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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 18:30:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 544
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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hw2942/bert-base-chinese-SSEC
|
hw2942
| 2023-08-14T03:38:11Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T03:25:44Z |
---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-chinese-wallstreetcn-morning-news-market-overview-SSEC-v3
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-chinese-wallstreetcn-morning-news-market-overview-SSEC-v3
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1007
- Accuracy: 0.6875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 34 | 2.2173 | 0.7188 |
| No log | 2.0 | 68 | 1.8368 | 0.7188 |
| No log | 3.0 | 102 | 2.7822 | 0.625 |
| No log | 4.0 | 136 | 2.3597 | 0.7188 |
| No log | 5.0 | 170 | 3.3032 | 0.5312 |
| No log | 6.0 | 204 | 2.9527 | 0.6562 |
| No log | 7.0 | 238 | 2.7575 | 0.6875 |
| No log | 8.0 | 272 | 2.9714 | 0.6875 |
| No log | 9.0 | 306 | 3.0941 | 0.6875 |
| No log | 10.0 | 340 | 3.1007 | 0.6875 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
mrkusypl/Miroslaw-Stabinski
|
mrkusypl
| 2023-08-14T02:53:11Z | 0 | 0 | null |
[
"pl",
"region:us"
] | null | 2023-08-07T20:26:39Z |
---
language:
- pl
---
<center>
<img src="https://cdn.discordapp.com/attachments/1138209218969731183/1138209219384979597/240774873_122099140169811_8790049852222389754_n.jpg"></img>
<h1>Mirosław Stabiński (RVC v2) (Mangio Crepe 64) (1125 Epochs)</h1>
**Model by:** kusy <br/>
**Voice Actor:** Mirosław Stabiński <br/>
**Dataset:** 00:21:47 <br/>
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1138209218969731183/1138209243686776903/example.mp3" type="audio/mpeg">
</audio><br />
<audio controls>
<source src="https://cdn.discordapp.com/attachments/1138209218969731183/1138211956268998697/gadanie.wav" type="audio/wav">
</audio>
<a href="https://huggingface.co/mrkusypl/Miroslaw-Stabinski/resolve/main/Miros%C5%82aw%20Stabi%C5%84ski%20%5B1125%20epoch%20%2B%20RVC%20v2%5D.zip">Download or copy the link</a>
</center>
|
hoaio/q-Taxi-v3
|
hoaio
| 2023-08-14T02:27:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-06-20T07:34:26Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hoaio/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Evan-Lin/Bart-large-abs-amazon-entailment
|
Evan-Lin
| 2023-08-14T01:55:53Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-08-14T01:43:21Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpetrgbosh/Evan-Lin/Bart-large-abs-amazon-entailment")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpetrgbosh/Evan-Lin/Bart-large-abs-amazon-entailment")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpetrgbosh/Evan-Lin/Bart-large-abs-amazon-entailment")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Evan-Lin/Bart-large-abs-amazon-allure2
|
Evan-Lin
| 2023-08-14T01:55:14Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-08-14T01:41:17Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpe0oa5rsb/Evan-Lin/Bart-large-abs-amazon-allure2")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpe0oa5rsb/Evan-Lin/Bart-large-abs-amazon-allure2")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpe0oa5rsb/Evan-Lin/Bart-large-abs-amazon-allure2")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Evan-Lin/Bart-large-abs-amazon-entailment2-rouge
|
Evan-Lin
| 2023-08-14T01:33:15Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-08-14T01:15:41Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpghor1ugg/Evan-Lin/Bart-large-abs-amazon-entailment2-rouge")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpghor1ugg/Evan-Lin/Bart-large-abs-amazon-entailment2-rouge")
model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpghor1ugg/Evan-Lin/Bart-large-abs-amazon-entailment2-rouge")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
csukuangfj/sherpa-onnx-streaming-paraformer-bilingual-zh-en
|
csukuangfj
| 2023-08-14T01:27:14Z | 0 | 1 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2023-08-14T01:25:23Z |
---
license: apache-2.0
---
`*.onnx` models are converted from
https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary
See also https://huggingface.co/csukuangfj/streaming-paraformer-zh
Note: We have used
https://huggingface.co/csukuangfj/streaming-paraformer-zh/blob/main/add-model-metadata.py
to add meta data to `model.onnx` and renamed it to `encoder.onnx`.
