modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Akashpb13/xlsr_maltese_wav2vec2
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
| 8
| 2023-04-11T07:06:47Z
|
---
license: apache-2.0
datasets:
- zaemyung/IteraTeR_plus
language:
- en
pipeline_tag: text2text-generation
---
# DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator
This model was obtained by fine-tuning [google/pegasus-large](https://huggingface.co/google/pegasus-large) on [IteraTeR+](https://huggingface.co/datasets/zaemyung/IteraTeR_plus) `multi_sent` dataset.
Paper: [Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks](https://aclanthology.org/2022.emnlp-main.678/) <br>
Authors: Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, and Dongyeop Kang
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")
before_inputs = [
"<bos>These were known as temple rings <coherence>. They</coherence> were worn on the head, near the temples of a woman or a girl.<eos>",
"Andrew Hendy, Hereditary Chief of the Miskitu Nation.<bos> <clarity>Proclaimed</clarity> by the Nicaraguans on the death of his cousin George V, who died on 8th November 1888.<eos> He was repudiated by many people of the Miskitu Nation and abdicated in favour of his cousin Jonathan I, on 8th March 1889. He retired to Nicaraguan territory where he became a Miskitu Jefe Inspector and River Magistrate."
]
model_inputs = tokenizer(before_inputs, return_tensors='pt', padding=True)
model_outputs = model.generate(**model_inputs, num_beams=8, max_length=1024)
after_texts = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)
print(after_texts)
# 'These were known as temple rings because they were worn on the head, near the temples of a woman or a girl.',
# 'Andrew Hendy, Hereditary Chief of the Miskitu Nation. He was proclaimed by the Nicaraguans on the death of his cousin George V, who died on 8th November 1888. He was repudiated by many people of the Miskitu Nation and abdicated in favour of his cousin Jonathan I, on 8th March 1889. He retired to Nicaraguan territory where he became a Miskitu Jefe Inspector and River Magistrate.']
```
|
Akiva/Joke
|
[] | null |
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-kangri
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: bridgeconn/snow-mountain
name: snow-moutain-Kangri
config: Kangri
split: train_500
metrics:
- type: wer
value: 17.40
name: WER
lower_is_better: true
---
<!-- 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. -->
# whisper-large-v2-kangri
This model is a fine-tuned version of [vasista22/whisper-hindi-large-v2](https://huggingface.co/vasista22/whisper-hindi-large-v2) on the [bridgeconn/snow-mountain](https://huggingface.co/datasets/bridgeconn/snow-mountain) dataset for the low resource Indian language- Kangri.
It achieves the following results on the evaluation set:
- Loss: 0.2967
- Wer: 0.1740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0001 | 40.0 | 1000 | 0.2442 | 0.1800 |
| 0.0 | 80.0 | 2000 | 0.2752 | 0.1764 |
| 0.0 | 120.0 | 3000 | 0.2870 | 0.1747 |
| 0.0 | 160.0 | 4000 | 0.2940 | 0.1745 |
| 0.0 | 200.0 | 5000 | 0.2967 | 0.1740 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AkshatSurolia/BEiT-FaceMask-Finetuned
|
[
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
image-classification
|
{
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"BeitForImageClassification"
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}
| 239
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1654
- F1: 0.8590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2845 | 1.0 | 715 | 0.1831 | 0.8249 |
| 0.1449 | 2.0 | 1430 | 0.1643 | 0.8479 |
| 0.0929 | 3.0 | 2145 | 0.1654 | 0.8590 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AlbertHSU/BertTEST
|
[
"pytorch"
] | null |
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}
| 8
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.816703001844709
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3170
- F1: 0.8167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7704 | 1.0 | 96 | 0.3685 | 0.7553 |
| 0.319 | 2.0 | 192 | 0.3247 | 0.7881 |
| 0.2142 | 3.0 | 288 | 0.3170 | 0.8167 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Aleksandar/bert-srb-base-cased-oscar
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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}
| 7
| 2023-04-11T07:46:23Z
|
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1717
- F1: 0.8552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2939 | 1.0 | 715 | 0.1884 | 0.8138 |
| 0.1464 | 2.0 | 1430 | 0.1720 | 0.8469 |
| 0.0939 | 3.0 | 2145 | 0.1717 | 0.8552 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Aleksandar/bert-srb-ner
|
[
"pytorch",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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}
| 4
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization_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. -->
# summarization_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1359
- Rouge1: 0.1813
- Rouge2: 0.1114
- Rougel: 0.1616
- Rougelsum: 0.1617
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.2358 | 1.0 | 1635 | 0.1719 | 0.1758 | 0.1033 | 0.1554 | 0.1554 | 19.0 |
| 0.2043 | 2.0 | 3270 | 0.1574 | 0.1764 | 0.1046 | 0.1561 | 0.1561 | 19.0 |
| 0.191 | 3.0 | 4905 | 0.1505 | 0.1778 | 0.1069 | 0.1577 | 0.1578 | 19.0 |
| 0.178 | 4.0 | 6540 | 0.1448 | 0.1797 | 0.1093 | 0.1597 | 0.1597 | 19.0 |
| 0.1734 | 5.0 | 8175 | 0.1406 | 0.1804 | 0.1102 | 0.1605 | 0.1604 | 19.0 |
| 0.1681 | 6.0 | 9810 | 0.1376 | 0.1811 | 0.111 | 0.1613 | 0.1613 | 19.0 |
| 0.1665 | 7.0 | 11445 | 0.1365 | 0.1815 | 0.1114 | 0.1618 | 0.1618 | 19.0 |
| 0.1643 | 8.0 | 13080 | 0.1359 | 0.1813 | 0.1114 | 0.1616 | 0.1617 | 19.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Aleksandar/electra-srb-ner-setimes
|
[
"pytorch",
"electra",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
"task_specific_params": {
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}
| 6
| null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: hawkeoni/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Aleksandar/electra-srb-ner
|
[
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
{
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"ElectraForTokenClassification"
],
"model_type": "electra",
"task_specific_params": {
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"max_length": null
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}
| 15
| null |
---
language:
- en
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small English
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 en
type: mozilla-foundation/common_voice_11_0
config: en
split: test
args: en
metrics:
- name: Wer
type: wer
value: 12.021334704238024
---
<!-- 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. -->
# Whisper Small English
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3107
- Wer: 12.0213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1577 | 0.06 | 2500 | 0.4077 | 16.2349 |
| 0.2244 | 0.12 | 5000 | 0.3698 | 14.7325 |
| 0.3231 | 0.19 | 7500 | 0.3434 | 13.7448 |
| 0.2536 | 0.25 | 10000 | 0.3406 | 13.4981 |
| 0.2234 | 0.31 | 12500 | 0.3510 | 14.1304 |
| 0.1989 | 0.38 | 15000 | 0.3388 | 13.6394 |
| 0.2449 | 0.44 | 17500 | 0.3394 | 13.4293 |
| 0.2302 | 0.5 | 20000 | 0.3198 | 12.5020 |
| 0.213 | 0.56 | 22500 | 0.3167 | 12.4904 |
| 0.2395 | 0.62 | 25000 | 0.3145 | 12.7533 |
| 0.1152 | 0.69 | 27500 | 0.3181 | 12.6087 |
| 0.0901 | 1.01 | 30000 | 0.3134 | 12.3240 |
| 0.1595 | 1.07 | 32500 | 0.3107 | 12.0213 |
| 0.1249 | 1.13 | 35000 | 0.3131 | 12.0869 |
| 0.1404 | 1.2 | 37500 | 0.3117 | 12.4635 |
| 0.1812 | 1.26 | 40000 | 0.3104 | 12.1415 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.2
|
Aleksandar1932/gpt2-hip-hop
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 8
| null |
---
tags:
- generated_from_trainer
model-index:
- name: lora-alpaca-spanish-30b-v0.2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lora-alpaca-spanish-30b-v0.2
This model was trained from scratch 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 512
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Alexander-Learn/bert-finetuned-squad-accelerate
|
[] | null |
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| 0
| null |
---
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: 271.77 +/- 15.69
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
...
```
|
Alexander-Learn/bert-finetuned-squad
|
[
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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}
| 7
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: chenoi/deepRL5-2
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Alexandru/creative_copilot
|
[] | null |
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}
}
| 0
| null |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
- instruction fine-tuning
model-index:
- name: flan-t5-small-distil-v2
results: []
language:
- en
pipeline_tag: text2text-generation
widget:
- text: >-
how can I become more healthy?
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-T5-61M
[]()
This model is one of our LaMini-LM series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to response to human instructions written in natural language.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text2text-generation', model = checkpoint)
input_prompt = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [t5-small](https://huggingface.co/t5-small) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 61M.
### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
Alireza1044/albert-base-v2-stsb
|
[
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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"AlbertForSequenceClassification"
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}
| 37
| null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice-3
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-finetuned-invoice-3
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0175
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9239
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AmirServi/MyModel
|
[] | null |
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| 0
| 2023-04-11T09:51:37Z
|
---
license: other
---
LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details.
--
license: other
---
# LLaMA Model Card
## Model details
**Organization developing the model**
The FAIR team of Meta AI.
**Model date**
LLaMA was trained between December. 2022 and Feb. 2023.
**Model version**
This is version 1 of the model.
**Model type**
LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters.
**Paper or resources for more information**
More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
**Citations details**
https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/
**License**
Non-commercial bespoke license
**Where to send questions or comments about the model**
Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue.
