modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 18:26:50
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 18:26:48
| card
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|---|---|---|---|---|---|---|---|---|---|
botp/Realistic_Vision_V1.3
|
botp
| 2023-05-04T09:18:22Z | 2 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T09:18:22Z |
---
license: creativeml-openrail-m
duplicated_from: SG161222/Realistic_Vision_V1.3
---
<b>Please read this!</b><br>
My model has always been free and always will be free. There are no restrictions on the use of the model. The rights to this model still belong to me.<br>
This model is available on <a href="https://www.mage.space/">Mage.Space</a> and <a href="https://sinkin.ai/">Sinkin.ai</a>
<hr/>
<b>I use this template to get good generation results:
Prompt:</b>
RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Example:</b> RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Negative Prompt:</b>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br>
<b>OR</b><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
<b>Euler A or DPM++ 2M Karras with 25 steps<br>
CFG Scale 3,5 - 7<br>
Hires. fix with Latent upscaler<br>
0 Hires steps and Denoising strength 0.25-0.45<br>
Upscale by 1.1-2.0</b>
|
Pietro97/ppo-Huggy
|
Pietro97
| 2023-05-04T09:15:03Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-04T09:14:55Z |
---
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: Pietro97/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
botp/Realistic_Vision_V2.0
|
botp
| 2023-05-04T09:14:37Z | 4 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T09:14:36Z |
---
license: creativeml-openrail-m
duplicated_from: SG161222/Realistic_Vision_V2.0
---
<b>Please read this!</b><br>
For version 2.0 it is recommended to use with VAE (to improve generation quality and get rid of blue artifacts): https://huggingface.co/stabilityai/sd-vae-ft-mse-original<br>
This model is available on <a href="https://www.mage.space/">Mage.Space</a>, <a href="https://sinkin.ai/">Sinkin.ai</a>, <a href="https://getimg.ai/">GetImg.ai</a> and (<a href="https://randomseed.co/">RandomSeed.co</a> - NSFW content)
<hr/>
<b>I use this template to get good generation results:
Prompt:</b>
RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Example:</b> RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Negative Prompt:</b>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br>
<b>OR</b><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
<b>Euler A or DPM++ 2M Karras with 25 steps<br>
CFG Scale 3,5 - 7<br>
Hires. fix with Latent upscaler<br>
0 Hires steps and Denoising strength 0.25-0.45<br>
Upscale by 1.1-2.0</b>
|
Theju/switch_low_b4_2
|
Theju
| 2023-05-04T09:13:46Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T09:11:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_low_b4_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. -->
# switch_low_b4_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
truegpt/truegpt_small
|
truegpt
| 2023-05-04T09:13:36Z | 3 | 0 |
transformers
|
[
"transformers",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-01T14:30:48Z |
# TrueGPT Small: AI Model for Action and Empowerment
TrueGPT Small is a lightweight version of the TrueGPT artificial intelligence model, designed for users who need the empowering and actionable features of TrueGPT with reduced computational requirements. By providing actionable solutions and eliminating uncertainty, TrueGPT Small retains the core features of the original TrueGPT while making it accessible to a wider range of devices and systems. With seamless integration to the Hugging Face ecosystem, users can easily utilize TrueGPT Small for various AI applications.
|
usix79/a2c-PandaReachDense-v2
|
usix79
| 2023-05-04T09:07:43Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T09:05:05Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.70 +/- 0.62
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
udon2301/gpt2-ft
|
udon2301
| 2023-05-04T08:56:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T11:05:45Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-ft
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. -->
# gpt2-ft
This model is a fine-tuned version of [rinna/japanese-gpt-1b](https://huggingface.co/rinna/japanese-gpt-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
brathief/Alice_extend_brathief_e500
|
brathief
| 2023-05-04T08:43:17Z | 7 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-04-22T13:39:09Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - brathief/Alice_extend_brathief_e500
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
pkufool/icefall_asr_aishell_pruned_transducer_stateless7_bbpe
|
pkufool
| 2023-05-04T08:39:07Z | 0 | 0 | null |
[
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2023-05-04T07:25:32Z |
---
license: apache-2.0
---
The results:
|Vocab size | Greedy search(dev & test) | Modified beam search(dev & test) | Fast beam search (dev & test) | Fast beam search LG (dev & test) | comments|
|-- | -- | -- | -- | -- | --|
|500 | 4.31 & 4.59 | 4.25 & 4.54 | 4.27 & 4.55 | 4.07 & 4.38 | --epoch 48 --avg 29|
The training command:
```bash
export CUDA_VISIBLE_DEVICES="4,5,6,7"
./pruned_transducer_stateless7_bbpe/train.py \
--world-size 4 \
--num-epochs 50 \
--start-epoch 1 \
--use-fp16 1 \
--max-duration 800 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--exp-dir pruned_transducer_stateless7_bbpe/exp \
--lr-epochs 6 \
--master-port 12535
```
The decoding command:
```bash
for m in greedy_search modified_beam_search fast_beam_search fast_beam_search_LG; do
./pruned_transducer_stateless7_bbpe/decode.py \
--epoch 48 \
--avg 29 \
--exp-dir ./pruned_transducer_stateless7_bbpe/exp \
--max-sym-per-frame 1 \
--ngram-lm-scale 0.25 \
--ilme-scale 0.2 \
--bpe-model data/lang_bbpe_500/bbpe.model \
--max-duration 2000 \
--decoding-method $m
done
```
|
civitary/msbrew
|
civitary
| 2023-05-04T08:38:50Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T08:32:55Z |
---
license: creativeml-openrail-m
---
|
pkufool/icefall_asr_librispeech_conformer_ctc
|
pkufool
| 2023-05-04T08:35:10Z | 0 | 4 | null |
[
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: apache-2.0
language:
- en
---
# Pre-trained Conformer-CTC models for the librispeech dataset with icefall.
The model was trained on full [LibriSpeech](http://openslr.org/12/) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/pull/13) for more details of this model.
## How to use
See (https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/README.md)
## Training procedure
The version of the mainly repositories are list below.
k2: https://github.com/k2-fsa/k2/commit/81cec9ec736d2c603ad75d933bb3e3a3706fb0dd
icefall: https://github.com/k2-fsa/icefall/commit/ef233486ae6d21bacb940de45efb35d0c334605c
lhotse: https://github.com/lhotse-speech/lhotse/commit/5dfe0f4c02b1334ebb7db6d67e1141fe406ca76b
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. It is better to use the given version above, but I think the latest version would be ok. And also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout ef233486
```
* Preparing data.
```
cd egs/librispeech/ASR
bash ./prepare.sh
```
* Training
```bash
export CUDA_VISIBLE_DEVICES="0,1,2,3"
python conformer_ctc/train.py --bucketing-sampler True \
--concatenate-cuts False \
--max-duration 200 \
--full-libri True \
--world-size 4
```
## Evaluation results
The best decoding results (WERs) on LibriSpeech test-clean and test-other are listed below, we got this results by averaging models from epoch 15 to 34.
||test-clean|test-other|
|--|--|--|
|WER|2.57%|5.94%|
|
Kiriko/LunarLanderAgent
|
Kiriko
| 2023-05-04T08:31:39Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T08:31:17Z |
---
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: 257.09 +/- 11.64
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
...
```
|
yemiancheng/like-model
|
yemiancheng
| 2023-05-04T08:27:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-04T05:15:02Z |
# readme
saving some models i like. i will collect them here for using(downloading) easily.
## why
- [x] sometimes i want to use it but fogget where to download it.
## life guarantee statement
If there is infringement, please temporarily notify me and I will delete it.