|
ckandemir/a2c-PandaReachDense-v3
|
ckandemir
| 2023-08-14T01:08:29Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T01:02:42Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
gregorgabrovsek/SloBertAA_Top10_WithOOC_082023
|
gregorgabrovsek
| 2023-08-14T01:03:46Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-13T17:09:23Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SloBertAA_Top10_WithOOC_082023
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. -->
# SloBertAA_Top10_WithOOC_082023
This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7250
- Accuracy: 0.9087
- F1: 0.9077
- Precision: 0.9076
- Recall: 0.9087
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3963 | 1.0 | 16293 | 0.3859 | 0.8775 | 0.8765 | 0.8784 | 0.8775 |
| 0.3207 | 2.0 | 32586 | 0.3425 | 0.8928 | 0.8928 | 0.8949 | 0.8928 |
| 0.2433 | 3.0 | 48879 | 0.3723 | 0.9011 | 0.8995 | 0.8999 | 0.9011 |
| 0.1874 | 4.0 | 65172 | 0.4615 | 0.9018 | 0.8999 | 0.9004 | 0.9018 |
| 0.1537 | 5.0 | 81465 | 0.5215 | 0.9026 | 0.9011 | 0.9014 | 0.9026 |
| 0.1136 | 6.0 | 97758 | 0.5769 | 0.9044 | 0.9027 | 0.9029 | 0.9044 |
| 0.067 | 7.0 | 114051 | 0.6370 | 0.9060 | 0.9039 | 0.9041 | 0.9060 |
| 0.0514 | 8.0 | 130344 | 0.6676 | 0.9058 | 0.9047 | 0.9049 | 0.9058 |
| 0.0275 | 9.0 | 146637 | 0.7306 | 0.9064 | 0.9054 | 0.9061 | 0.9064 |
| 0.0243 | 10.0 | 162930 | 0.7250 | 0.9087 | 0.9077 | 0.9076 | 0.9087 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.8.0
- Datasets 2.10.1
- Tokenizers 0.13.2
|
rdpb/lora-trained-xl-colab2
|
rdpb
| 2023-08-14T00:50:18Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-13T23:00:59Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of thaisluna
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - rdpb/lora-trained-xl-colab2
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of thaisluna using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
brunoboat/ppo-LunarLander-8
|
brunoboat
| 2023-08-14T00:42:45Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T00:11:54Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -145.84 +/- 70.15
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'brunoboat/ppo-LunarLander-8'
'batch_size': 512
'minibatch_size': 128}
```
|
C-Lo/balanced_gendered-dataset
|
C-Lo
| 2023-08-14T00:21:59Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T00:18:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: balanced_gendered-dataset
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. -->
# balanced_gendered-dataset
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Nelver28/grailsolver-test-10
|
Nelver28
| 2023-08-14T00:13:43Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T00:13:27Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
mkshing/novelai-tokenizer-v1
|
mkshing
| 2023-08-14T00:12:17Z | 0 | 0 | null |
[
"tokenizer",
"novelai",
"sentencepiece",
"en",
"ja",
"license:gpl-2.0",
"region:us"
] | null | 2023-07-04T06:41:50Z |
---
license: gpl-2.0
language:
- en
- ja
tags:
- tokenizer
- novelai
- sentencepiece
---
# NovelAI Tokenizer v1
This repository is exactly the same as [NovelAI/nerdstash-tokenizer-v1](https://huggingface.co/NovelAI/nerdstash-tokenizer-v1),
but the config has been changed to address the following points (the sp model itself is not changed).
- Load as T5Tokenizer
- Enable to decode digits (In the original, digits are registered as `additional_special_tokens`, so if `skip_special_tokens=True` when decoding, the digits are also skipped.)