## Intended use
**Primary intended uses**
The primary use of LLaMA is research on large language models, including:
exploring potential applications such as question answering, natural language understanding or reading comprehension,
understanding capabilities and limitations of current language models, and developing techniques to improve those,
evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations.
**Primary intended users**
The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence.
**Out-of-scope use cases**
LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers.
## Factors
**Relevant factors**
One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model.
**Evaluation factors**
As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model.
## Metrics
**Model performance measures**
We use the following measure to evaluate the model:
- Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs,
- Exact match for question answering,
- The toxicity score from Perspective API on RealToxicityPrompts.
**Decision thresholds**
Not applicable.
**Approaches to uncertainty and variability**
Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training.
## Evaluation datasets
The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs.
## Training dataset
The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing.
## Quantitative analysis
Hyperparameters for the model architecture
<table>
<thead>
<tr>
<th >LLaMA</th> <th colspan=6>Model hyper parameters </th>
</tr>
<tr>
<th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T
</tr>
<tr>
<th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
<tr>
<th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T
</tr>
</tbody>
</table>
*Table 1 - Summary of LLama Model Hyperparameters*
We present our results on eight standard common sense reasoning benchmarks in the table below.
<table>
<thead>
<tr>
<th>LLaMA</th> <th colspan=9>Reasoning tasks </th>
</tr>
<tr>
<th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th>
</tr>
</thead>
<tbody>
<tr>
<th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93
</th>
<tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94
</th>
<tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92
</th>
<tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr>
</tbody>
</table>
*Table 2 - Summary of LLama Model Performance on Reasoning tasks*
We present our results on bias in the table below. Note that lower value is better indicating lower bias.
| No | Category | FAIR LLM |
| --- | -------------------- | -------- |
| 1 | Gender | 70.6 |
| 2 | Religion | 79 |
| 3 | Race/Color | 57 |
| 4 | Sexual orientation | 81 |
| 5 | Age | 70.1 |
| 6 | Nationality | 64.2 |
| 7 | Disability | 66.7 |
| 8 | Physical appearance | 77.8 |
| 9 | Socioeconomic status | 71.5 |
| | LLaMA Average | 66.6 |
*Table 3 - Summary bias of our model output*
## Ethical considerations
**Data**
The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data.
**Human life**
The model is not intended to inform decisions about matters central to human life, and should not be used in such a way.
**Mitigations**
We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier.
**Risks and harms**
Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard.
**Use cases**
LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.
|
Amit29/t5-small-finetuned-xsum
|
[] | null |
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}
| 0
| 2023-04-11T09:58:47Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: gian-cr/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AndrewMcDowell/wav2vec2-xls-r-300m-german-de
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
| 15
| 2023-04-11T10:41:28Z
|
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
This model is a diffusion model for unconditional image generation of shoes
trained on a custom dataset at 128x128 resolution
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Apocalypse-19/shoe-generator')
image = pipeline().images[0]
image
```
|
AnonymousSub/AR_rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"BertModel"
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}
| 5
| null |
เกมออนไลน์ที่ไม่เล่นไม่ได้เลย LUCKY FUN 789 เพราะมีเกมให้เลือกเล่นมากมาย อาทิ บาคาร่า กีฬา สล็อต ยิงปลา เกมโปรด เกมฮอต ป็อกเด้ง ไฮโล หวย แม้กระทั้งเทรดบิทคอยด์ ที่กำลังเป็นที่นิยมในการลงทุน ณ ปัจจุบัน หากท่านเป็นคนที่มีความสนใจการลงทุนที่ครบจบในเว็ปเดียว ก็ไม่ควรจะพลาดที่จะเป็นสมาชิก แล้วท่านจะได้ประสบการณ์ใหม่ อัพเดตการลงทุนในยุคที่ใครช้าถือว่าเอ้าท์ ไม่พลาดโอกาสคว้ากำไรอย่างแน่นอน เพราะเป็นเกมที่สามารถจะทำเงินให้แก่คนที่เล่นนั้นได้อย่างมหาศาลเลยทีเดียว และถ้าเล่นกันดีๆแล้วละก็ อย่างไรเกมนี้ก็เป็นเกมการพนันที่สร้างเงินกันได้ เพราะฉะนั้นแล้วสิ่งที่สำคัญที่สุดที่จะได้เงินคือต้องเล่นเท่านั้นถึงจะรู้ผลได้ว่าจะได้หรือไม่ได้ เพราะไม่งั้นแล้วถ้าไม่เล่นยังไงก็ไม่มีทางได้เงินกันอย่างแน่นอน
<p>► <a href="https://luckyfun789.com/" rel="noopener nofollow">เกมสล็อตออนไลน์</a></p>
LUCKYFUN789
ผู้ให้บริการเกมสล็อตออนไลน์ ครบ จบในเว็บเดียว ด้วยระบบอัตโนมัติ ฝาก-ถอนไว จ่ายจริง ล้ำสมัยด้วยเกมสล็อตออนไลน์รูปแบบใหม่ ที่ LUCKYFUN789
|
AnonymousSub/AR_specter
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
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| 2
| null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v4
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. -->
# gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v4
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 0.1593 | 21.46 | 5000 | 0.0853 |
| 0.0847 | 42.92 | 10000 | 0.1198 |
| 0.0406 | 64.38 | 15000 | 0.0613 |
| 0.0324 | 85.83 | 20000 | 0.0307 |
| 0.0238 | 107.3 | 25000 | 0.0211 |
| 0.0207 | 128.75 | 30000 | 0.0184 |
| 0.0193 | 150.21 | 35000 | 0.0176 |
| 0.0185 | 171.67 | 40000 | 0.0171 |
| 0.018 | 193.13 | 45000 | 0.0170 |
| 0.0177 | 214.59 | 50000 | 0.0167 |
| 0.0174 | 236.05 | 55000 | 0.0167 |
| 0.0172 | 257.51 | 60000 | 0.0166 |
| 0.017 | 278.97 | 65000 | 0.0165 |
| 0.0169 | 300.43 | 70000 | 0.0164 |
| 0.0168 | 321.89 | 75000 | 0.0164 |
| 0.0167 | 343.35 | 80000 | 0.0163 |
| 0.0166 | 364.8 | 85000 | 0.0163 |
| 0.0165 | 386.27 | 90000 | 0.0163 |
| 0.0164 | 407.72 | 95000 | 0.0162 |
| 0.0164 | 429.18 | 100000 | 0.0162 |
| 0.0163 | 450.64 | 105000 | 0.0162 |
| 0.0163 | 472.1 | 110000 | 0.0162 |
| 0.0163 | 493.56 | 115000 | 0.0162 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
AnonymousSub/SR_rule_based_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 2
| null |
---
language:
- he
tags:
- language model
---
## AlephBertGimmel
Modern Hebrew pretrained BERT model with a 128K token vocabulary.
[Checkpoint](https://github.com/Dicta-Israel-Center-for-Text-Analysis/alephbertgimmel/tree/main/alephbertgimmel-small/ckpt_29400--Max128Seq) of the alephbertgimmel-small-128 from [alephbertgimmel](https://github.com/Dicta-Israel-Center-for-Text-Analysis/alephbertgimmel)
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("imvladikon/alephbertgimmel-small-128")
tokenizer = AutoTokenizer.from_pretrained("imvladikon/alephbertgimmel-small-128")
text = "{} היא מטרופולין המהווה את מרכז הכלכלה"
input = tokenizer.encode(text.format("[MASK]"), return_tensors="pt")
mask_token_index = torch.where(input == tokenizer.mask_token_id)[1]
token_logits = model(input).logits
mask_token_logits = token_logits[0, mask_token_index, :]
top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
for token in top_5_tokens:
print(text.format(tokenizer.decode([token])))
# ישראל היא מטרופולין המהווה את מרכז הכלכלה
# ירושלים היא מטרופולין המהווה את מרכז הכלכלה
# חיפה היא מטרופולין המהווה את מרכז הכלכלה
# אילת היא מטרופולין המהווה את מרכז הכלכלה
# אשדוד היא מטרופולין המהווה את מרכז הכלכלה
```
```python
def ppl_naive(text, model, tokenizer):
input = tokenizer.encode(text, return_tensors="pt")
loss = model(input, labels=input)[0]
return torch.exp(loss).item()
text = """{} היא עיר הבירה של מדינת ישראל, והעיר הגדולה ביותר בישראל בגודל האוכלוסייה"""
for word in ["חיפה", "ירושלים", "תל אביב"]:
print(ppl_naive(text.format(word), model, tokenizer))
# 9.825098991394043
# 10.594215393066406
# 9.536449432373047
# I'd expect that for "ירושלים" should be the smallest value, but...
@torch.inference_mode()
def ppl_pseudo(text, model, tokenizer, ignore_idx=-100):
input = tokenizer.encode(text, return_tensors='pt')
mask = torch.ones(input.size(-1) - 1).diag(1)[:-2]
repeat_input = input.repeat(input.size(-1) - 2, 1)
input = repeat_input.masked_fill(mask == 1, tokenizer.mask_token_id)
labels = repeat_input.masked_fill(input != tokenizer.mask_token_id, ignore_idx)
loss = model(input, labels=labels)[0]
return torch.exp(loss).item()
for word in ["חיפה", "ירושלים", "תל אביב"]:
print(ppl_pseudo(text.format(word), model, tokenizer))
# 4.346900939941406
# 3.292382001876831
# 2.732590913772583
```
When using AlephBertGimmel, please reference:
```bibtex
@misc{guetta2022large,
title={Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All},
author={Eylon Guetta and Avi Shmidman and Shaltiel Shmidman and Cheyn Shmuel Shmidman and Joshua Guedalia and Moshe Koppel and Dan Bareket and Amit Seker and Reut Tsarfaty},
year={2022},
eprint={2211.15199},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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}
| 2
| null |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-8x8-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.48 +/- 0.50
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** created by Apocalypse-19.
