | 3.0 | 471 | 2.4348 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Word2vec/nlpl_15 | Word2vec | 2023-07-04T14:57:15Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:07:11Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 262269 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_15", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/15.zip |
Word2vec/nlpl_14 | Word2vec | 2023-07-04T14:56:57Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:06:53Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 292967 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: no lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_14", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/14.zip
|
Word2vec/nlpl_12 | Word2vec | 2023-07-04T14:56:26Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:06:07Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 292479 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_12", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/12.zip |
Word2vec/nlpl_11 | Word2vec | 2023-07-04T14:56:10Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:05:50Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 261794 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_11", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/11.zip |
Word2vec/nlpl_10 | Word2vec | 2023-07-04T14:55:57Z | 0 | 0 | null | [
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
]
| null | 2023-07-04T10:05:32Z | ---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov ([email protected]) on a vocabulary of size 302815 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_10", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/10.zip |
mcamara/ppo-PyramidsRND1 | mcamara | 2023-07-04T14:50:48Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-07-04T14:50:43Z | ---
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mcamara/ppo-PyramidsRND1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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