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mkshing/novelai-tokenizer-v1", use_fast=False)
text = "1+1=3"
tokenizer.decode(tokenizer.encode(text), skip_special_tokens=True)
# '1+1=3'
```
|
HexHands/finishABOUTME
|
HexHands
| 2023-08-14T00:04:07Z | 153 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-01T01:56:24Z |
---
license: cc-by-4.0
language: en
tags:
- text-generation
pipeline_tag: text-generation
widget:
- text: "My name is "
- text: "I believe that I need to be more friendly."
- text: "Follow @griffpatch!"
- text: "How will my projects get better?"
---
# finishABOUTME
finishABOUTME is a torch model which was trained on 2000 Scratch About Me sections.
It is meant to finish any About Me section!
# Example
Input: This Scratch Studio will reach 100 followers in a few days!\n
Output: This Scratch Studio will reach 100 followers in a few days!\nThis studio here so much slower. Sorry for the inconveni have all, but we get every monday feel free to add projects about duckling Pond!\n\nThe Duckling Pond
|
ckandemir/ML-Agents-Pyramids
|
ckandemir
| 2023-08-13T23:58:35Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-08-13T23:58:32Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ckandemir/ML-Agents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e9_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T23:23:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T23:23:15Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
AOLCDROM/WAV2LIP-HQ-Updated-MIRROR
|
AOLCDROM
| 2023-08-13T23:22:41Z | 0 | 3 | null |
[
"region:us"
] | null | 2023-08-13T23:14:06Z |
This is a mirror of the weights for the Wav2Lip-HQ-Updated repo, because the linked files on Google Drive appear to be incorrect or down.
License follows oriignal authors intent.
---
license: other
---
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e9_s108_v4_l5_v50
|
KingKazma
| 2023-08-13T23:20:22Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T23:20:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e8_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T23:15:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T23:15:45Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
AmelieSchreiber/esm2_t12_35M_UR50D_RNA_LoRA_weighted
|
AmelieSchreiber
| 2023-08-13T23:13:58Z | 2 | 1 |
peft
|
[
"peft",
"transformers",
"biology",
"esm",
"esm2",
"protein",
"protein language model",
"en",
"license:mit",
"region:us"
] | null | 2023-08-13T23:01:51Z |
---
library_name: peft
license: mit
language:
- en
tags:
- transformers
- biology
- esm
- esm2
- protein
- protein language model
---
# ESM-2 RNA Binding Site LoRA
This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation (LoRA) of
the [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) model for the (binary) token classification task of
predicting RNA binding sites of proteins. You can also find a version of this model
that was fine-tuned without LoRA [here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_UR50D_rna_binding_site_predictor).
## Training procedure
This is a Low Rank Adaptation (LoRA) of `esm2_t12_35M_UR50D`,
trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
using a `85/15` train/test split. This model was trained with class weighting due to the imbalanced nature
of the RNA binding site dataset (fewer binding sites than non-binding sites). This model has slightly improved
precision, recall, and F1 score over [AmelieSchreiber/esm2_t12_35M_weighted_lora_rna_binding](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_weighted_lora_rna_binding)
but may suffer from mild overfitting, as indicated by the training loss being slightly lower than the eval loss (see metrics below).
If you are searching for binding sites and aren't worried about false positives, the higher recall may make this model
preferable to the other RNA binding site predictors.
You can train your own version
using [this notebook](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_weighted_lora_rna_binding/blob/main/LoRA_binding_sites_no_sweeps_v2.ipynb)!
You just need the RNA `binding_sites.xml` file [found here](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites).
You may also need to run some `pip install` statements at the beginning of the script. If you are running in colab run:
```python
!pip install transformers[torch] datasets peft -q
```
```python
!pip install accelerate -U -q
```
Try to improve upon these metrics by adjusting the hyperparameters:
```
{'eval_loss': 0.500779926776886,
'eval_precision': 0.1708695652173913,
'eval_recall': 0.8397435897435898,
'eval_f1': 0.2839595375722543,
'eval_auc': 0.771835775620126,
'epoch': 11.0}
{'loss': 0.4171,
'learning_rate': 0.00032491416877500004,
'epoch': 11.43}
```
A similar model can also be trained using the Github with a training script and conda env YAML, which can be
[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). This version uses wandb sweeps for hyperparameter search.