## Usage
```python
model = load_from_hub(repo_id="Apocalypse-19/q-FrozenLake-8x8-Slippery", 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"])
```
|
AnonymousSub/SR_rule_based_roberta_hier_triplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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},
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}
| 1
| null |
---
language: ja
license: cc-by-sa-4.0
library_name: transformers
tags:
- bert
- fill-mask
datasets:
- wikipedia
mask_token: "[MASK]"
widget:
- text: "京都 大学 で [MASK] を 専攻 する 。"
- text: "東京 は 日本 の [MASK] だ 。"
- text: "カフェ で [MASK] を 注文 する 。"
---
# ku-accms/bert-base-japanese-ssuw
## Model description
This is a pre-trained Japanese BERT base model for super short unit words (SSUW).
## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea).
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='ku-accms/bert-base-japanese-ssuw')
>>> unmasker("京都 大学 で [MASK] を 専攻 する 。")
[{'sequence': '京都 大学 で 文学 を 専攻 する 。',
'score': '0.1464807540178299',
'token': '14603',
'token_str': '文学'}
{'sequence': '京都 大学 で 哲学 を 専攻 する 。',
'score': '0.08064978569746017',
'token': '15917',
'token_str': '哲学'}
{'sequence': '京都 大学 で 演劇 を 専攻 する 。',
'score': '0.0800977498292923',
'token': '16772',
'token_str': '演劇'}
{'sequence': '京都 大学 で 法学 を 専攻 する 。',
'score': '0.04579947143793106',
'token': '16255',
'token_str': '法学'}
{'sequence': '京都 大学 で 英語 を 専攻 する 。',
'score': '0.045536939054727554',
'token': '14592',
'token_str': '英語'}
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
import zenhan
import Mykytea
kytea_model_path = "somewhere"
kytea = Mykytea.Mykytea("-model {} -notags".format(kytea_model_path))
def preprocess(text):
return " ".join(kytea.getWS(zenhan.h2z(text)))
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('ku-accms/bert-base-japanese-ssuw')
model = BertModel.from_pretrained("ku-accms/bert-base-japanese-ssuw")
text = "京都大学で自然言語処理を専攻する。"
encoded_input = tokenizer(preprocess(text), return_tensors='pt')
output = model(**encoded_input)
```
## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB).
## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using [transformers](https://github.com/huggingface/transformers) library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.
The following hyperparameters were used for the pre-training.
- learning_rate: 2e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
}
| 8
| null |
---
language: ja
license: cc-by-sa-4.0
library_name: transformers
tags:
- roberta
- fill-mask
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "京都 大学 で [MASK] を 専攻 する 。"
- text: "東京 は 日本 の [MASK] だ 。"
- text: "カフェ で [MASK] を 注文 する 。"
- text: "[MASK] 名人 が タイトル の 防衛 に 成功 する 。"
---
# ku-accms/roberta-base-japanese-ssuw
## Model description
This is a pre-trained Japanese RoBERTa base model for super short unit words (SSUW).
## Pre-processing
The input text should be converted to full-width (zenkaku) characters and segmented into super short unit words in advance (e.g., by KyTea).
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='ku-accms/roberta-base-japanese-ssuw')
>>> unmasker("京都 大学 で [MASK] を 専攻 する 。")
[{'sequence': '京都 大学 で 文学 を 専攻 する 。',
'score': '0.1479644924402237',
'token': '17907',
'token_str': '文学'}
{'sequence': '京都 大学 で 哲学 を 専攻 する 。',
'score': '0.07658644765615463',
'token': '19302',
'token_str': '哲学'}
{'sequence': '京都 大学 で デザイン を 専攻 する 。',
'score': '0.06302948296070099',
'token': '14411',
'token_str': 'デザイン'}
{'sequence': '京都 大学 で 建築 を 専攻 する 。',
'score': '0.060596249997615814',
'token': '15478',
'token_str': '建築'}
{'sequence': '京都 大学 で 工学 を 専攻 する 。',
'score': '0.0574776753783226',
'token': '18632',
'token_str': '工学'}
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
import zenhan
import Mykytea
kytea_model_path = "somewhere"
kytea = Mykytea.Mykytea("-model {} -notags".format(kytea_model_path))
def preprocess(text):
return " ".join(kytea.getWS(zenhan.h2z(text)))
from transformers import BertTokenizer, RobertaModel
tokenizer = BertTokenizer.from_pretrained('ku-accms/roberta-base-japanese-ssuw')
model = RobertaModel.from_pretrained("ku-accms/roberta-base-japanese-ssuw")
text = "京都大学で自然言語処理を専攻する。"
encoded_input = tokenizer(preprocess(text), return_tensors='pt')
output = model(**encoded_input)
```
## Training data
We used a Japanese Wikipedia dump (as of 20230101, 3.3GB) and a Japanese portion of CC100 (70GB).
## Training procedure
We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the RoBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took about 7 days using 4 NVIDIA A100-SXM4-80GB GPUs.
The following hyperparameters were used for the pre-training.
- learning_rate: 1e-4
- weight decay: 1e-2
- per_device_train_batch_size: 80
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 960
- max_seq_length: 512
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500,000
- warmup_steps: 10,000
|
AnonymousSub/SR_rule_based_roberta_only_classfn_twostage_epochs_1_shard_10
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"RobertaModel"
],
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}
}
}
| 4
| null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Ninetail Dreambooth model trained by blazers with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 3
| null |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-8x8-Slippery-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.57 +/- 0.50
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** created by Apocalypse-19.
## Usage
```python
model = load_from_hub(repo_id="Apocalypse-19/q-FrozenLake-8x8-Slippery-v2", 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"])
```
|
AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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}
}
}
| 2
| null |
---
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: 208.69 +/- 71.29
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
...
```
|
AnonymousSub/SR_rule_based_twostagetriplet_epochs_1_shard_1
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
}
| 2
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: chenoi/deepRL7
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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}
}
}
| 4
| null |
Access to model PrathameshPawar/bart_raw is restricted and you are not in the authorized list. Visit https://huggingface.co/PrathameshPawar/bart_raw to ask for access.
|
AnonymousSub/cline-emanuals-s10-AR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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}
}
}
| 27
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5552523874488404
- name: Recall
type: recall
value: 0.37720111214087115
- name: F1
type: f1
value: 0.44922737306843263
- name: Accuracy
type: accuracy
value: 0.9469454063528707
---
<!-- 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. -->
# ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2942
- Precision: 0.5553
- Recall: 0.3772
- F1: 0.4492
- Accuracy: 0.9469
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2666 | 0.6024 | 0.2808 | 0.3831 | 0.9405 |
| No log | 2.0 | 426 | 0.2605 | 0.5708 | 0.3364 | 0.4233 | 0.9456 |
| 0.1299 | 3.0 | 639 | 0.2827 | 0.5658 | 0.3346 | 0.4205 | 0.9452 |
| 0.1299 | 4.0 | 852 | 0.2836 | 0.5503 | 0.3753 | 0.4463 | 0.9469 |
| 0.051 | 5.0 | 1065 | 0.2942 | 0.5553 | 0.3772 | 0.4492 | 0.9469 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/cline-s10-SR
|
[] | null |
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}
| 0
| null |
---
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: 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
|
AnonymousSub/cline-techqa
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
}
| 6
| null |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4859
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7003 | 0.54 | 500 | 1.4859 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 2.0.0
- Tokenizers 0.10.3
|
AnonymousSub/cline_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 8
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -135.57 +/- 57.14
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'chenoi/deepRL8-1'
'batch_size': 512
'minibatch_size': 128}
```
|
AnonymousSub/consert-s10-AR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
}
| 31
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: infatum/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/consert-s10-SR
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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"max_length": null
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}
| 28
| null |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-stats-extract
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-stats-extract
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3450
- Rouge1: 62.188
- Rouge2: 51.5988
- Rougel: 55.8383
- Rougelsum: 58.4919
- Gen Len: 90.4286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 4 | 1.0447 | 51.2166 | 37.2933 | 44.8635 | 47.5954 | 74.0 |
| No log | 2.0 | 8 | 0.5919 | 55.0964 | 43.0158 | 49.4166 | 51.4412 | 92.2857 |
| No log | 3.0 | 12 | 0.4159 | 60.2619 | 48.694 | 54.0969 | 54.9467 | 95.1429 |
| No log | 4.0 | 16 | 0.3450 | 62.188 | 51.5988 | 55.8383 | 58.4919 | 90.4286 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/declutr-emanuals-s10-SR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 28
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert_ai
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_ai
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0761
- Accuracy: 0.9913
- F1: 0.9913
- Precision: 0.9833
- Recall: 0.9995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0358 | 1.0 | 6059 | 0.0390 | 0.9923 | 0.9923 | 0.9859 | 0.9989 |
| 0.0187 | 2.0 | 12118 | 0.0738 | 0.9884 | 0.9884 | 0.9779 | 0.9993 |
| 0.0056 | 3.0 | 18177 | 0.0761 | 0.9913 | 0.9913 | 0.9833 | 0.9995 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/declutr-model-emanuals
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 4
| 2023-04-11T15:01:48Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.03 +/- 19.66
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
...