my email: `ymc-github@gmail.com` or `yemiancheng1993@163.com`
|
MartinMarenz/q-Taxiv3-02
|
MartinMarenz
| 2023-05-04T08:21:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T08:21:13Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxiv3-02
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="MartinMarenz/q-Taxiv3-02", 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"])
```
|
Aleksandar/electra-srb-ner
|
Aleksandar
| 2023-05-04T08:14:22Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.9568394937134688
---
<!-- 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. -->
# electra-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3406
- Precision: 0.8934
- Recall: 0.9087
- F1: 0.9010
- Accuracy: 0.9568
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3686 | 1.0 | 625 | 0.2108 | 0.8326 | 0.8494 | 0.8409 | 0.9335 |
| 0.1886 | 2.0 | 1250 | 0.1784 | 0.8737 | 0.8713 | 0.8725 | 0.9456 |
| 0.1323 | 3.0 | 1875 | 0.1805 | 0.8654 | 0.8870 | 0.8760 | 0.9468 |
| 0.0675 | 4.0 | 2500 | 0.2018 | 0.8736 | 0.8880 | 0.8807 | 0.9502 |
| 0.0425 | 5.0 | 3125 | 0.2162 | 0.8818 | 0.8945 | 0.8881 | 0.9512 |
| 0.0343 | 6.0 | 3750 | 0.2492 | 0.8790 | 0.8928 | 0.8859 | 0.9513 |
| 0.0253 | 7.0 | 4375 | 0.2562 | 0.8821 | 0.9006 | 0.8912 | 0.9525 |
| 0.0142 | 8.0 | 5000 | 0.2788 | 0.8807 | 0.9013 | 0.8909 | 0.9524 |
| 0.0114 | 9.0 | 5625 | 0.2793 | 0.8861 | 0.9002 | 0.8931 | 0.9534 |
| 0.0095 | 10.0 | 6250 | 0.2967 | 0.8887 | 0.9034 | 0.8960 | 0.9550 |
| 0.008 | 11.0 | 6875 | 0.2993 | 0.8899 | 0.9067 | 0.8982 | 0.9556 |
| 0.0048 | 12.0 | 7500 | 0.3215 | 0.8887 | 0.9038 | 0.8962 | 0.9545 |
| 0.0034 | 13.0 | 8125 | 0.3242 | 0.8897 | 0.9068 | 0.8982 | 0.9554 |
| 0.003 | 14.0 | 8750 | 0.3311 | 0.8884 | 0.9085 | 0.8983 | 0.9559 |
| 0.0025 | 15.0 | 9375 | 0.3383 | 0.8943 | 0.9062 | 0.9002 | 0.9562 |
| 0.0011 | 16.0 | 10000 | 0.3346 | 0.8941 | 0.9112 | 0.9026 | 0.9574 |
| 0.0015 | 17.0 | 10625 | 0.3362 | 0.8944 | 0.9081 | 0.9012 | 0.9567 |
| 0.001 | 18.0 | 11250 | 0.3464 | 0.8877 | 0.9100 | 0.8987 | 0.9559 |
| 0.0012 | 19.0 | 11875 | 0.3415 | 0.8944 | 0.9089 | 0.9016 | 0.9568 |
| 0.0005 | 20.0 | 12500 | 0.3406 | 0.8934 | 0.9087 | 0.9010 | 0.9568 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|
SHENMU007/neunit_BASE_V4
|
SHENMU007
| 2023-05-04T08:11:57Z | 80 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"1.1.0",
"generated_from_trainer",
"zh",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2023-05-04T06:13:58Z |
---
language:
- zh
license: mit
tags:
- 1.1.0
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch neunit
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. -->
# SpeechT5 TTS Dutch neunit
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.12.1
|
MartinMarenz/q-Taxiv3-01
|
MartinMarenz
| 2023-05-04T08:11:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-04T08:11:46Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxiv3-01
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="MartinMarenz/q-Taxiv3-01", 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"])
```
|
leonardosaveri/DSChallenge_Roberta_Base
|
leonardosaveri
| 2023-05-04T08:08:51Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T07:52:31Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DSChallenge_Roberta_Base
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DSChallenge_Roberta_Base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1755
- Accuracy: 0.9549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2974 | 1.0 | 793 | 0.1676 | 0.9419 |
| 0.1491 | 2.0 | 1586 | 0.1755 | 0.9549 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
nozmenoz/bella
|
nozmenoz
| 2023-05-04T08:06:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-04-29T07:37:29Z |
---
license: creativeml-openrail-m
---
|
zohaib99k/Bert_Arabic-SQuADv2-QA
|
zohaib99k
| 2023-05-04T07:42:02Z | 115 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"question-answering",
"ar",
"dataset:ZeyadAhmed/Arabic-SQuADv2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-04T07:37:13Z |
---
datasets:
- ZeyadAhmed/Arabic-SQuADv2.0
language:
- ar
metrics:
-
name: exact_match
type: exact_match
value: 65.12
-
name: F1
type: f1
value: 71.49
---
# AraElectra for Question Answering on Arabic-SQuADv2
This is the [AraElectra](https://huggingface.co/aubmindlab/araelectra-base-discriminator) model, fine-tuned using the [Arabic-SQuADv2.0](https://huggingface.co/datasets/ZeyadAhmed/Arabic-SQuADv2.0) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. with help of [AraElectra Classifier](https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-CLS) to predicted unanswerable question.
## Overview
**Language model:** AraElectra <br>
**Language:** Arabic <br>
**Downstream-task:** Extractive QA
**Training data:** Arabic-SQuADv2.0
**Eval data:** Arabic-SQuADv2.0 <br>
**Test data:** Arabic-SQuADv2.0 <br>
**Code:** [See More Info on Github](https://github.com/zeyadahmed10/Arabic-MRC)
**Infrastructure**: 1x Tesla K80
## Hyperparameters
```
batch_size = 8
n_epochs = 4
base_LM_model = "AraElectra"
learning_rate = 3e-5
optimizer = AdamW
padding = dynamic
```
## Online Demo on Arabic Wikipedia and User Provided Contexts
See model in action hosted on streamlit [](https://share.streamlit.io/wissamantoun/arabic-wikipedia-qa-streamlit/main)
## Usage
For best results use the AraBert [preprocessor](https://github.com/aub-mind/arabert/blob/master/preprocess.py) by aub-mind
```python
from transformers import ElectraForQuestionAnswering, ElectraForSequenceClassification, AutoTokenizer, pipeline
from preprocess import ArabertPreprocessor
prep_object = ArabertPreprocessor("araelectra-base-discriminator")
question = prep_object('ما هي جامعة الدول العربية ؟')
context = prep_object('''
جامعة الدول العربية هيمنظمة إقليمية تضم دولاً عربية في آسيا وأفريقيا.
ينص ميثاقها على التنسيق بين الدول الأعضاء في الشؤون الاقتصادية، ومن ضمنها العلاقات التجارية الاتصالات، العلاقات الثقافية، الجنسيات ووثائق وأذونات السفر والعلاقات الاجتماعية والصحة. المقر الدائم لجامعة الدول العربية يقع في القاهرة، عاصمة مصر (تونس من 1979 إلى 1990).
''')
# a) Get predictions
qa_modelname = 'ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA'
cls_modelname = 'ZeyadAhmed/AraElectra-Arabic-SQuADv2-CLS'
qa_pipe = pipeline('question-answering', model=qa_modelname, tokenizer=qa_modelname)
QA_input = {
'question': question,
'context': context
}
CLS_input = {
'text': question,
'text_pair': context
}
qa_res = qa_pipe(QA_input)
cls_res = cls_pipe(CLS_iput)
threshold = 0.5 #hyperparameter can be tweaked
## note classification results label0 probability it can be answered label1 probability can't be answered
## if label1 probability > threshold then consider the output of qa_res is empty string else take the qa_res
# b) Load model & tokenizer
qa_model = ElectraForQuestionAnswering.from_pretrained(qa_modelname)
cls_model = ElectraForSequenceClassification.from_pretrained(cls_modelname)
tokenizer = AutoTokenizer.from_pretrained(qa_modelname)
```
## Performance
Evaluated on the Arabic-SQuAD 2.0 test set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/) except changing in the preprocessing a little to fit the arabic language [the modified eval script](https://github.com/zeyadahmed10/Arabic-MRC/blob/main/evaluatev2.py).
```
"exact": 65.11555277951281,
"f1": 71.49042547237256,,
"total": 9606,
"HasAns_exact": 56.14535768645358,
"HasAns_f1": 67.79623803036668,
"HasAns_total": 5256,
"NoAns_exact": 75.95402298850574,
"NoAns_f1": 75.95402298850574,
"NoAns_total": 4350
```
|
maksim2000153/xlm-roberta-base-finetuned-panx-de-ner
|
maksim2000153
| 2023-05-04T07:39:10Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-05-04T07:18:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8653353814644136
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8653
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-simcse-roberta-large-semeval2015-restaurants
|
StevenLimcorn
| 2023-05-04T07:31:28Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T13:10:10Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2015-restaurants
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. -->
# unsup-simcse-roberta-large-semeval2015-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
MDOWNLOAD/ZNBAELORA
|
MDOWNLOAD
| 2023-05-04T07:28:45Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T07:27:12Z |
---
license: creativeml-openrail-m
---
|
redstonehero/aiomonstergirls_v3
|
redstonehero
| 2023-05-04T07:18:12Z | 29 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T06:54:17Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
---
|
Theju/switch_low_2
|
Theju
| 2023-05-04T07:14:20Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T07:13:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_low_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. -->
# switch_low_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- 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: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Theju/switch_medium_2
|
Theju
| 2023-05-04T07:10:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-04T07:09:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_medium_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. -->
# switch_medium_2
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-simcse-roberta-large-semeval2015-laptops
|
StevenLimcorn
| 2023-05-04T07:06:14Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T13:00:59Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2015-laptops
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. -->
# unsup-simcse-roberta-large-semeval2015-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
ttj/sac-logos-ava1-l14-linearMSE
|
ttj
| 2023-05-04T06:57:58Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-05-04T06:52:03Z |
---
license: apache-2.0
---
model ported from https://github.com/christophschuhmann/improved-aesthetic-predictor
|
soumi-maiti/libri23mix_eend_ss
|
soumi-maiti
| 2023-05-04T06:49:28Z | 4 | 0 |
espnet
|
[
"espnet",
"audio",
"diarization",
"en",
"dataset:librimix",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2023-05-04T06:34:06Z |
---
tags:
- espnet
- audio
- diarization
language: en
datasets:
- librimix
license: cc-by-4.0
---
## ESPnet2 DIAR model
### `soumi-maiti/libri23mix_eend_ss`
This model was trained by soumimaiti using librimix recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout d837c97c88f13ffe655a30bcff93d814f212b225
pip install -e .