However, it does not use class weighting.
### Framework versions
- PEFT 0.4.0
## Using the Model
To use the model, try running the following pip install statements:
```python
!pip install transformers peft -q
```
then try tunning:
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch
# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_UR50D_RNA_LoRA_weighted"
# ESM2 base model
base_model_path = "facebook/esm2_t12_35M_UR50D"
# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)
# Ensure the model is in evaluation mode
loaded_model.eval()
# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT" # Replace with your actual sequence
# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')
# Run the model
with torch.no_grad():
logits = loaded_model(**inputs).logits
# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)
# Define labels
id2label = {
0: "No binding site",
1: "Binding site"
}
# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
if token not in ['<pad>', '<cls>', '<eos>']:
print((token, id2label[prediction]))
```
|
D4ve-R/yellow-lora-sd15
|
D4ve-R
| 2023-08-13T23:09:19Z | 3 | 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
| 2023-08-12T17:29:27Z |
---
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 - D4ve-R/yellow-lora-sd15
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.
|
FireHead90544/RudraRVCs
|
FireHead90544
| 2023-08-13T23:08:19Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-08-09T15:39:45Z |
---
license: openrail
---
# RVCs - Some of the voices I trained
**Seiya Ryuuguuin - The Hero Is Overpowered But Overly Cautious (JP VA: Yuuichirou Umehara)**
Currently, these ones are available:
- ## [Seiya Ryuuguuin RVC v2 Mangio-Crepe (340 Epochs, 5440 Steps)](https://huggingface.co/FireHead90544/RudraRVCs/resolve/main/SeiyaRyuuguuinRVC.zip)
- ## [Seiya Ryuuguuin RVC v2 RMVPE (300 Epochs, 6300 Steps)](https://huggingface.co/FireHead90544/RudraRVCs/resolve/main/SeiyaRyuuguuinV2.zip) # This seems to perform better
- ## [Seiya Ryuuguuin Max RVC v2 RMVPE (400 Epochs, 8400 Steps)](https://huggingface.co/FireHead90544/RudraRVCs/resolve/main/SeiyaRyuuguuinMax.zip) # Probably the best one
## Samples
- ### Mangio-Crepe
- [NEFFEX - Cold](https://cdn.discordapp.com/attachments/1090766429785178142/1138861234561753249/Seiya_Ryuuguuin_-_Cold.mp3)
- [Kenshi Yonezu - Kick Back](https://cdn.discordapp.com/attachments/1090766429785178142/1138861234951819264/Seiya_Ryuuguuin_-_Kick_Back.mp3)
- ### RMVPE
- [YOASOBI - Running Into The Night](https://cdn.discordapp.com/attachments/549264174753120267/1138908849076703332/Seiya_Ryuuguuin_-_Racing_Into_The_Night.mp3)
- [Tk From Ling Tosite Sigure - Unravel](https://cdn.discordapp.com/attachments/549264174753120267/1138908849789734972/Seiya_Ryuuguuin_-_Unravel.mp3)
- [Jin Hashimoto - Stand Proud](https://cdn.discordapp.com/attachments/549264174753120267/1138908849424834741/Seiya_Ryuuguuin_-_Stand_Proud.mp3)
- [KSUKE - Contradiction](https://cdn.discordapp.com/attachments/549264174753120267/1138908848749551636/Seiya_Ryuuguuin_-_Contradiction.mp3)
- [Smash Mouth - All Star](https://cdn.discordapp.com/attachments/549264174753120267/1138908850137858189/Seiya_Ryuuguuin_-_All_Star.mp3)
- [OxT - Clattanoia](https://cdn.discordapp.com/attachments/549264174753120267/1138908850469216327/Seiya_Ryuuguuin_-_Clattanoia.mp3)
- <video controls width="640" height="360">
<source src="https://cdn.discordapp.com/attachments/1138965403658362910/1139679982717767870/Cupid.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
- <video controls width="640" height="360">
<source src="https://cdn.discordapp.com/attachments/1138965403658362910/1140419271772606474/Yoru_Ni_Kakeru.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e7_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T23:08:18Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T23:08:14Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
camus-ng/lora-trained-xl-cory-5
|
camus-ng
| 2023-08-13T23:07:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-13T14:01:35Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of <ntvc> man
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - camus-ng/lora-trained-xl-cory-5
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of <ntvc> man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e6_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T23:00:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T23:00:44Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
RohitKeswani/flan_t5_base_peft
|
RohitKeswani
| 2023-08-13T22:54:32Z | 2 | 1 |
peft
|
[
"peft",
"Summarization",
"summarization",
"region:us"
] |
summarization
| 2023-08-13T22:43:54Z |
---
library_name: peft
tags:
- Summarization
pipeline_tag: summarization
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e5_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T22:53:17Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:53:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e4_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T22:45:46Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:45:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T22:38:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:38:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s108_v4_l5_v50
|
KingKazma
| 2023-08-13T22:32:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:32:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