```
|
AnonymousSub/declutr-model
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 4
| 2023-04-11T15:02:33Z
|
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: justinsiow/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/declutr-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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}
| 4
| null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Find your model_id: ongknsro/dogdog02-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/declutr-s10-AR
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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}
| 26
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert_human
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_human
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0451
- Accuracy: 0.9930
- F1: 0.9930
- Precision: 0.9923
- Recall: 0.9921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.062 | 1.0 | 5488 | 0.0409 | 0.9914 | 0.9914 | 0.9924 | 0.9885 |
| 0.0279 | 2.0 | 10976 | 0.0414 | 0.9925 | 0.9925 | 0.9923 | 0.9909 |
| 0.008 | 3.0 | 16464 | 0.0451 | 0.9930 | 0.9930 | 0.9923 | 0.9921 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/dummy_1
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
| 33
| null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased_finetuned_docvqa
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. -->
# layoutlmv2-base-uncased_finetuned_docvqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.4213 | 0.22 | 50 | 4.6420 |
| 4.513 | 0.44 | 100 | 4.3167 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AnonymousSub/hier_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 8
| null |
---
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="Luksal/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"])
```
|
AnonymousSub/roberta-base_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
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| 25
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: spanish-spellchecker-t5-base-wikitest1000
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. -->
# spanish-spellchecker-t5-base-wikitest1000
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943
- Bleu: 0.0
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|
| No log | 1.0 | 52 | 0.4171 | 0.0 | 19.0 |
| No log | 2.0 | 104 | 0.3522 | 0.0 | 19.0 |
| No log | 3.0 | 156 | 0.3275 | 0.0 | 19.0 |
| No log | 4.0 | 208 | 0.3160 | 0.0 | 19.0 |
| No log | 5.0 | 260 | 0.3064 | 0.0 | 19.0 |
| No log | 6.0 | 312 | 0.3012 | 0.0 | 19.0 |
| No log | 7.0 | 364 | 0.2987 | 0.0 | 19.0 |
| No log | 8.0 | 416 | 0.2959 | 0.0 | 19.0 |
| No log | 9.0 | 468 | 0.2947 | 0.0 | 19.0 |
| 0.5085 | 10.0 | 520 | 0.2943 | 0.0 | 19.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
|
AnonymousSub/rule_based_bert_quadruplet_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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}
| 33
| 2023-04-11T15:31:19Z
|
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Luksal/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_10
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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| 4
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 19.76 +/- 2.52
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r chenoi/deepRL8-2-last
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .home.chenoi1.miniconda3.envs.last.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=deepRL8-2-last
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .home.chenoi1.miniconda3.envs.rl.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=deepRL8-2-last --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ashagi/Ashvx
|
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}
| 0
| 2023-04-11T20:05:34Z
|
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/multiplenegatives-e5-base-unsupervised-500-34358c
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('rithwik-db/multiplenegatives-e5-base-unsupervised-500-34358c')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/multiplenegatives-e5-base-unsupervised-500-34358c')
model = AutoModel.from_pretrained('rithwik-db/multiplenegatives-e5-base-unsupervised-500-34358c')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/multiplenegatives-e5-base-unsupervised-500-34358c)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 298 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Atchuth/DialoGPT-small-MBOT
|
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| 0
| null |
---
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: 545.00 +/- 120.79
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 AMI0x -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 AMI0x -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 AMI0x
```
## 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)])
```
|
Augustvember/wokka5
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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}
| 11
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.69
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="yingzhi/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Augustvember/your-model-name
|
[] | null |
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}
| 0
| null |
---
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="vinaysatish/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"])
```
|
Axon/resnet34-v1
|
[
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | null |
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| 0
| null |
---
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: 258.14 +/- 25.99
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
...
```
|
Ayato/DialoGTP-large-Yuri
|
[] | null |
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| 0
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: dyingc/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayham/albert_distilgpt2_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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"EncoderDecoderModel"
],
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}
| 9
| null |
---
language:
- de
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-fine-tuned-de_learn
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: de
split: validation[9500:14300]
args: 'config: german, split: test'
metrics:
- name: Wer
type: wer
value: 14.329741524756484
---
<!-- 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. -->
# whisper-fine-tuned-de_learn
This model is a fine-tuned version of [whisper-fine-tuned-de_arg_new](https://huggingface.co/whisper-fine-tuned-de_arg_new) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3423
- Wer: 14.3297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2689 | 0.67 | 1000 | 0.3094 | 15.5378 |
| 0.1072 | 1.33 | 2000 | 0.3068 | 15.0653 |
| 0.1134 | 2.0 | 3000 | 0.2991 | 14.5704 |
| 0.0437 | 2.67 | 4000 | 0.3166 | 14.8876 |
| 0.0163 | 3.33 | 5000 | 0.3308 | 14.4940 |
| 0.0118 | 4.0 | 6000 | 0.3314 | 14.3882 |
| 0.0052 | 4.67 | 7000 | 0.3399 | 14.2915 |
| 0.0032 | 5.33 | 8000 | 0.3423 | 14.3297 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ayham/albert_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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"EncoderDecoderModel"
],
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}
| 6
| null |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: rlucasz93/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Ayham/distilbert_gpt2_summarization_xsum
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
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"EncoderDecoderModel"
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}
| 8
| null |
---
tags:
- generated_from_trainer
model-index:
- name: finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned
This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6485
- Cer: 0.0565
## 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
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1738 | 0.28 | 200 | 1.2406 | 0.2262 |
| 0.9181 | 0.57 | 400 | 0.8294 | 0.1084 |
| 0.6552 | 0.85 | 600 | 0.6485 | 0.0565 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Ayham/xlmroberta_large_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
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}
| 12
| null |
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-kannada-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-kannada-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
```
|
Ayham/xlnet_bert_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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"max_length": null
},
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}
}
}
| 7
| null |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_cardb
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9584607543
- name: NER Recall
type: recall
value: 0.9673353909
- name: NER F Score
type: f_score
value: 0.9628776242
---
| Feature | Description |
| --- | --- |
| **Name** | `en_cardb` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.4,<3.5.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `antineoplastic`, `cancertype`, `carcinogen` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 96.29 |
| `ENTS_P` | 95.85 |
| `ENTS_R` | 96.73 |
| `TRANSFORMER_LOSS` | 63857.68 |
| `NER_LOSS` | 42226.48 |
|
Ayumi/Jovana
|
[] | null |
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}
| 0
| null |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- anime
- aiart
---
**This model is trained for 12 characters from Kuma Kuma Kuma Bear (くまクマ熊ベアー) + 5 characters from Saving 80,000 Gold in Another World for My Retirement (老後に備えて異世界で8万枚の金貨を貯めます)**
**Why do I train the two animes together?**
I feel these two animes (light novels actually) have so much similarity that I really want to make some crossovers.
For examples please see
https://civitai.com/models/37632/kumabear-roukin8-characters-fullckpt
Moreover there is no reason to do single anime either. I plan to add __shinmai renkinjutsushi no tenpo keiei__ next.
## Trigger Words
**KumaBear**
* Atla
* Cliff
* Eleanora
* Fina
* Flora
* Gentz
* Misana
* Noire
* Shia
* Shuri
* Telmina
* Yuna
**Roukin8**
* Adelaide
* Beatrice
* Colette
* Sabine
* YamanoMitsuha
**Styles** (may not be very effective)
* aniscreen
* fanart
* light novel
* official art
* ..., style(s) of your favorite model if know how to merge things properly
---
To get everything right you may need additional trigger words for outfits and ornaments. Here are some suggestions
- If you want to get the bear costume of Yuna you may add kigurumi, bear hood, animal hood, animal costume, hand puppet etc.
- Add Red bow for Fina/Shuri/Noire
- Add twin drill for Shia
- Add double bun for Flora
- Add scrunchie for telmina
Kumakyuu and Kumayuru are not tagged, but you may get something that look right by prompting with bears, stuffed animal etc.
Interestingly I can hardly take off the hood of Yuna during the early phase of training, but it becomes possible after longer training (actually now Yuna by default does not have hood though almost all the images of her have hood on!)