cd egs2/librimix/enh_diar23
./run.sh --skip_data_prep false --skip_train true --download_model soumi-maiti/libri23mix_eend_ss
```
## DIAR config
<details><summary>expand</summary>
```
config: conf/tuning/train_diar_enh_convtasnet_concat_feats_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_enh_train_diar_enh_convtasnet_concat_feats_adapt
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss_enh
- min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 16
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param:
- ../enh_diar1/exp/diar_enh_train_diar_enh_convtasnet_concat_feats_raw/valid.loss_enh.best.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 1
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_enh_stats_8k/train/speech_shape
- exp/diar_enh_stats_8k/train/text_shape
- exp/diar_enh_stats_8k/train/speech_ref1_shape
- exp/diar_enh_stats_8k/train/speech_ref2_shape
- exp/diar_enh_stats_8k/train/speech_ref3_shape
- exp/diar_enh_stats_8k/train/noise_ref1_shape
valid_shape_file:
- exp/diar_enh_stats_8k/valid/speech_shape
- exp/diar_enh_stats_8k/valid/text_shape
- exp/diar_enh_stats_8k/valid/speech_ref1_shape
- exp/diar_enh_stats_8k/valid/speech_ref2_shape
- exp/diar_enh_stats_8k/valid/speech_ref3_shape
- exp/diar_enh_stats_8k/valid/noise_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 80000
- 80000
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 24000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/espnet_rttm
- text
- rttm
- - dump/raw/train/spk1.scp
- speech_ref1
- sound
- - dump/raw/train/spk2.scp
- speech_ref2
- sound
- - dump/raw/train/spk3.scp
- speech_ref3
- sound
- - dump/raw/train/noise1.scp
- noise_ref1
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev/wav.scp
- speech
- sound
- - dump/raw/dev/espnet_rttm
- text
- rttm
- - dump/raw/dev/spk1.scp
- speech_ref1
- sound
- - dump/raw/dev/spk2.scp
- speech_ref2
- sound
- - dump/raw/dev/spk3.scp
- speech_ref3
- sound
- - dump/raw/dev/noise1.scp
- noise_ref1
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-07
weight_decay: 0
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.5
patience: 1
token_list: null
src_token_list: null
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
enh_criterions:
- name: si_snr
conf:
eps: 1.0e-07
wrapper: pit
wrapper_conf:
weight: 1.0
independent_perm: true
flexible_numspk: true
diar_num_spk: 3
diar_input_size: 128
enh_model_conf:
loss_type: si_snr
asr_model_conf:
ctc_weight: 0.5
interctc_weight: 0.0
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
st_model_conf:
stft_consistency: false
loss_type: mask_mse
mask_type: null
diar_model_conf:
diar_weight: 0.2
attractor_weight: 0.2
subtask_series:
- enh
- diar
model_conf:
calc_enh_loss: true
bypass_enh_prob: 0
use_preprocessor: true
token_type: bpe
bpemodel: null
src_token_type: bpe
src_bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
enh_encoder: conv
enh_encoder_conf:
channel: 512
kernel_size: 16
stride: 8
enh_separator: tcn_nomask
enh_separator_conf:
layer: 8
stack: 3
bottleneck_dim: 128
hidden_dim: 512
kernel: 3
causal: false
norm_type: gLN
enh_decoder: conv
enh_decoder_conf:
channel: 512
kernel_size: 16
stride: 8
enh_mask_module: multi_mask
enh_mask_module_conf:
max_num_spk: 3
mask_nonlinear: relu
bottleneck_dim: 128
frontend: default
frontend_conf: {}
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf: {}
asr_preencoder: null
asr_preencoder_conf: {}
asr_encoder: rnn
asr_encoder_conf: {}
asr_postencoder: null
asr_postencoder_conf: {}
asr_decoder: rnn
asr_decoder_conf: {}
st_preencoder: null
st_preencoder_conf: {}
st_encoder: rnn
st_encoder_conf: {}
st_postencoder: null
st_postencoder_conf: {}
st_decoder: rnn
st_decoder_conf: {}
st_extra_asr_decoder: rnn
st_extra_asr_decoder_conf: {}
st_extra_mt_decoder: rnn
st_extra_mt_decoder_conf: {}
diar_frontend: default
diar_frontend_conf:
hop_length: 64
fs: 8000
diar_specaug: null
diar_specaug_conf: {}
diar_normalize: utterance_mvn
diar_normalize_conf: {}
diar_encoder: transformer
diar_encoder_conf:
input_layer: conv2d8
num_blocks: 4
linear_units: 512
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.1
diar_decoder: linear
diar_decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
win_length: 256
hop_length: 64
diar_attractor: rnn
diar_attractor_conf:
unit: 256
layer: 1
dropout: 0.0
attractor_grad: true
required:
- output_dir
version: '202205'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
VinayakMane47/mt5-small-finetuned-amazon-en-es
|
VinayakMane47
| 2023-05-04T06:46:38Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-04T05:58:15Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: VinayakMane47/mt5-small-finetuned-amazon-en-es
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. -->
# VinayakMane47/mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.4768
- Validation Loss: 3.7299
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 6160, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 10.4984 | 4.9846 | 0 |
| 6.4092 | 4.2145 | 1 |
| 5.5483 | 3.9695 | 2 |
| 5.0862 | 3.8716 | 3 |
| 4.8314 | 3.8164 | 4 |
| 4.6503 | 3.7648 | 5 |
| 4.5296 | 3.7418 | 6 |
| 4.4768 | 3.7299 | 7 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BigXiang/Sea_side_shaonv
|
BigXiang
| 2023-05-04T06:28:12Z | 0 | 3 | null |
[
"region:us"
] | null | 2023-03-30T18:26:46Z |
介绍：
我上次因为在讨论区主张那些标记韩服的banban，不要跑来偷汉服，而被ban了3天（大概率是被banban们恶意举报了）,刚刚通过申诉解封。同时上次的shaonv崇拜（LOL_style_v10）也在没有任何提醒的情况下被删除了。我会在之后想办法补档。这次带来的是海边shaonv，20个批次练了3小时，成品很不错。不多说，请直接看效果。考虑到上次shaonv崇拜因为不同人群喜好而在评论区引起了争议。这次考虑到不同受众，直接做了“大小”两个版本的lora。
（哈哈，我个人强推小奈奈，效果最佳。大奈奈内测一天，喜欢大奈奈的得等到明天才能下）
同样的，不需要过多prompt就能得到优秀成品，荤素兼备，大道至简。触发词也一如既往可爱。
相关问题、或者渲染出来的优秀作品，都欢迎在评论区留言反馈。
看板娘样图示范：
keai, <lora:SSS_style-000018:0.6>
Negative prompt: bad-picture-chill-75v, negativeembed
Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 4231914231, Size: 560x700,
xyz权重参考值（不知道为啥，我发现xyz脚本跑出来的图其实不准，真的是仅供参考）：
注意！一旦下载这个lora，即证明你自愿遵守以下使用事项：
· 本Lora仅供个人学习交流，禁止任何形式的转载、传播；禁止用于任何商业用途。
· Lora禁止用于从事非法活动，使用时请遵守所在地的法律法规。对于Lora使用者的非法行为，本人概不负责并坚决反对。
· 不鼓励将Lora用于生成NSFW内容。
EN
This is used to generate shaonv at the beach .And you are welcome to show your work in the comment section.Hope you enjoy.
Example：
keai, <lora:SSS_style-000018:0.6>
Negative prompt: bad-picture-chill-75v, negativeembed
Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 4231914231, Size: 560x700,
Attention! By downloading this lora, you certify your voluntary compliance with the following terms:
Terms of Use:
· My Lora is only for personal learning and communication.Any form of reproduction and dissemination is prohibited; any commercial use is prohibited.
· Use for illegal activities is prohibited, and please observe the laws and regulations of your location when using Lora. I am not responsible for and strongly oppose any illegal actions by Lora users.