platzi/platzi-distilroberta-base-mrpc-glue-angrim
|
platzi
| 2023-08-13T22:31:39Z | 103 | 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
| 2023-08-13T21:44:25Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
widget:
- text: ["Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.",
"Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998."]
example_title: Not Equivalent
- text: ["Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.",
"With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier."]
example_title: Equivalent
model-index:
- name: platzi-distilroberta-base-mrpc-glue-angrim
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: 0.8284313725490197
- name: F1
type: f1
value: 0.8771929824561404
---
<!-- 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-angrim
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: 0.3994
- Accuracy: 0.8284
- F1: 0.8772
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5211 | 1.09 | 500 | 0.3994 | 0.8284 | 0.8772 |
| 0.3565 | 2.18 | 1000 | 0.5487 | 0.8456 | 0.8857 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
redstonehero/meinahentai_v4
|
redstonehero
| 2023-08-13T22:29:04Z | 29 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T20:13:29Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
redstonehero/meinapastel_v6
|
redstonehero
| 2023-08-13T22:28:59Z | 29 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T20:13:32Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e1_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T22:23:15Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:23:12Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
ckandemir/ppo-SnowballTarget
|
ckandemir
| 2023-08-13T22:16:59Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-13T22:16:57Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ckandemir/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e1_s108_v4_l5_v50
|
KingKazma
| 2023-08-13T22:16:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:16:34Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e0_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T22:15:45Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:15:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e9_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T22:03:03Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:03:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
langdonh/en_student_name_detector
|
langdonh
| 2023-08-13T22:02:34Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
token-classification
| 2023-08-13T22:02:11Z |
---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_student_name_detector
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7230769231
- name: NER Recall
type: recall
value: 0.734375
- name: NER F Score
type: f_score
value: 0.7286821705
---
|
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e-1_s108_v4_l5_v50
|
KingKazma
| 2023-08-13T22:00:43Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T22:00:41Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e9_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:55:36Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:55:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
BrendaScar/dqn-SpaceInvadersNoFrameskip-v4
|
BrendaScar
| 2023-08-13T21:53:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-13T21:52:53Z |
---
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: 657.50 +/- 163.33
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga BrendaScar -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 BrendaScar -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 BrendaScar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e7_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:45:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:45:46Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Wanaldino/lora-trained-xl-colab
|
Wanaldino
| 2023-08-13T21:43:30Z | 0 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-08-13T19:54:46Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of a women
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - Wanaldino/lora-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of a women using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
redstonehero/cetusmix_v4
|
redstonehero
| 2023-08-13T21:42:07Z | 751 | 4 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T20:31:26Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
redstonehero/angrarealflex_v20
|
redstonehero
| 2023-08-13T21:42:05Z | 29 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T20:38:38Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
redstonehero/cyberrealistic_v33
|
redstonehero
| 2023-08-13T21:41:58Z | 30 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T20:17:37Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e8_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T21:39:52Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:39:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
SaranaAbidueva/mbart50_ru_bua
|
SaranaAbidueva
| 2023-08-13T21:38:06Z | 104 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"ru",
"bua",
"bxr",
"dataset:SaranaAbidueva/buryat-russian_parallel_corpus",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-11T10:42:25Z |
---
language:
- ru
- bua
- bxr
datasets:
- SaranaAbidueva/buryat-russian_parallel_corpus
metrics:
- bleu
---
This model translates from Russian to Buryat language.