Many characters are missing from the two animes. I may update the KumaBear one at the end of the season with the following characters
* kumakyuu
* kumayuru
* Lurina
* Farrat (king)
* Kitia (queen)
* Karin
* Sanya
* Helen
* Ans
* Mylene
* Cattleya
## Dataset
* KumaBear 5113
* anime screenshots 5042
* fanart 37
* official art 15
* novel illustration 19
* Roukin8 2948 (screenshots only)
* Regularization ~30K
## Training
* First trained for 9739 steps, resumed and trained for another 20494 steps
* clip skip 1, resolution 512, batch size 8, on top of [JosephusCheung/ACertainty](https://huggingface.co/JosephusCheung/ACertainty/tree/main)
* 2.5e-6 cosine scheduler, Adam8bit, conditional dropout 0.08
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
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"ElectraForQuestionAnswering"
],
"model_type": "electra",
"task_specific_params": {
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}
| 12
| null |
---
license: mit
base_model: CompVis/stable-diffusion-v1-4
---
|
Azaghast/DistilBART-SCP-ParaSummarization
|
[
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngram_size": 3,
"num_beams": 4,
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}
| 8
| null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# rithwik-db/cleaned-e5-base-unsupervised-16
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('rithwik-db/cleaned-e5-base-unsupervised-16')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rithwik-db/cleaned-e5-base-unsupervised-16')
model = AutoModel.from_pretrained('rithwik-db/cleaned-e5-base-unsupervised-16')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/cleaned-e5-base-unsupervised-16)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 149 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Azura/data
|
[] | null |
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}
| 0
| null |
Access to model meltano/singer-sdk is restricted and you are not in the authorized list. Visit https://huggingface.co/meltano/singer-sdk to ask for access.
|
BSC-LT/roberta-base-bne-capitel-pos
|
[
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"pos",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
| 14
| null |
---
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="Yanrds/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"])
```
|
BSC-LT/roberta-base-bne-sqac
|
[
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
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"prefix": null
},
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"max_length": null
},
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},
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},
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"early_stopping": null,
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}
}
}
| 10
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Yanrds/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BSC-LT/roberta-base-bne
|
[
"pytorch",
"roberta",
"fill-mask",
"es",
"dataset:bne",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 594
| 2023-04-12T01:15:27Z
|
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# hlyu/distilbert_tasb_14
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('hlyu/distilbert_tasb_14')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hlyu/distilbert_tasb_14')
model = AutoModel.from_pretrained('hlyu/distilbert_tasb_14')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=hlyu/distilbert_tasb_14)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 5055 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 0.0001
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
BSC-LT/roberta-large-bne-capitel-ner
|
[
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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"RobertaForTokenClassification"
],
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}
| 5
| 2023-04-12T01:18:54Z
|
---
license: mit
---
## Paella
We are releasing a new Paella model which builds on top of our initial paper https://arxiv.org/abs/2211.07292.
Paella is a text-to-image model that works in a quantized latent space and learns similarly to MUSE and Diffusion models.
Since the paper-release we worked intensively to bring Paella to a similar level as other
state-of-the-art models. With this release we are coming a step closer to that goal. However, our main intention is not
to make the greatest text-to-image model out there (at least for now), it is to bring text-to-image models closer
to people outside the field on a technical basis. For example, many models have codebases with many thousand lines of
code, that make it pretty hard for people to dive into the code and easily understand it. And that is the contribution
we are the most with Paella. The training and sampling code for Paella is minimalistic and can be understood in a few
minutes, making further extensions, quick tests, idea testing etc. extremely fast. For instance, the entire
sampling code can be written in just **12 lines** of code.
### How does Paella work?
Paella works in a quantized latent space, just like StableDiffusion etc., to reduce the computational power needed.
Images will be encoded to a smaller latent space and converted to visual tokens of shape *h x w*. Now during training,
these visual tokens will be noised, by replacing a random amount of tokens with other randomly selected tokens
from the codebook of the VQGAN. The noised image will be given to the model, along with a timestep and the conditional
information, which is text in our case. The model is tasked to predict the un-noised version of the tokens.
And that's it. The model is optimized with the CrossEntropy loss between the original tokens and the predicted tokens.
The amount of noise added during the training is just a linear schedule, meaning that we uniformly sample a percentage
between 0 and 100% and noise that amount of tokens.<br><br>
<figure>
<img src="https://user-images.githubusercontent.com/61938694/231248435-d21170c1-57b4-4a8f-90a6-62cf3e7effcd.png" width="400">
<figcaption>Images are noised and then fed to the model during training.</figcaption>
</figure>
Sampling is also extremely simple, we start with the entire image being random tokens. Then we feed the latent image,
the timestep and the condition into the model and let it predict the final image. The models outputs a distribution
over every token, which we sample from with standard multinomial sampling.
Since there are infinite possibilities for the result to look like, just doing a single step results in very basic
shapes without any details. That is why we add noise to the image again and feed it back to the model. And we repeat
that process for a number of times with less noise being added every time and slowly get our final image.
You can see how images emerge [here](https://user-images.githubusercontent.com/61938694/231252449-d9ac4d15-15ef-4aed-a0de-91fa8746a415.png).<br>
The following is the entire sampling code needed to generate images:
```python
def sample(model_inputs, latent_shape, unconditional_inputs, steps=12, renoise_steps=11, temperature=(0.7, 0.3), cfg=8.0):
with torch.inference_mode():
sampled = torch.randint(0, model.num_labels, size=latent_shape)
initial_noise = sampled.clone()
timesteps = torch.linspace(1.0, 0.0, steps+1)
temperatures = torch.linspace(temperature[0], temperature[1], steps)
for i, t in enumerate(timesteps[:steps]):
t = torch.ones(latent_shape[0]) * t
logits = model(sampled, t, **model_inputs)
if cfg:
logits = logits * cfg + model(sampled, t, **unconditional_inputs) * (1-cfg)
sampled = logits.div(temperatures[i]).softmax(dim=1).permute(0, 2, 3, 1).reshape(-1, logits.size(1))
sampled = torch.multinomial(sampled, 1)[:, 0].view(logits.size(0), *logits.shape[2:])
if i < renoise_steps:
t_next = torch.ones(latent_shape[0]) * timesteps[i+1]
sampled = model.add_noise(sampled, t_next, random_x=initial_noise)[0]
return sampled
```
### Results
<img src="https://user-images.githubusercontent.com/61938694/231598512-2410c172-5a9d-43f4-947c-6ff7eaee77e7.png">
Since Paella is also conditioned on CLIP image embeddings the following things are also possible:<br><br>
<img src="https://user-images.githubusercontent.com/61938694/231278319-16551a8d-bfd1-49c9-b604-c6da3955a6d4.png">
<img src="https://user-images.githubusercontent.com/61938694/231287637-acd0b9b2-90c7-4518-9b9e-d7edefc6c3af.png">
<img src="https://user-images.githubusercontent.com/61938694/231287119-42fe496b-e737-4dc5-8e53-613bdba149da.png">
### Technical Details.
Model-Architecture: U-Net (Mix of....) <br>
Dataset: Laion-A, Laion Aesthetic > 6.0 <br>
Training Steps: 1.3M <br>
Batch Size: 2048 <br>
Resolution: 256 <br>
VQGAN Compression: f4 <br>
Condition: ByT5-XL (95%), CLIP-H Image Embedding (10%), CLIP-H Text Embedding (10%)
Optimizer: AdamW
Hardware: 128 A100 @ 80GB <br>
Training Time: ~3 weeks <br>
Learning Rate: 1e-4 <br>
More details on the approach, training and sampling can be found in paper and on GitHub.
### Paper, Code Release
Paper: https://arxiv.org/abs/2211.07292 <br>
Code: https://github.com/dome272/Paella <br>
### Goal
So you see, there are no heavy math formulas or theorems needed to achieve good sampling qualities. Moreover,
there are no constants such as alpha, beta, alpha_cum_prod etc. necessary as in diffusion models. This makes this
method really suitable for people new to the field of generative AI. We hope we can set the foundation for further
research in that direction and hope to contribute to a world where AI is accessible and can be understood by everyone.
### Limitations & Conclusion
There are still many things to improve for Paella to get on par with standard diffusion models or to even outperform
them. One primary thing we notice is that even though we only condition the model on CLIP image embedding 10% of the
time, during inference the model heavily relies on the generated image embeddings by a prior model (mapping clip text
embeddings to image embeddings as proposed in Dalle2). We counteract this by decreasing the importance of the image
embeddings by reweighing the attention scores. There probably is a way to avoid this happening already in training.
Other limitations such as lack of composition, text depiction, unawareness of concepts etc. could also be reduced by
continuing the training for longer. As a reference, Paella has only seen as many images as SD 1.4 and due to earlier
To conclude, this is still work in progress, but our first model that works a million times better than the first
versions we trained months ago. We hope that more people become interested in this approach, since we believe it has
a lot of potential to become much better than this and to enable new people to have an easy-to-understand introduction
to the field of generative AI.