· it's discouraged for generating NSFW content.
|
Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically
|
Zayn
| 2023-05-04T06:28:10Z | 0 | 9 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"image-captioning",
"doi:10.57967/hf/0658",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2022-10-09T09:34:50Z |
---
tags:
- image-to-text
- image-captioning
license: apache-2.0
widget:
- src: https://pixabay.com/get/ga187b8f146a9fa30b1f553d63fa94271e023868cd247fbad7ce02b6ffb5718a52fc04809be440f997f57dad90614dde2e9821edf8e628925f0042c6584fc04ec809421a040e3bc9561324249ab6e09c4_1280.jpg
example_title: Horse Riding
- src: https://static1.bigstockphoto.com/6/8/2/large1500/286059499.jpg
example_title: Bicycle
---
This is an image captioning model training by Zayn
```python
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
model = VisionEncoderDecoderModel.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
feature_extractor = ViTFeatureExtractor.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
tokenizer = AutoTokenizer.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
max_length = 20
num_beams = 8
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_step(image_paths):
images = []
for image_path in image_paths:
i_image = Image.open(image_path)
if i_image.mode != "RGB":
i_image = i_image.convert(mode="RGB")
images.append(i_image)
pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
predict_step(['Image URL.jpg'])
|
sd-concepts-library/ahx-beta-453407d
|
sd-concepts-library
| 2023-05-04T05:58:52Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-05-04T05:58:48Z |
---
license: mit
---
### ahx-beta-453407d on Stable Diffusion
This is the `<ahx-beta-453407d>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
hanafuusen2001/LoRA_download_2
|
hanafuusen2001
| 2023-05-04T05:57:40Z | 0 | 3 | null |
[
"license:other",
"region:us"
] | null | 2023-04-12T12:36:37Z |
---
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 |
|----------------------|--------------------|--------------------|
|samdoesartsSamYang_offset.safetensors |https://civitai.com/models/6638 |https://civitai.com/api/download/models/7804 |
|samdoesartsSamYang_original.safetensors |已失效 expired |https://civitai.com/api/download/models/10864 |
|hipoly3DModelLora_v20.safetensors |https://civitai.com/models/8730?modelVersionId=44566 |https://civitai.com/api/download/models/44566 |
|hipoly3DModelLora_v10.safetensors |https://civitai.com/models/8730?modelVersionId=10301 |https://civitai.com/api/download/models/10301 |
|Zheng.safetensors |https://civitai.com/models/11034?modelVersionId=39348 |https://civitai.com/api/download/models/39348 |
注 1：samdoesartsSamYang 模型的觸發詞為：sam yang
注 2：hipoly3DModelLora_v10 模型的觸發詞為：hiqcgbody
<img src="https://raw.githubusercontent.com/hanafuusen/images/main/samdoesartsSamYang_civitai.jpg" width="" height="">
<img src="https://raw.githubusercontent.com/hanafuusen/images/main/hipoly3DModelLora_v10_civitai.jpg" width="" height="">
|
adiga20/git-base-pokemon
|
adiga20
| 2023-05-04T05:44:11Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T05:44:11Z |
---
license: creativeml-openrail-m
---
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2015-restaurants
|
StevenLimcorn
| 2023-05-04T05:42:15Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:04:57Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2015-restaurants
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2015-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2015-laptops
|
StevenLimcorn
| 2023-05-04T05:41:13Z | 94 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:00:54Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2015-laptops
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2015-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-facebook-election-ads
|
StevenLimcorn
| 2023-05-04T05:40:44Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:03:37Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-facebook-election-ads
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. -->
# semeval-unsup-promcse-bert-base-uncased-facebook-election-ads
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2016-laptops
|
StevenLimcorn
| 2023-05-04T05:36:20Z | 93 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T16:59:00Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2016-laptops
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2016-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2014-restaurants
|
StevenLimcorn
| 2023-05-04T05:32:50Z | 88 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:02:15Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2014-restaurants
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2014-restaurants
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
versae/wav2vec2-base-finetuned-coscan-sex
|
versae
| 2023-05-04T05:32:07Z | 156 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:coscan-speech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-09-06T23:00:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- coscan-speech
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-coscan-sex
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Coscan Speech
type: NbAiLab/coscan-speech
args: no
metrics:
- name: Test Accuracy
type: accuracy
value: 0.9993247805536799
- name: Validation Accuracy
type: accuracy
value: 0.9965283657917019
---
<!-- 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-coscan-sex
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0229
- Accuracy: 0.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0034 | 1.0 | 6644 | 0.0229 | 0.9965 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.10.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
StevenLimcorn/unsup-promcse-bert-base-uncased-semeval2014-laptops
|
StevenLimcorn
| 2023-05-04T05:32:00Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-05-03T17:07:54Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: semeval-unsup-promcse-bert-base-uncased-semeval2014-laptops
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. -->
# semeval-unsup-promcse-bert-base-uncased-semeval2014-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
StevenLimcorn/unsup-simcse-roberta-large-semeval2014-laptops
|
StevenLimcorn
| 2023-05-04T05:23:55Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-04-27T16:36:00Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: unsup-simcse-roberta-large-semeval2014-laptops
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. -->
# unsup-simcse-roberta-large-semeval2014-laptops
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
alanwalk/ShirtTugPose_lora
|
alanwalk
| 2023-05-04T05:07:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-04T05:05:21Z |
https://civitai.com/models/7706/shirt-tug-pose-lora
LORA model for shirt tug pose, suggested LORA weights: 0.5 ~ 1.5, default weight 1 should be good enough.
If the pose doesn't show up for some checkpoints, try greater weights.
Trigger words: shirt, naked shirt, shirt tug
|
imania/amir_take_home_result-2023_05_03-22_33_43
|
imania
| 2023-05-04T05:03:42Z | 179 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T04:52:50Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
|
fiatrete/dan-used-models
|
fiatrete
| 2023-05-04T04:58:27Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-03-16T09:36:38Z |
---
license: openrail
---
models used in [DAN](https://github.com/fiatrete/DAN-Stable-Diffusion-Computing-Network).
all models are gathered from network(most from [civitai](https://civitai.com)).
this place is used as a data store.
|
P1NHE4D/whisper-medium-nn-v3
|
P1NHE4D
| 2023-05-04T04:57:41Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nn",
"dataset:norwegian-parliament",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T12:25:27Z |
---
language:
- nn
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- norwegian-parliament
metrics:
- wer
model-index:
- name: whisper-medium-nn-v3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Stortingskorpuset
type: norwegian-parliament
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 11.337582785573966
---
<!-- 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-medium-nn-v3
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Stortingskorpuset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2116
- Wer: 11.3376
## 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
- 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: 1000
- training_steps: 8000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4413 | 0.25 | 2000 | 0.4447 | 26.7707 |
| 0.1945 | 1.1 | 4000 | 0.3042 | 17.8344 |
| 0.1013 | 1.35 | 6000 | 0.2421 | 14.2138 |
| 0.0308 | 2.2 | 8000 | 0.2116 | 11.3376 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
P1NHE4D/whisper-medium-nb-v3
|
P1NHE4D
| 2023-05-04T04:34:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nb",
"dataset:norwegian-parliament",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T12:17:13Z |
---
language:
- nb
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- norwegian-parliament
metrics:
- wer
model-index:
- name: whisper-medium-nb-v3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Stortingskorpuset
type: norwegian-parliament
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 10.024541720925574
---
<!-- 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-medium-nb-v3
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Stortingskorpuset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1948
- Wer: 10.0245
## 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
- 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: 1000
- training_steps: 8000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4018 | 0.25 | 2000 | 0.4179 | 25.0751 |
| 0.1617 | 1.1 | 4000 | 0.2911 | 16.5849 |
| 0.0885 | 1.35 | 6000 | 0.2264 | 12.5146 |
| 0.0269 | 2.2 | 8000 | 0.1948 | 10.0245 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
|
abhishek/autotrain-m7xl-lpfp-h4qr-55209128847
|
abhishek
| 2023-05-04T04:21:15Z | 0 | 0 | null |
[
"autotrain",
"text-generation",
"dataset:abhishek/autotrain-data-m7xl-lpfp-h4qr",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T19:55:21Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love 🤗 AutoTrain because "
datasets:
- abhishek/autotrain-data-m7xl-lpfp-h4qr
co2_eq_emissions:
emissions: 0
---
# Model Trained Using AutoTrain
- Problem type: Text Generation
- CO2 Emissions (in grams): 0.0000
## Validation Metrics
loss: 0.8759807348251343
|
shawt100/shawtsanders
|
shawt100
| 2023-05-04T04:14:50Z | 36 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"dataset:OpenAssistant/oasst1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-05-04T03:46:42Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
datasets:
- OpenAssistant/oasst1
metrics:
- character
library_name: diffusers
pipeline_tag: text-to-image
---
### shawtsanders Dreambooth model trained by shawt100 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:
|
joseph-t/purrfect-ai-test
|
joseph-t
| 2023-05-04T03:44:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T03:44:02Z |
---
license: creativeml-openrail-m
---
|
muwenxin/autotrain-xgwbishe1-55280129012
|
muwenxin
| 2023-05-04T03:38:17Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"summarization",
"en",
"dataset:muwenxin/autotrain-data-xgwbishe1",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-05-04T03:34:03Z |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- muwenxin/autotrain-data-xgwbishe1
co2_eq_emissions:
emissions: 1.7354362265383152
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 55280129012
- CO2 Emissions (in grams): 1.7354
## Validation Metrics
- Loss: 3.123
- Rouge1: 15.575
- Rouge2: 2.825
- RougeL: 11.785
- RougeLsum: 13.616
- Gen Len: 20.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/muwenxin/autotrain-xgwbishe1-55280129012
```
|
4bit/oasst-llama13b-4bit-128g
|
4bit
| 2023-05-04T03:10:55Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T02:57:06Z |
https://wandb.ai/open-assistant/supervised-finetuning/runs/lguuq2c1
Quantized from https://huggingface.co/dvruette/oasst-llama-13b-2-epochs
GGML Version: https://huggingface.co/Black-Engineer/oasst-llama13b-ggml-q4
|
yfyeung/icefall-asr-multidataset-pruned_transducer_stateless7-2023-05-04
|
yfyeung
| 2023-05-04T03:00:54Z | 0 | 3 | null |
[
"tensorboard",
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-05-04T02:34:06Z |
---
license: apache-2.0
---
Introduction This repo contains pre-trained models, checkpoints, training logs and decoding results of the following pull-request:
https://github.com/k2-fsa/icefall/pull/1010
|
4bit/koala-13B-GPTQ-4bit-128g
|
4bit
| 2023-05-04T02:54:46Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"koala",
"ShareGPT",
"gptq",
"dataset:RyokoAI/ShareGPT52K",
"dataset:Hello-SimpleAI/HC3",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-04T02:48:14Z |
---
license: other
library_name: transformers
pipeline_tag: text-generation
datasets:
- RyokoAI/ShareGPT52K
- Hello-SimpleAI/HC3
tags:
- koala
- ShareGPT
- llama
- gptq
inference: false
---
# Koala: A Dialogue Model for Academic Research
This repo contains the weights of the Koala 13B model produced at Berkeley. It is the result of combining the diffs from https://huggingface.co/young-geng/koala with the original Llama 13B model.
This version has then been quantized to 4-bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
## My Koala repos
I have the following Koala model repositories available:
**13B models:**
* [Unquantized 13B model in HF format](https://huggingface.co/TheBloke/koala-13B-HF)
* [GPTQ quantized 4bit 13B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g)
* [GPTQ quantized 4bit 13B model in GGML format for `llama.cpp`](https://huggingface.co/TheBloke/koala-13B-GPTQ-4bit-128g-GGML)
**7B models:**
* [Unquantized 7B model in HF format](https://huggingface.co/TheBloke/koala-7B-HF)
* [Unquantized 7B model in GGML format for llama.cpp](https://huggingface.co/TheBloke/koala-7b-ggml-unquantized)
* [GPTQ quantized 4bit 7B model in `pt` and `safetensors` formats](https://huggingface.co/TheBloke/koala-7B-GPTQ-4bit-128g)
* [GPTQ quantized 4bit 7B model in GGML format for `llama.cpp`](https://huggingface.co/TheBloke/koala-7B-GPTQ-4bit-128g-GGML)
## Provided files
Three model files are provided. You don't need all three - choose the one that suits your needs best!
Details of the files provided:
* `koala-13B-4bit-128g.pt`
* pt format file, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save koala-13B-4bit-128g.pt`
* `koala-13B-4bit-128g.safetensors`
* newer `safetensors` format, with improved file security, created with the latest [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) code.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors koala-13B-4bit-128g.safetensors`
* `koala-13B-4bit-128g.no-act-order.ooba.pt`
* `pt` format file, created with [oobabooga's older CUDA fork of GPTQ-for-LLaMa](https://github.com/oobabooga/GPTQ-for-LLaMa).
* This file is included primarily for Windows users, as it can be used without needing to compile the latest GPTQ-for-LLaMa code.
* It should hopefully therefore work with one-click-installers on Windows, which include the older GPTQ-for-LLaMa code.
* The older GPTQ code does not support all the latest features, so the quality may be fractionally lower.
* Command to create:
* `python3 llama.py koala-13B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save koala-13B-4bit-128g.no-act-order.ooba.pt`
## How to run in `text-generation-webui`
File `koala-13B-4bit-128g.no-act-order.ooba.pt` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).
The other two model files were created with the latest GPTQ code, and require that the latest GPTQ-for-LLaMa is used inside the UI.
Here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa
git clone https://github.com/oobabooga/text-generation-webui
mkdir -p text-generation-webui/repositories
ln -s GPTQ-for-LLaMa text-generation-webui/repositories/GPTQ-for-LLaMa
```
Then install this model into `text-generation-webui/models` and launch the UI as follows:
```
cd text-generation-webui
python server.py --model koala-13B-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
```
The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.
If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can instead use the CUDA branch:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
cd GPTQ-for-LLaMa
python setup_cuda.py install
```
Then link that into `text-generation-webui/repositories` as described above.
Or just use `koala-13B-4bit-128g.no-act-order.ooba.pt` as mentioned above.
## How the Koala delta weights were merged
The Koala delta weights were originally merged using the following commands, producing [koala-13B-HF](https://huggingface.co/TheBloke/koala-13B-HF):
```
git clone https://github.com/young-geng/EasyLM
git clone https://huggingface.co/TheBloke/llama-13b
mkdir koala_diffs && cd koala_diffs && wget https://huggingface.co/young-geng/koala/resolve/main/koala_13b_diff_v2
cd EasyLM
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_torch_to_easylm \
--checkpoint_dir=/content/llama-13b \
--output_file=/content/llama-13b-LM \
--streaming=True
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.scripts.diff_checkpoint --recover_diff=True \
--load_base_checkpoint='params::/content/llama-13b-LM' \
--load_target_checkpoint='params::/content/koala_diffs/koala_13b_diff_v2' \
--output_file=/content/koala_13b.diff.weights \
--streaming=True
PYTHON_PATH="${PWD}:$PYTHONPATH" python \
-m EasyLM.models.llama.convert_easylm_to_hf --model_size=13b \
--output_dir=/content/koala-13B-HF \
--load_checkpoint='params::/content/koala_13b.diff.weights' \
--tokenizer_path=/content/llama-13b/tokenizer.model
```
## Further info
Check out the following links to learn more about the Berkeley Koala model.
* [Blog post](https://bair.berkeley.edu/blog/2023/04/03/koala/)
* [Online demo](https://koala.lmsys.org/)
* [EasyLM: training and serving framework on GitHub](https://github.com/young-geng/EasyLM)
* [Documentation for running Koala locally](https://github.com/young-geng/EasyLM/blob/main/docs/koala.md)
## License
The model weights are intended for academic research only, subject to the
[model License of LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md),
[Terms of Use of the data generated by OpenAI](https://openai.com/policies/terms-of-use),
and [Privacy Practices of ShareGPT](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb).
Any other usage of the model weights, including but not limited to commercial usage, is strictly prohibited.
|
MDOWNLOAD/OMOECLORA
|
MDOWNLOAD
| 2023-05-04T02:48:02Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-04T02:46:16Z |
---
license: creativeml-openrail-m
---
|
Smoden/pinocchio_diff_lora_1500
|
Smoden
| 2023-05-04T02:38:17Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-04T00:47:15Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - Smoden/pinocchio_diff_lora_1500
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
stablediffusionapi/theallys-mix-iv-veri
|
stablediffusionapi
| 2023-05-04T02:00:14Z | 0 | 1 | null |
[
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-04T02:00:06Z |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# TheAlly's Mix IV: Verisimilar API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "theallys-mix-iv-veri"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/theallys-mix-iv-veri)
Credits: [View credits](https://civitai.com/?query=TheAlly%27s%20Mix%20IV%3A%20Verisimilar)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "theallys-mix-iv-veri",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
platzi/platzi-distilroberta-base-mrpc-glue-cristian-durango
|
platzi
| 2023-05-04T01:52:35Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-04T01:33:56Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: platzi-distilroberta-base-mrpc-glue-cristian-durango
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8794567062818336
---
<!-- 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. -->
# platzi-distilroberta-base-mrpc-glue-cristian-durango
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4245
- Accuracy: 0.8260
- F1: 0.8795
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5318 | 1.09 | 500 | 0.4245 | 0.8260 | 0.8795 |
| 0.3704 | 2.18 | 1000 | 0.6045 | 0.8309 | 0.8739 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
TMZN/train_MEGA
|
TMZN
| 2023-05-04T01:36:35Z | 0 | 0 | null |
[
"license:gpl-3.0",
"region:us"
] | null | 2023-05-03T07:36:51Z |
---
license: gpl-3.0
---
# train_MEGA
以马恩全集为主要数据集的训练，未完。<br>
The training using the complete works of Marx and Engels as the primary dataset is incomplete.<br>
Das Training mit den gesammelten Werken von Marx und Engels als primärem Datensatz ist unvollständig.<br>
2023年5月3日15点20分 还在手搓数据集，打算用训练小说的方法试试。
<br>
同步https://github.com/tmzncty/train_MEGA
|
juan-barsce/my_awesome_eli5_clm-model
|
juan-barsce
| 2023-05-04T01:31:51Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T01:14:01Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juan-barsce/my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juan-barsce/my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7254
- Validation Loss: 3.7653
- 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9035 | 3.7936 | 0 |
| 3.7854 | 3.7763 | 1 |
| 3.7254 | 3.7653 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ToddGoldfarb/Cadet-Medium
|
ToddGoldfarb
| 2023-05-04T01:31:07Z | 47 | 2 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"conversational",
"en",
"dataset:allenai/soda",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T02:36:53Z |
---
license: openrail
datasets:
- allenai/soda
language:
- en
pipeline_tag: conversational
---
# What is Cadet-Medium?
Inspired by Allen AI's **Cosmo-XL**, **Cadet-Medium** is a somewhat small conversational model trained off of the **SODA** dataset. **Cadet-Medium** is intended for inference at the edge (on something as small as a 2GB RAM Raspberry Pi).
**Cadet-Medium** is trained off of the **t5-base** pretrained model from Google.
If you have any questions, or any comments on improvements, please contact me at: **tcgoldfarb@gmail.com**
# Google Colab Link
Here is the link to the Google Colab file, where I walk through the process of training the model and using the SODA public dataset from AI2.
https://colab.research.google.com/drive/1uekZ0gO3GqjPwno16tV1A4Gitrl7p3ur?usp=sharing
# Get Started With Cadet-Medium
Use the code snippet below to get started with Cadet-Medium!