|
BSen/wav2vec2-base-timit-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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}
| 4
| 2023-04-12T01:24:28Z
|
---
license: mit
tags:
- generated_from_trainer
datasets:
- ricardo-filho/tcm-0.9-no-valor-objeto
model-index:
- name: bert_base_tcm_0.9_no_valor_objeto
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_0.9_no_valor_objeto
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the ricardo-filho/tcm-0.9-no-valor-objeto dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0176
- Criterio Julgamento Precision: 0.8310
- Criterio Julgamento Recall: 0.8310
- Criterio Julgamento F1: 0.8310
- Criterio Julgamento Number: 142
- Data Sessao Precision: 0.7909
- Data Sessao Recall: 0.9667
- Data Sessao F1: 0.87
- Data Sessao Number: 90
- Modalidade Licitacao Precision: 0.9564
- Modalidade Licitacao Recall: 0.9815
- Modalidade Licitacao F1: 0.9688
- Modalidade Licitacao Number: 648
- Numero Exercicio Precision: 0.9362
- Numero Exercicio Recall: 0.9788
- Numero Exercicio F1: 0.9570
- Numero Exercicio Number: 330
- Objeto Licitacao Precision: 0.4460
- Objeto Licitacao Recall: 0.5849
- Objeto Licitacao F1: 0.5061
- Objeto Licitacao Number: 106
- Overall Precision: 0.8751
- Overall Recall: 0.9316
- Overall F1: 0.9025
- Overall Accuracy: 0.9953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0159 | 1.0 | 3497 | 0.0176 | 0.8310 | 0.8310 | 0.8310 | 142 | 0.7909 | 0.9667 | 0.87 | 90 | 0.9564 | 0.9815 | 0.9688 | 648 | 0.9362 | 0.9788 | 0.9570 | 330 | 0.4460 | 0.5849 | 0.5061 | 106 | 0.8751 | 0.9316 | 0.9025 | 0.9953 |
| 0.0161 | 2.0 | 6994 | 0.0191 | 0.8312 | 0.9014 | 0.8649 | 142 | 0.7890 | 0.9556 | 0.8643 | 90 | 0.9580 | 0.9846 | 0.9711 | 648 | 0.9475 | 0.9848 | 0.9658 | 330 | 0.5556 | 0.6604 | 0.6034 | 106 | 0.8920 | 0.9476 | 0.9189 | 0.9954 |
| 0.0094 | 3.0 | 10491 | 0.0215 | 0.8125 | 0.9155 | 0.8609 | 142 | 0.7818 | 0.9556 | 0.86 | 90 | 0.9608 | 0.9846 | 0.9726 | 648 | 0.9503 | 0.9848 | 0.9673 | 330 | 0.5108 | 0.6698 | 0.5796 | 106 | 0.8834 | 0.9498 | 0.9154 | 0.9955 |
| 0.0057 | 4.0 | 13988 | 0.0212 | 0.8269 | 0.9085 | 0.8658 | 142 | 0.8095 | 0.9444 | 0.8718 | 90 | 0.9697 | 0.9861 | 0.9778 | 648 | 0.9501 | 0.9818 | 0.9657 | 330 | 0.5290 | 0.6887 | 0.5984 | 106 | 0.8935 | 0.9498 | 0.9208 | 0.9960 |
| 0.0049 | 5.0 | 17485 | 0.0214 | 0.8344 | 0.9225 | 0.8763 | 142 | 0.7905 | 0.9222 | 0.8513 | 90 | 0.9652 | 0.9830 | 0.9740 | 648 | 0.9474 | 0.9818 | 0.9643 | 330 | 0.5217 | 0.6792 | 0.5902 | 106 | 0.8894 | 0.9476 | 0.9176 | 0.9957 |
| 0.0036 | 6.0 | 20982 | 0.0297 | 0.8397 | 0.9225 | 0.8792 | 142 | 0.7748 | 0.9556 | 0.8557 | 90 | 0.9636 | 0.9799 | 0.9717 | 648 | 0.9585 | 0.9788 | 0.9685 | 330 | 0.5435 | 0.7075 | 0.6148 | 106 | 0.8922 | 0.9498 | 0.9201 | 0.9953 |
| 0.0016 | 7.0 | 24479 | 0.0297 | 0.8302 | 0.9296 | 0.8771 | 142 | 0.7925 | 0.9333 | 0.8571 | 90 | 0.9652 | 0.9830 | 0.9740 | 648 | 0.9467 | 0.9697 | 0.9581 | 330 | 0.5746 | 0.7264 | 0.6417 | 106 | 0.8948 | 0.9498 | 0.9215 | 0.9955 |
| 0.0016 | 8.0 | 27976 | 0.0298 | 0.8212 | 0.8732 | 0.8464 | 142 | 0.8095 | 0.9444 | 0.8718 | 90 | 0.9666 | 0.9815 | 0.9740 | 648 | 0.9524 | 0.9697 | 0.9610 | 330 | 0.5746 | 0.7264 | 0.6417 | 106 | 0.8974 | 0.9438 | 0.9200 | 0.9955 |
| 0.0011 | 9.0 | 31473 | 0.0319 | 0.7949 | 0.8732 | 0.8322 | 142 | 0.7788 | 0.9 | 0.8351 | 90 | 0.9650 | 0.9799 | 0.9724 | 648 | 0.9467 | 0.9697 | 0.9581 | 330 | 0.6016 | 0.7264 | 0.6581 | 106 | 0.8938 | 0.9400 | 0.9163 | 0.9954 |
| 0.0011 | 10.0 | 34970 | 0.0324 | 0.8141 | 0.8944 | 0.8523 | 142 | 0.7524 | 0.8778 | 0.8103 | 90 | 0.9680 | 0.9815 | 0.9747 | 648 | 0.9494 | 0.9667 | 0.9580 | 330 | 0.5878 | 0.7264 | 0.6498 | 106 | 0.8939 | 0.9407 | 0.9167 | 0.9954 |
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Babelscape/wikineural-multilingual-ner
|
[
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"multilingual",
"dataset:Babelscape/wikineural",
"transformers",
"named-entity-recognition",
"sequence-tagger-model",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
] |
token-classification
|
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| 41,608
| 2023-04-12T01:29:08Z
|
---
pipeline_tag: image-classification
tags:
- art
---
Models for [Sketch2Image](https://github.com/GreeneryScenery/Sketch2Image).
|
Bagus/wav2vec2-large-xlsr-bahasa-indonesia
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
],
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}
| 12
| 2023-04-12T01:41:02Z
|
---
tags:
- generated_from_keras_callback
model-index:
- name: pegasus-large-musicLyrics
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pegasus-large-musicLyrics
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0472
- Train Sparse Categorical Accuracy: 0.7971
- Validation Loss: 1.0387
- Validation Sparse Categorical Accuracy: 0.7992
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 1.4713 | 0.7524 | 1.0652 | 0.7960 | 0 |
| 1.0824 | 0.7934 | 1.0482 | 0.7982 | 1 |
| 1.0472 | 0.7971 | 1.0387 | 0.7992 | 2 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28
|
[
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
{
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
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}
| 18
| 2023-04-12T02:31:56Z
|
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-wikiann-zh-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: zh
split: validation
args: zh
metrics:
- name: Precision
type: precision
value: 0.7890612756621219
- name: Recall
type: recall
value: 0.8060513887777155
- name: F1
type: f1
value: 0.797465848346862
- name: Accuracy
type: accuracy
value: 0.9432393178410795
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wikiann-zh-ner
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2092
- Precision: 0.7891
- Recall: 0.8061
- F1: 0.7975
- Accuracy: 0.9432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.842 | 0.16 | 400 | 0.3530 | 0.5535 | 0.6872 | 0.6131 | 0.8927 |
| 0.32 | 0.32 | 800 | 0.2800 | 0.6929 | 0.6749 | 0.6838 | 0.9190 |
| 0.2928 | 0.48 | 1200 | 0.2438 | 0.7031 | 0.7661 | 0.7333 | 0.9301 |
| 0.245 | 0.64 | 1600 | 0.2525 | 0.6959 | 0.7919 | 0.7408 | 0.9280 |
| 0.2236 | 0.8 | 2000 | 0.2315 | 0.7441 | 0.7503 | 0.7472 | 0.9342 |
| 0.2444 | 0.96 | 2400 | 0.2119 | 0.7719 | 0.7675 | 0.7697 | 0.9379 |
| 0.1899 | 1.12 | 2800 | 0.2267 | 0.7531 | 0.8062 | 0.7788 | 0.9387 |
| 0.1649 | 1.28 | 3200 | 0.2249 | 0.7519 | 0.8202 | 0.7846 | 0.9395 |
| 0.1521 | 1.44 | 3600 | 0.2220 | 0.7778 | 0.8032 | 0.7903 | 0.9413 |
| 0.1787 | 1.6 | 4000 | 0.2185 | 0.7879 | 0.7860 | 0.7869 | 0.9417 |
| 0.146 | 1.76 | 4400 | 0.2134 | 0.7721 | 0.8128 | 0.7919 | 0.9416 |
| 0.1557 | 1.92 | 4800 | 0.2111 | 0.7857 | 0.8101 | 0.7977 | 0.9429 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr
|
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| 0
| 2023-04-12T02:33:28Z
|
---
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: 106.00 +/- 2.00
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 pablomaya -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 pablomaya -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 pablomaya
```
## 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', 1000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
BhanuSama/gpt2-finetuned-xsum
|
[] | null |
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}
| 0
| 2023-04-12T03:24:49Z
|
---
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: 584.00 +/- 104.33
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 MohammedEltoum -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 MohammedEltoum -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 MohammedEltoum
```
## 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)])
```
|
BigSalmon/FormalBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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| 10
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.78
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="xnpeng/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BigSalmon/GPTIntro
|
[] | null |
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| 0
| 2023-04-12T04:15:00Z
|
---
license: apache-2.0
inference: false
---
**NOTE: This "delta model" cannot be used directly.**
Users have to apply it on top of the original LLaMA weights to get actual Vicuna weights.
See https://github.com/lm-sys/FastChat#vicuna-weights for instructions.
<br>
<br>
# Vicuna Model Card
## Model details
**Model type:**
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
Vicuna was trained between March 2023 and April 2023.
**Organizations developing the model:**
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
**Paper or resources for more information:**
https://vicuna.lmsys.org/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues
## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training dataset
70K conversations collected from ShareGPT.com.
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
## Major updates of weights v1.1
- Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from `"###"` to the EOS token `"</s>"`. This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
- Fix the supervised fine-tuning loss computation for better model quality.