```
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import colorful as cf
cf.use_true_colors()
cf.use_style('monokai')
class CadetMedAgent:
def __init__(self):
print(cf.bold | cf.purple("Waking up Cadet-Medium..."))
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.tokenizer = AutoTokenizer.from_pretrained("t5-base", model_max_length=512)
self.model = AutoModelForSeq2SeqLM.from_pretrained("ToddGoldfarb/Cadet-Medium", low_cpu_mem_usage=True).to(self.device)
self.conversation_history = ""
def observe(self, observation):
self.conversation_history = self.conversation_history + observation
# The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.
if len(self.conversation_history) > 400:
self.conversation_history = self.conversation_history[112:]
def set_input(self, situation_narrative="", role_instruction=""):
input_text = "dialog: "
if situation_narrative != "":
input_text = input_text + situation_narrative
if role_instruction != "":
input_text = input_text + " <SEP> " + role_instruction
input_text = input_text + " <TURN> " + self.conversation_history
# Uncomment the line below to see what is fed to the model.
# print(input_text)
return input_text
def generate(self, situation_narrative, role_instruction, user_response):
user_response = user_response + " <TURN> "
self.observe(user_response)
input_text = self.set_input(situation_narrative, role_instruction)
inputs = self.tokenizer([input_text], return_tensors="pt").to(self.device)
# I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.
outputs = self.model.generate(inputs["input_ids"], max_new_tokens=512, temperature=1, top_p=.95,
do_sample=True)
cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
added_turn = cadet_response + " <TURN> "
self.observe(added_turn)
return cadet_response
def reset_history(self):
self.conversation_history = []
def run(self):
def get_valid_input(prompt, default):
while True:
user_input = input(prompt)
if user_input in ["Y", "N", "y", "n"]:
return user_input
if user_input == "":
return default
while True:
continue_chat = ""
# MODIFY THESE STRINGS TO YOUR LIKING :)
situation_narrative = "Imagine you are Cadet-Medium talking to ???."
role_instruction = "You are Cadet-Medium, and you are talking to ???."
self.chat(situation_narrative, role_instruction)
continue_chat = get_valid_input(cf.purple("Start a new conversation with new setup? [Y/N]:"), "Y")
if continue_chat in ["N", "n"]:
break
print(cf.blue("CM: See you!"))
def chat(self, situation_narrative, role_instruction):
print(cf.green(
"Cadet-Medium is running! Input [RESET] to reset the conversation history and [END] to end the conversation."))
while True:
user_input = input("You: ")
if user_input == "[RESET]":
self.reset_history()
print(cf.green("[Conversation history cleared. Chat with Cadet-Medium!]"))
continue
if user_input == "[END]":
break
response = self.generate(situation_narrative, role_instruction, user_input)
print(cf.blue("CM: " + response))
def main():
print(cf.bold | cf.blue("LOADING MODEL"))
CadetMed = CadetMedAgent()
CadetMed.run()
if __name__ == '__main__':
main()
```
# Citations and Special Thanks
Special thanks to Hyunwoo Kim for discussing with me the best way to use the SODA dataset. If you haven't looked into their work with SODA, Prosocial-Dialog, or COSMO, I recommend you do so! As well, read the paper on SODA!
The article is listed below.
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
```
|
rcugarte/genfonts
|
rcugarte
| 2023-05-04T01:28:39Z | 0 | 0 | null |
[
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:rcugarte/genfonts_data",
"region:us"
] |
text-to-image
| 2023-05-04T01:19:53Z |
---
datasets:
- rcugarte/genfonts_data
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
---
|
ZyXin/ppo-Pyramids_Training
|
ZyXin
| 2023-05-04T01:14:39Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-05-04T01:14:34Z |
---
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: ZyXin/ppo-Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
1008611sS/159357258
|
1008611sS
| 2023-05-04T01:12:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-05-04T01:11:38Z |
---
license: bigscience-bloom-rail-1.0
---Nanshan Mountain lies to the southeast. Since then, the worm has been a snake and the snake a fish. The Nanshan Mountain lies to the southeast of Jiehuni. The twin bird in its east, its bird green, red, two birds wings. One day in the southern Shandong Province.
|
DurangoFon/vit_model
|
DurangoFon
| 2023-05-04T00:55:55Z | 216 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-04T00:07:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1345 | 3.85 | 500 | 0.0189 | 0.9925 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Smoden/Chronicles_diff_lora_1500
|
Smoden
| 2023-05-04T00:45:11Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-03T23:27:34Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - Smoden/Chronicles_diff_lora_1500
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
huggingtweets/marcash_uk
|
huggingtweets
| 2023-05-04T00:07:28Z | 138 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-04T00:07:19Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1641976415481389056/XkRvxaLF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MARC 🍊</div>
<div style="text-align: center; font-size: 14px;">@marcash_uk</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MARC 🍊.
| Data | MARC 🍊 |
| --- | --- |
| Tweets downloaded | 349 |
| Retweets | 44 |
| Short tweets | 176 |
| Tweets kept | 129 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/njtz7k2s/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @marcash_uk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/v9r62wtl) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/v9r62wtl/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/marcash_uk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
lewdryuna/A-Himawari
|
lewdryuna
| 2023-05-03T23:55:01Z | 0 | 2 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"ja",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-05-03T23:55:01Z |
---
license: creativeml-openrail-m
language:
- ja
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
library_name: diffusers
duplicated_from: natsusakiyomi/HimawariMixs
---
<div class="flex justify-center">
<div class="container p-0 w-100">
<img class="mt-0 object-cover rounded-t-lg w-100"
style="height: 320px;"
src="https://huggingface.co/natsusakiyomi/HimawariMixs/resolve/main/image/header.jpeg"
width="100%"/>
<div class="flex px-4">
<div class="flex-auto">
<h1 class="mb-2 text-3xl font-bold leading-tight" style="color: rgb(255, 151, 0/var(--tw-text-opacity));">
HimawariMixSeries
</h1>
<p class="mb-4 text-base text-neutral-600 dark:text-neutral-200">
様々なモデルをマージした背景や細部の表現力が強いVAE内蔵型モデル
</p>
</div>
<div>
<a
href="https://twitter.com/min__san"
class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md"
style="background-color: #1da1f2">
<svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24">
<path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" />
</svg>
</a>
</div>
</div>
</div>
</div>
---
<h4>📄 ライセンス / License</h4>
<div class="px-2">
<table class="table-fixed border mt-0 text-xs">
<tbody>
<tr>
<td class="px-4 text-base" colspan="2">
<a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">
修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license
</a>
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルのクレジットを入れずに使用する<br>
Use the model without crediting the creator
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルで生成した画像を商用利用する<br>
Sell images they generate
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデルを商用の画像生成サービスで利用する</br>
Run on services that generate images for money
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span class="text-green-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M4.5 12.75l6 6 9-13.5" />
</svg>
</span>
</td>
<td>
このモデルを使用したマージモデルを共有する<br>
Share merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデル、またはこのモデルをマージしたモデルを販売する</br>
Sell this model or merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span class="text-red-500">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round" d="M6 18L18 6M6 6l12 12" />
</svg>
</span>
</td>
<td>
このモデルをマージしたモデルに異なる権限を設定する</br>
Have different permissions when sharing merges
</td>
</tr>
</tbody>
</table>
</div>
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>HimawariMix-v3</code> <small></small>
</h3>
<div>
背景強化をメインに改宗したモデルでリアル系のモデルの比率が多くなったモデル<br>
リアル系を多く含んでいるため手の破綻は他と比べて比較的出ずらい.....気がする
B型のほうが比較的扱いやすい気がします
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>HimawariMix-v2</code> <small></small>
</h3>
<div>
背景よりキャラを重視して作られたモデル<br>
v1.20v1.10やv1とは違いいろいろな場面でも使えるようになりました<br>
このHimawariMixの特徴である彩度の高さが出始めた<br>
悪く言えば器用貧乏
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>HimawariMix-v1.20 and 1.10</code> <small></small>
</h3>
<div>
HimawariMix-v1のマージ比率を変えたマイナーチェンジモデル<br>
クローズアップに特化しておりそれ以外はあまりさえない<br>
マイナーチェンジにより破綻が少なくなり安定性が増した
v1.20とv1.10の違いはVAEの違い
<hr class="my-6 h-0.5 border-t-0 opacity-100 dark:opacity-50" style="background-color: rgb(245 245 245/var(--tw-bg-opacity));">
<h3 id="blue_pencil-v7" class="mt-0 text-2xl">
<code>HimawariMix-v1</code> <small></small>
</h3>
<div>
初代HimawariMix<br>
当時あまりなかった背景とキャラクターを両立させるために作ったモデル<br>
特徴は背景が割と強いモデルなお今となっては普通でHimawariMixの特徴である彩度の高さはこの時点ではまだない
---
# 作者&連絡先
Twiter: [@min__san](https://twitter.com/min__san)
|
hashiikhan/whisper-small-Eng-1
|
hashiikhan
| 2023-05-03T23:00:30Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:speech_commands",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T22:50:33Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- wer
model-index:
- name: whisper-small-Eng-1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: speech_commands
type: speech_commands
config: v0.01
split: test
args: v0.01
metrics:
- name: Wer
type: wer
value: 239.6
---
<!-- 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-Eng-1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0620
- Wer: 239.6
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 7.156 | 0.01 | 5 | 6.9727 | 256.4 |
| 7.5392 | 0.02 | 10 | 5.0620 | 239.6 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jkkawach/ppo-Huggy
|
jkkawach
| 2023-05-03T23:00:20Z | 15 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-05-03T23:00:12Z |
---
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: jkkawach/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
poorviachar/my_awesome_qa_model
|
poorviachar
| 2023-05-03T22:51:55Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-02T22:31:23Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: poorviachar/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# poorviachar/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6838
- Validation Loss: 1.8516
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.6824 | 2.6359 | 0 |
| 2.0129 | 1.8516 | 1 |
| 1.6838 | 1.8516 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
sqllama/lora-spider-dono
|
sqllama
| 2023-05-03T22:36:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-04-30T01:00:50Z |
## Setup Notes
For this model, a VM with 2 T4 GPUs was used.
Note 1. Output directory was initially lora-alpaca and then contents were moved to new folder when initializing git repository.
## Log
(sqltest) chrisdono@deep-learning-duo-t4-3:~/alpaca-lora$ WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model 'decapoda-research/llama-7b-hf' --data_path 'spider' --output_dir './lora-alpaca' --num_epochs 10 --batch_size 32 --micro_batch_size 16 --learning_rate '9e-5' --add_eos_token
Adding last loss values not included in trainer json file from last checkpoint.
{'loss': 0.241, 'learning_rate': 1.0040816326530613e-05, 'epoch': 8.98}
{'loss': 0.2343, 'learning_rate': 9.42857142857143e-06, 'epoch': 9.04}
{'loss': 0.2376, 'learning_rate': 8.816326530612245e-06, 'epoch': 9.11}
{'loss': 0.2355, 'learning_rate': 8.204081632653062e-06, 'epoch': 9.17}
{'loss': 0.229, 'learning_rate': 7.591836734693877e-06, 'epoch': 9.24}
{'loss': 0.2325, 'learning_rate': 6.979591836734694e-06, 'epoch': 9.3}
{'loss': 0.24, 'learning_rate': 6.367346938775511e-06, 'epoch': 9.36}
{'loss': 0.2438, 'learning_rate': 5.755102040816327e-06, 'epoch': 9.43}
{'loss': 0.2391, 'learning_rate': 5.142857142857143e-06, 'epoch': 9.49}
{'loss': 0.2351, 'learning_rate': 4.530612244897959e-06, 'epoch': 9.55}
{'loss': 0.2289, 'learning_rate': 3.9183673469387755e-06, 'epoch': 9.62}
{'loss': 0.2294, 'learning_rate': 3.3061224489795924e-06, 'epoch': 9.68}
{'loss': 0.2344, 'learning_rate': 2.693877551020408e-06, 'epoch': 9.75}
{'loss': 0.2358, 'learning_rate': 2.0816326530612247e-06, 'epoch': 9.81}
{'loss': 0.2365, 'learning_rate': 1.469387755102041e-06, 'epoch': 9.87}
{'loss': 0.2309, 'learning_rate': 8.571428571428572e-07, 'epoch': 9.94}
{'loss': 0.2438, 'learning_rate': 2.4489795918367347e-07, 'epoch': 10.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
{'train_runtime': 17144.6766, 'train_samples_per_second': 2.916, 'train_steps_per_second': 0.092, 'train_loss': 0.41175747267000234, 'epoch': 10.0}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1570
/1570 [4:45:44<00:00, 10.92s/it]
|
mattjmattj/HF_RL_unit4_reinforce_CartPole
|
mattjmattj
| 2023-05-03T22:32:50Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-03T22:32:40Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: HF_RL_unit4_reinforce_CartPole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 441.80 +/- 87.01
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
|
artemfilipenko/keyphrase-generation-bart-large-trained-on-augmented-and-default-inspec
|
artemfilipenko
| 2023-05-03T22:16:04Z | 105 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"en",
"dataset:midas/inspec",
"dataset:artemfilipenko/synonyms-augmented-5x-inspec",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-03T22:06:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- midas/inspec
- artemfilipenko/synonyms-augmented-5x-inspec
model-index:
- name: synonyms_5000_plus_3000_default_3_epoch
results: []
language:
- en
metrics:
- f1
---
<!-- 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. -->
# synonyms_5000_plus_3000_default_3_epoch
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the midas/inspec generation dataset, concatenated with data augmented custom artemfilipenko/synonyms-augmented-5x-inspec dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7956
- F1@5ext: 0.4590
- P@5ext: 0.6116
- R@5ext: 0.4109
- F1@10ext: 0.5403
- P@10ext: 0.5953
- R@10ext: 0.5374
- F1@5abs: 0.2019
- P@5abs: 0.3080
- R@5abs: 0.1721
- F1@10abs: 0.2307
- P@10abs: 0.3066
- R@10abs: 0.2109
- F1@oext: 0.5427
- P@oext: 0.6045
- R@oext: 0.5246
- F1@oabs: 0.2316
- P@oabs: 0.3079
- R@oabs: 0.2094
## 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: 10
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 2.6.1
- Tokenizers 0.13.1
|
jajsmith/dsn_afrispeech
|
jajsmith
| 2023-05-03T21:54:22Z | 75 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:tobiolatunji/afrispeech-200",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-05-03T17:17:19Z |
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- tobiolatunji/afrispeech-200
model-index:
- name: Whisper Small En - Owos
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. -->
# Whisper Small En - Owos
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AfriSpeech_j dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6865
- eval_wer: 29.3845
- eval_runtime: 1774.5798
- eval_samples_per_second: 1.691
- eval_steps_per_second: 0.211
- epoch: 0.06
- step: 250
## 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
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.27.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
vldnechai/poca-SoccerTwos
|
vldnechai
| 2023-05-03T21:39:07Z | 36 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-05-03T21:37:51Z |
---
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: vldnechai/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jploski/llama-7b-hf
|
jploski
| 2023-05-03T21:32:32Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T21:23:14Z |
---
license: other
---
Note: this is yahma/llama-7b-hf with checkpoint shards split into smaller files in order to enable loading in restricted memory environments like free Google Colab. The remaining description below is copied from yahma/llama-7b-hf.
LLaMA-7B converted to work with git head Transformers/HuggingFace on April 8, 2023. This version should resolve the EOS token issues.
This is under a special license, please see the LICENSE file for details.
This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file).
You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.
--
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.