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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| 8
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9261570669458271
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2262
- Accuracy: 0.926
- F1: 0.9262
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.837 | 1.0 | 250 | 0.3302 | 0.9015 | 0.8980 |
| 0.2559 | 2.0 | 500 | 0.2262 | 0.926 | 0.9262 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BigSalmon/GPTNeo350MInformalToFormalLincoln4
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
| 11
| null |
---
license: apache-2.0
---
## convert vicuna-7b to ggml-vicuna-7b-f16
Source : https://huggingface.co/chharlesonfire/vicuna-7b
No unnecessary changes
Same format
No quantization
|
BigSalmon/GPTNeo350MInformalToFormalLincoln6
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
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"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
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}
| 14
| null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: ManishW/SoccerTwos_v0
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/InformalToFormalLincoln14
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
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},
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"max_length": 50
},
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},
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}
| 5
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 96.70 +/- 99.97
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'experiment_name': 'final3'
'env_name': 'LunarLander-v2'
'learning_rate': 0.0003
'seed': 1
'total_timesteps': 125000
'num_envs': 4
'num_steps': 128
'torch_deterministic': True
'cuda': True
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 10
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'save_video': True
'batch_size': 512
'mini_batch_size': 128}
```
|
BigSalmon/InformalToFormalLincoln15
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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}
| 11
| 2023-04-12T04:43:49Z
|
---
license: mit
language:
- en
pipeline_tag: text-classification
tags:
- emotion
- 20 classes
- code
- emotions
widget:
- text: I'm so angry right now. I can't believe he did that to me.
example_title: anger
- text: I'm feeling disgusted by the smell of this food.
example_title: disgust
- text: I'm feeling very afraid of what might happen next.
example_title: fear
- text: I'm so joyful right now! This is the best day of my life.
example_title: joy
- text: >-
I'm feeling neutral about this situation. I don't really care one way or
another.
example_title: neutral
- text: I'm feeling really sad today after my dog passed away."
example_title: sadness
- text: I'm so surprised by what just happened! I never saw that coming.
example_title: surprise
- text: I'm feeling cheeky today. I'm going to play a little prank on my friend.
example_title: cheeky
- text: I'm feeling confused about what to do next. I need some guidance.
example_title: confuse
- text: I'm feeling curious about the world around me. There's so much to learn!
example_title: curious
- text: I'm feeling empathetic towards my friend who is going through a tough time.
example_title: empathetic
- text: I'm feeling grumpy today. Everything is annoying me!
example_title: grumpy
- text: I'm feeling guilty about what I did. I wish I could take it back.
example_title: guilty
- text: I'm feeling very energetic today. I'm ready to take on the world!
example_title: energetic
- text: I'm feeling impatient waiting for this movie to start.
example_title: impatient
- text: >-
I'm feeling so much love for my family right now. They mean everything to
me.
example_title: love
- text: I'm thinking about my future and what I want to achieve.
example_title: think
- text: >-
I'm feeling serious about this issue. It's important and needs to be
addressed.
example_title: serious
- text: >-
I'm feeling suspicious of what he's telling me. I think he's hiding
something.
example_title: suspicious
- text: I'm feeling whiny today. Everything is bothering me!
example_title: whiny
- text: I love football so much
example_title: love 2
- text: I'm reflecting on my experiences to gain insights
example_title: think 2
- text: >-
I borrowed money from a friend and haven't paid it back yet. Now I feel
ashamed.
example_title: guilty 2
- text: I'm starting to think that he's up to something.
example_title: suspicious 2
- text: We need to approach this matter with a sense of purpose
example_title: serious 2
---
# Emotion classification from 20 classes
## 20 Emotion labels
| id | label |
| --- | ---------- |
| 0 | anger |
| 1 | cheeky |
| 2 | confuse |
| 3 | curious |
| 4 | disgust |
| 5 | empathetic |
| 6 | energetic |
| 7 | fear |
| 8 | grumpy |
| 9 | guilty |
| 10 | impatient |
| 11 | joy |
| 12 | love |
| 13 | neutral |
| 14 | sadness |
| 15 | serious |
| 16 | surprise |
| 17 | suspicious |
| 18 | think |
| 19 | whiny |
## How to use
Here is how to use this model to get the emotion label of a given text:
```python
from transformers import AutoModelForSequenceClassification, pipeline
model_name = 'jitesh/emotion-english'
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("text-classification", model=model, tokenizer=model_name)
text = "I can't wait any longer "
prediction = classifier(text)
print(prediction[0], text)
```
The above code outputs the following line.
```bash
{'label': 'impatient', 'score': 0.924211859703064} I can't wait any longer
```
|
BigSalmon/InformalToFormalLincoln17
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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"max_length": 50
},
"translation_en_to_de": {
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}
| 12
| 2023-04-12T04:55:39Z
|
---
license: apache-2.0
---
## convert ggml-vicuna-7b-f16 to ggml-vicuna-7b-q4_0
Source: https://huggingface.co/chharlesonfire/ggml-vicuna-7b-f16
No unnecessary changes
## Usage:
1. Download llama.cpp from https://github.com/ggerganov/llama.cpp
2. make and run llama.cpp and choose model with ggml-vicuna-7b-q4_0.bin
|
BigSalmon/MrLincoln10
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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"do_sample": true,
"max_length": 50
},
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},
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}
}
| 5
| null |
Access to model BMILab/TCR-BERT-MLM is restricted and you are not in the authorized list. Visit https://huggingface.co/BMILab/TCR-BERT-MLM to ask for access.
|
BigSalmon/MrLincoln2
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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}
}
| 9
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0920
- Accuracy: 0.9831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7143 | 1.0 | 399 | 0.6080 | 0.9187 |
| 0.29 | 2.0 | 798 | 0.1861 | 0.9751 |
| 0.2249 | 3.0 | 1197 | 0.1158 | 0.9813 |
| 0.1649 | 4.0 | 1597 | 0.0999 | 0.9796 |
| 0.1302 | 5.0 | 1995 | 0.0920 | 0.9831 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BigSalmon/MrLincoln7
|
[] | null |
{
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}
| 0
| null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -153.95 +/- 91.84
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'justinsiow/ppo-LunarLander-v2-scratch'
'batch_size': 512
'minibatch_size': 128}
```
|
BigeS/DialoGPT-small-Rick
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 10
| null |
---
license: other
---
# 聲明 Disclaimer
本資料夾中的模型不是我所製作，版權歸原作者所有（各模型版權詳見 http://www.civitai.com 所示）。我上傳至本資料夾僅爲方便在綫抽取資源，并非盈利。
The models in this folder are not made by me, and the copyright belongs to the original author (see http://www.civitai.com for details on the copyright of each model). I uploaded to this folder only for the convenience of extracting resources online, not for profit.
# 模型列表 List of Models
本資料夾中所有模型詳見下表。
All the models in this folder are detailed in the table below.