|
jfecunha/arquivo-layoutxml-model
|
jfecunha
| 2023-05-03T21:32:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-04-27T08:20:22Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: arquivo-layoutxml-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. -->
# arquivo-layoutxml-model
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2997
- Ategory Precision: 0.8719
- Ategory Recall: 0.8491
- Ategory F1: 0.8603
- Ategory Number: 497
- Itle Precision: 0.8745
- Itle Recall: 0.8971
- Itle F1: 0.8857
- Itle Number: 2508
- One Precision: 0.8855
- One Recall: 0.8855
- One F1: 0.8855
- One Number: 2951
- Ubtitle Precision: 0.9494
- Ubtitle Recall: 0.9774
- Ubtitle F1: 0.9632
- Ubtitle Number: 23695
- Overall Precision: 0.9356
- Overall Recall: 0.9593
- Overall F1: 0.9473
- Overall Accuracy: 0.9629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tooucci/CartPole
|
tooucci
| 2023-05-03T21:22:55Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-21T23:38:15Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole
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
|
AliiaR/t5-small-finetuned-model
|
AliiaR
| 2023-05-03T21:01:54Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-05-02T20:28:10Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: AliiaR/t5-small-finetuned-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# AliiaR/t5-small-finetuned-model
This model is a fine-tuned version of [AliiaR/t5-small-finetuned-model](https://huggingface.co/AliiaR/t5-small-finetuned-model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4127
- Validation Loss: 1.1016
- Train Rouge1: 14.9189
- Train Rouge2: 3.7554
- Train Rougel: 13.6461
- Train Rougelsum: 13.6801
- Train Gen Len: 13.4191
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 1.4127 | 1.1016 | 14.9189 | 3.7554 | 13.6461 | 13.6801 | 13.4191 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pandma/es_pipeline
|
pandma
| 2023-05-03T20:54:53Z | 4 | 0 |
spacy
|
[
"spacy",
"token-classification",
"es",
"model-index",
"region:us"
] |
token-classification
| 2023-05-03T20:54:28Z |
---
tags:
- spacy
- token-classification
language:
- es
model-index:
- name: es_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.998766394
- name: NER Recall
type: recall
value: 0.9988961039
- name: NER F Score
type: f_score
value: 0.9988312447
---
| Feature | Description |
| --- | --- |
| **Name** | `es_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **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 (13 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `BILLING_PERIOD_END`, `BILLING_PERIOD_START`, `BILL_OWNER`, `COMPANY_NAME`, `CUPS`, `DIRECTION`, `ENERGY_P1_PRICE`, `ENERGY_P2_PRICE`, `ENERGY_P3_PRICE`, `NIF`, `POWER_P1_PRICE`, `POWER_P2_PRICE`, `TOTAL_IMPORTE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.88 |
| `ENTS_P` | 99.88 |
| `ENTS_R` | 99.89 |
| `TRANSFORMER_LOSS` | 6425.46 |
| `NER_LOSS` | 41888.91 |
|
AnshulRustogi/bert-base-multilingual-cased
|
AnshulRustogi
| 2023-05-03T20:52:58Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-03T19:55:21Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased1
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-multilingual-cased1
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8440
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 214 | 4.0240 |
| No log | 2.0 | 428 | 2.6347 |
| 4.063 | 3.0 | 642 | 2.3167 |
| 4.063 | 4.0 | 856 | 2.1420 |
| 2.3039 | 5.0 | 1070 | 2.0258 |
| 2.3039 | 6.0 | 1284 | 1.9483 |
| 2.3039 | 7.0 | 1498 | 1.8992 |
| 1.9096 | 8.0 | 1712 | 1.8669 |
| 1.9096 | 9.0 | 1926 | 1.8460 |
| 1.7069 | 10.0 | 2140 | 1.8440 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
uisikdag/ayla_ozetler2006_bertuncased
|
uisikdag
| 2023-05-03T20:52:16Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-03T16:04:41Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ayla_ozetler200_bertuncased
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. -->
# ayla_ozetler200_bertuncased
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3311
- Accuracy: 0.9
## Model description
## 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
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.89 | 6 | 1.6870 | 0.4278 |
| 1.7467 | 1.93 | 13 | 1.1508 | 0.6972 |
| 1.0982 | 2.96 | 20 | 0.7106 | 0.8028 |
| 1.0982 | 4.0 | 27 | 0.5116 | 0.85 |
| 0.5588 | 4.89 | 33 | 0.4031 | 0.8694 |
| 0.3365 | 5.93 | 40 | 0.3696 | 0.8778 |
| 0.3365 | 6.96 | 47 | 0.3394 | 0.8806 |
| 0.2345 | 8.0 | 54 | 0.3397 | 0.9 |
| 0.1791 | 8.89 | 60 | 0.3311 | 0.9 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.11.0
|
openmmlab/upernet-swin-base
|
openmmlab
| 2023-05-03T20:51:22Z | 979 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"upernet",
"vision",
"image-segmentation",
"en",
"arxiv:1807.10221",
"arxiv:2103.14030",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2023-01-13T14:34:17Z |
---
language: en
license: mit
tags:
- vision
- image-segmentation
model_name: openmmlab/upernet-swin-base
---
# UperNet, Swin Transformer base-sized backbone
UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al.
Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030).
Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM).
Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.
## Intended uses & limitations
You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for
fine-tuned versions (with various backbones) on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).
|
alesthehuman/dqn-SpaceInvadersNoFrameskip-v4
|
alesthehuman
| 2023-05-03T20:51:09Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-03T20:50:32Z |
---
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: 599.50 +/- 212.67
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 alesthehuman -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 alesthehuman -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 alesthehuman
```
## 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)])
```
|
ThanHitt/FishTreeRock_Classifier_v1
|
ThanHitt
| 2023-05-03T20:37:34Z | 241 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-05-03T20:37:27Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: FishTreeRock_Classifier_v1
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9850746393203735
---
# FishTreeRock_Classifier_v1
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### fish
#### rock
#### tree
|
ratish/DBERT_MAKE_NewData_v1
|
ratish
| 2023-05-03T20:28:13Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-03T20:24:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_MAKE_NewData_v1
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. -->
# ratish/DBERT_MAKE_NewData_v1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5238
- Validation Loss: 0.6256
- Train Accuracy: 0.8909
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 240, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.2333 | 1.9085 | 0.6545 | 0 |
| 1.6567 | 1.3839 | 0.6727 | 1 |
| 1.2308 | 1.0679 | 0.8364 | 2 |
| 0.9605 | 0.8879 | 0.8364 | 3 |
| 0.8155 | 0.7807 | 0.8364 | 4 |
| 0.7106 | 0.7242 | 0.8545 | 5 |
| 0.6365 | 0.6794 | 0.8182 | 6 |
| 0.5894 | 0.6334 | 0.8909 | 7 |
| 0.5446 | 0.6293 | 0.8909 | 8 |
| 0.5238 | 0.6256 | 0.8909 | 9 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
KatarLegacy/kebayabali
|
KatarLegacy
| 2023-05-03T20:23:39Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T20:23:02Z |
---
license: creativeml-openrail-m
---
|
KatarLegacy/demon_cosplay_outfit
|
KatarLegacy
| 2023-05-03T20:15:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-03T20:14:19Z |
---
license: creativeml-openrail-m
---
|
m5rcelo/a2c-AntBulletEnv-v0
|
m5rcelo
| 2023-05-03T20:10:00Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"tensorboard",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-05-03T18:26:18Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1556.28 +/- 442.97
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
nergaldarski/mistoonAnime
|
nergaldarski
| 2023-05-03T19:53:18Z | 0 | 5 | null |
[
"region:us"
] | null | 2023-05-03T19:41:13Z |
CivitAI: https://civitai.com/models/24149/mistoonanime
|
Multi-Domain-Expert-Learning/expert-pubmed_abstracts
|
Multi-Domain-Expert-Learning
| 2023-05-03T19:48:41Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-03T13:01:42Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: expert-pubmed_abstracts
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. -->
# expert-pubmed_abstracts
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2407
- Accuracy: 0.5368
## 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- 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
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2802 | 0.01 | 500 | 2.2553 | 0.5345 |
| 2.2277 | 0.02 | 1000 | 2.2407 | 0.5368 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
rohitraman/my_new_model
|
rohitraman
| 2023-05-03T19:44:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-21T10:19:17Z |
---
tags:
- generated_from_trainer
model-index:
- name: my_new_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_new_model
This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- 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
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
athenasarch/ppo-LunarLander-v2
|
athenasarch
| 2023-05-03T19:42:46Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-04-28T21:34:08Z |
---
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: 278.28 +/- 18.48
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
...
```
|
aashay96/indic-BloomLM
|
aashay96
| 2023-05-03T19:09:44Z | 0 | 5 | null |
[
"region:us"
] | null | 2023-04-27T10:07:45Z |
# Indic Language Bloom Model Training
This repository contains the code and resources for fine-tuning the Huggingface Bloom model on the Indic language dataset using Low-Rank Adaptation (LoRA). The goal is to create a high-performance language model specifically tailored to Indic languages.
## Dataset
The dataset used for training is provided by AI4Bharat. I have uploaded it to huggingface hub at:
- [Processed Indic Language Corpus](https://huggingface.co/datasets/aashay96/indic_language_corpus/tree/main)
## Progress
### Completed
- [x] Low-Rank Adaptation fine-tuning of the Bloom model on streaming data
- [x] Single checkpoint available (training logs at [Weights & Biases](https://wandb.ai/indic-lm/huggingface/runs/7kq2m62v/))
### To Do
- [ ] Benchmark current multilingual LLMs on IndicGLUE using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
- [ ] Integrate DeepSpeed for better resource utilization
- [ ] Convert current instruction dataset to Indic languages and train (dolly v2 dataset, distilled from GPT, etc.)
- [ ] Model doesn't stop producing text - how to fix?
- [ ] Deploy RLHF community app using [Cheese](https://github.com/CarperAI/cheese)
## Using the Model
```bash
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "aashay96/indic-BloomLM"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
batch = tokenizer("आप कैसे हैं", return_tensors='pt')
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=10)
print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
|
DKYoon/mt5-xxl-lm-adapt
|
DKYoon
| 2023-05-03T19:01:24Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2205.12647",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-04-13T19:03:29Z |
---
license: apache-2.0
---
🤗 Language model initialized from mT5 and trained for an additional 100K steps on the Prefix LM objective using mC4 data.
Paper: [Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation](https://arxiv.org/abs/2205.12647)
Authors: Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, Noah Constant
PyTorch port of the original Flax checkpoint at [Google/T5X repository](https://github.com/google-research/t5x).
|
ratish/gpt_v1.4.1
|
ratish
| 2023-05-03T18:58:06Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-03T18:52:39Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: ratish/gpt_v1.4.1
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. -->
# ratish/gpt_v1.4.1
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:
- Train Loss: 1.9635
- Validation Loss: 0.8785
- Train Accuracy: 0.8889
- Epoch: 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9635 | 0.8785 | 0.8889 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
SuzieCreamchease/God_Knitting_Sheep
|
SuzieCreamchease
| 2023-05-03T18:50:37Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-05-03T17:47:58Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bright1/fine-tuned-twitter-Roberta-base-sentiment
|
bright1
| 2023-05-03T18:39:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-03T01:13:01Z |
---
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-twitter-Roberta-base-sentiment
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. -->
# fine-tuned-twitter-Roberta-base-sentiment
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5453
- eval_accuracy: {'accuracy': 0.7915}
- eval_f1score: {'f1': 0.790972084150606}
- eval_runtime: 68.7486
- eval_samples_per_second: 29.092
- eval_steps_per_second: 3.636
- step: 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-07
- 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-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1399
- num_epochs: 7
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.