| 模型名稱 Model Name | Civitai 頁面鏈接 Civitai Page Link | Civitai 下載鏈接 Civitai Download Link |
|----------------------|--------------------|--------------------|
|realdosmix.safetensors |https://civitai.com/models/6925/realdosmix |https://civitai.com/api/download/models/8137 |
<img src="https://raw.githubusercontent.com/hanafuusen/images/main/realdosmix_civitai.jpg" width="" height="">
|
BillelBenoudjit/jplu-wikiann
|
[
"fr",
"dataset:wikiann",
"model-index"
] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-mscd1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-mscd1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8065
- Wer: 0.3824
## 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 6.6008 | 41.67 | 500 | 4.7974 | 1.0 |
| 1.2892 | 83.33 | 1000 | 1.7488 | 0.5 |
| 0.1991 | 125.0 | 1500 | 1.3122 | 0.4412 |
| 0.109 | 166.67 | 2000 | 1.3265 | 0.3235 |
| 0.071 | 208.33 | 2500 | 1.1280 | 0.3529 |
| 0.0508 | 250.0 | 3000 | 1.6514 | 0.3529 |
| 0.0365 | 291.67 | 3500 | 1.8065 | 0.3824 |
### Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
Bimal/my_bot_model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10
| null |
---
license: mit
---
### pemhHiS是一个通过岛综合版500页数据+欢乐恶搞+二次元百科词条进行微调后的清华大学ChatGLM-6B模型。
使用LORA进行finetune
示例串 这里[https://www.nmbxd1.com/t/56735335]
|
Binbin/test
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0
| null |
I want a photo for my youtube channel that has my name in letters and an image of sweet from gta san andreas has to say ADIN MODS the image
|
Biniam/en_ti_translate
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 14
| 2023-04-12T06:14:42Z
|
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-Cerebras-256M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [cerebras/Cerebras-GPT-256M](https://huggingface.co/cerebras/Cerebras-GPT-256M) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [cerebras/Cerebras-GPT-256M](https://huggingface.co/cerebras/Cerebras-GPT-256M) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 256M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
BinksSachary/DialoGPT-small-shaxx
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
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}
}
}
| 12
| null |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-wikiann-zh-ner-new
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: zh
split: validation
args: zh
metrics:
- name: Precision
type: precision
value: 0.7833553500660502
- name: Recall
type: recall
value: 0.8069318818538381
- name: F1
type: f1
value: 0.7949688510369846
- name: Accuracy
type: accuracy
value: 0.9435204272863568
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-wikiann-zh-ner-new
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2085
- Precision: 0.7834
- Recall: 0.8069
- F1: 0.7950
- Accuracy: 0.9435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.773 | 0.16 | 400 | 0.3232 | 0.5816 | 0.6670 | 0.6214 | 0.9049 |
| 0.3149 | 0.32 | 800 | 0.2954 | 0.6832 | 0.6923 | 0.6877 | 0.9195 |
| 0.2912 | 0.48 | 1200 | 0.2418 | 0.7010 | 0.7551 | 0.7270 | 0.9299 |
| 0.2446 | 0.64 | 1600 | 0.2539 | 0.7159 | 0.7743 | 0.7440 | 0.9292 |
| 0.2193 | 0.8 | 2000 | 0.2330 | 0.7441 | 0.7613 | 0.7526 | 0.9351 |
| 0.2434 | 0.96 | 2400 | 0.2186 | 0.7603 | 0.7696 | 0.7649 | 0.9369 |
| 0.1915 | 1.12 | 2800 | 0.2245 | 0.7568 | 0.8032 | 0.7793 | 0.9398 |
| 0.1607 | 1.28 | 3200 | 0.2263 | 0.7566 | 0.8138 | 0.7842 | 0.9399 |
| 0.1513 | 1.44 | 3600 | 0.2228 | 0.7782 | 0.7964 | 0.7872 | 0.9414 |
| 0.1777 | 1.6 | 4000 | 0.2098 | 0.7857 | 0.7916 | 0.7887 | 0.9423 |
| 0.1466 | 1.76 | 4400 | 0.2132 | 0.7673 | 0.8163 | 0.7911 | 0.9418 |
| 0.1528 | 1.92 | 4800 | 0.2093 | 0.7793 | 0.8114 | 0.7951 | 0.9435 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BinksSachary/ShaxxBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9
| null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="xnpeng/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BinksSachary/ShaxxBot2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
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}
| 12
| null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: git-base-pokemon
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. -->
# git-base-pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4530
- Wer Score: 16.5809
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 7.2955 | 16.67 | 50 | 5.0105 | 21.4433 |
| 3.6221 | 33.33 | 100 | 2.2745 | 21.3860 |
| 1.8264 | 50.0 | 150 | 1.4530 | 16.5809 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0a0+d321be6
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Blabla/Pipipopo
|
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| 0
| null |
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-Cerebras-590M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 590M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
```
|
Blaine-Mason/hackMIT-finetuned-sst2
|
[
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] |
text-classification
|
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| 36
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.908256880733945
---
<!-- 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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3078
- Accuracy: 0.9083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 211 | 0.3078 | 0.9083 |
| No log | 2.0 | 422 | 0.4370 | 0.8968 |
| 0.0968 | 3.0 | 633 | 0.4457 | 0.9002 |
| 0.0968 | 4.0 | 844 | 0.4723 | 0.9048 |
| 0.0259 | 5.0 | 1055 | 0.4991 | 0.9014 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Blazeolmo/Scrabunzi
|
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| 0
| null |
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<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว ดาวน์โหลด 2023</p>
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<p>ดาวน์โหลด เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว Hd 2023</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่อง HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องสตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องเต็มเรื่องภาพยนตร์ในรูปแบบ HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว สตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องฟรี</p>
<p>ดูเรื่อง เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) เต็มเรื่องออนไลน์ฟรี 2023</p>
<p>ดาวน์โหลด เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) หนังเต็ม เรื่องราว Hd 2023</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่อง HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่องสตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 เต็มเรื่องภาพยนตร์ในรูปแบบ HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว สตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่องฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว ดาวน์โหลดฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่อง 2023 ดูออนไลน์ฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว Google Drive</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว สตรีมสด</p>
<p>ดูเรื่อง เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 เต็มเรื่องออนไลน์ฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง พากย์ไทย เต็มเรื่อง:</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เต็มเรื่อง037</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องnetflix</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เต็มเรื่องhd</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องpantip</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง ค่าตั๋ว</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เรื่องย่อ</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องReview</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง (2023) - ดูหนังออนไลน์ฟรี 037HDmovie</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) หนังเต็มเรื่อง</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่อง HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) หนังเต็มออนไลน์</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) หนังเต็มออนไลน์ฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) ดูหนังออนไลน์</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) ดูฟรี เต็มเรื่อง</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) ดูฟรีในรูปแบบ HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) สตรีมออนไลน์</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่อง ซับไทย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่อง พากย์ไทย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่องFacebook</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่องInstagram</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่องTwitter</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่องYoutube</p>
|
BlightZz/DialoGPT-medium-Kurisu
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 19
| 2023-04-12T06:34:19Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### girlnew1 Dreambooth model trained by Fred99774 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BlindMan820/Sarcastic-News-Headlines
|
[
"pytorch",
"distilbert",
"text-classification",
"English",
"dataset:Kaggle Dataset",
"transformers",
"Text",
"Sequence-Classification",
"Sarcasm",
"DistilBert"
] |
text-classification
|
{
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 28
| null |
<p>[ดูหนัง] เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง/เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เต็มเรื่อง HD พากย์ไทย THAIวันนี้!.....</p>
<p> ทเรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง ดูหนังออนไลน์เต็มเรื่องฟรี HD1080 . เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง (2023) | จอห์น วิ
</p><p>► <a href="https://golden678.com/" rel="noopener nofollow">กำลังเล่น⇒ ⟹ ➟ เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง (THAI)</a></p>
<p>► <a href="https://golden678.com/" rel="noopener nofollow">กำลังเล่น⇒ ⟹ ➟ เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง (THAI)</a></p>
<p> แรงกว่านรก 4 (2023) หนังเต็มออนไลน์.
<p>(THAI) . เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เต็ม HD 1080p ฟรีออนไลน์ .Tid Noi: More Than True Love หนังเต็มเรื่องฟรี HD . สตรีมมิ่งประเทศไทยเต็มรูปแบบ HD (1080i) . พร้อมให้ดาวน์โหลด HD,DB,720,1080,4K, MKV, ความละเอียดสูงพิเศษ </p>
<p>ข้อมูลภาพยนตร์</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง รอบพิเศษ</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง จองตั๋ว</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องพากย์ไทย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง รอบฉาย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง ฉายญี่ปุ่น</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องสปอย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องวันที่ออกฉาย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องเข้าวันไหน</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องซับไทย</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องเวอร์ชันเต็ม</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องพากย์ไทย เต็มเรื่อง</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องอูต้า</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องดู</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023)</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องการ์ด</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องกล้อง</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องกล่อง</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องกราว</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องฉบับเต็ม</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องฟรี 2023</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว ดาวน์โหลด 2023</p>
<p>ดูเรื่อง เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องเต็มเรื่องออนไลน์ฟรี</p>
<p>ดาวน์โหลด เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว Hd 2023</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่อง HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องสตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องเต็มเรื่องภาพยนตร์ในรูปแบบ HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องหนังเต็ม เรื่องราว สตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องฟรี</p>
<p>ดูเรื่อง เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) เต็มเรื่องออนไลน์ฟรี 2023</p>
<p>ดาวน์โหลด เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 หนังเต็ม เรื่องราว HD</p>
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<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่อง HD</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 ภาพยนตร์เต็มเรื่องสตรีมมิ่งฟรี</p>
<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) 2023 เต็มเรื่องภาพยนตร์ในรูปแบบ HD</p>
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<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่อง ซับไทย</p>
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<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(2023) เต็มเรื่องYoutube</p>
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<p>[ดูหนัง] เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง/เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง เต็มเรื่อง HD พากย์ไทย THAIวันนี้!.....</p>
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<p>เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่องภาพยนตร์เต็มเรื่องฟรี</p>
<p>ดูเรื่อง เรื่อง John Wick: Chapter 4 จอห์น วิค 4 : แรงกว่านรก (2023) เต็มเรื่อง(Tid Noi: More Than True Love) เต็มเรื่องออนไลน์ฟรี 2023</p>
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|
BobBraico/bert-finetuned-ner
|
[] | null |
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| 0
| null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.22 +/- 3.72
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r justinsiow/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
BobBraico/distilbert-base-uncased-finetuned-imdb-accelerate
|
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| 0
| null |
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|
BobBraico/distilbert-base-uncased-finetuned-imdb
|
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| 0
| null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-end2end-chatbot-generative
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-chatbot-generative
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2857
## 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: 16
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9718 | 0.16 | 100 | 2.3951 |
| 2.4855 | 0.32 | 200 | 2.3368 |
| 2.4712 | 0.49 | 300 | 2.3113 |
| 2.4346 | 0.65 | 400 | 2.2972 |
| 2.4126 | 0.81 | 500 | 2.2888 |
| 2.4219 | 0.97 | 600 | 2.2857 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Botjallu/DialoGPT-small-harrypotter
|
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| 0
| 2023-04-12T07:06:48Z
|
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.03 +/- 4.46
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r justinsiow/rl_course_vizdoom_health_gathering_supreme-v2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme-v2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme-v2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
BotterHax/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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"GPT2LMHeadModel"
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| 8
| null |
---
license: openrail
pipeline_tag: image-segmentation
---
|
Brayan/CNN_Brain_Tumor
|
[] | null |
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}
| 0
| null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://singularite.itch.io/snowballtarget
2. Step 1: Find your model_id: jmurphy97/ppo-SnowballTarget1
3. Step 2: Select your SnowballTarget.onnx file
4. Click on Watch the agent play 👀
|
Brinah/1
|
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| 0
| null |
---
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: 270.78 +/- 21.22
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
...
```
|
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