How to use in Python:
```python
from transformers import MBartForConditionalGeneration, MBart50Tokenizer
model = MBartForConditionalGeneration.from_pretrained("SaranaAbidueva/mbart50_ru_bua")
tokenizer = MBart50Tokenizer.from_pretrained("SaranaAbidueva/mbart50_ru_bua")
def translate(text, max_length=200, num_beams=5, repetition_penalty=5.0, **kwargs):
encoded = tokenizer(text, return_tensors="pt")
generated_tokens = model.generate(
**encoded.to(model.device),
max_length=max_length,
num_beams=num_beams,
repetition_penalty=repetition_penalty
)
return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
translate('Евгений Онегин интересная книга')
```
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e6_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:37:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:37:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e5_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:28:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:28:01Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bweln/llama-2-7b-miniguanaco
|
bweln
| 2023-08-13T21:21:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-13T21:12:26Z |
A model from a finetuning exercise - see more; https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:21:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:21:03Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
GeneralRincewind/ShakespeareGPT
|
GeneralRincewind
| 2023-08-13T21:20:51Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-13T05:59:18Z |
https://colab.research.google.com/drive/1Dlm8FA9JjjcqJIkfCagaIQWex8Ho5IKI#scrollTo=e8xIjRNsl3Bb
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("GeneralRincewind/ShakespeareGPT")
model = AutoModelForCausalLM.from_pretrained("GeneralRincewind/ShakespeareGPT")
#### Generate text
from transformers import TextStreamer
tokenized_text = tokenizer("", return_tensors="pt", truncation=True)
input_ids = tokenized_text.input_ids
streamer = TextStreamer(tokenizer)
model.eval()
full_completion = model.generate(inputs=tokenized_text["input_ids"].to("cuda"),
attention_mask=tokenized_text["attention_mask"].to("cuda"),
temperature=0.9,
top_k=80,
top_p=0.65,
do_sample=True,
streamer=streamer,
num_beams=1,
max_new_tokens=500,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
repetition_penalty=1)
decoded_text = tokenizer.decode(full_completion[0])
print(decoded_text)
```
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e4_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:19:56Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:19:55Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e5_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T21:16:27Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:16:25Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e3_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:11:18Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T18:29:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
RazzzHF/kendrick
|
RazzzHF
| 2023-08-13T21:10:56Z | 0 | 0 | null |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-08-13T21:10:02Z |
---
license: cc-by-nc-nd-4.0
---
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e9_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:07:52Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:07:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e2_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:07:34Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:07:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e2_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:02:41Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T18:20:44Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e8_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:00:57Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:00:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e3_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T21:00:49Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:00:48Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T21:00:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T21:00:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e7_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:54:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:53:57Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0058
|
bigmorning
| 2023-08-13T20:53:24Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:53:17Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0058
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. -->
# whisper_charsplit_new_round2__0058
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0013
- Train Accuracy: 0.0795
- Train Wermet: 7.9766
- Validation Loss: 0.5741
- Validation Accuracy: 0.0768
- Validation Wermet: 6.8820
- Epoch: 57
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
| 0.0008 | 0.0795 | 8.0277 | 0.5581 | 0.0769 | 6.9081 | 47 |
| 0.0005 | 0.0795 | 7.6084 | 0.5604 | 0.0769 | 6.7158 | 48 |
| 0.0006 | 0.0795 | 8.0561 | 0.5729 | 0.0767 | 7.4189 | 49 |
| 0.0014 | 0.0795 | 8.2875 | 0.5658 | 0.0768 | 7.5768 | 50 |
| 0.0011 | 0.0795 | 8.4376 | 0.5665 | 0.0768 | 7.2469 | 51 |
| 0.0018 | 0.0795 | 8.3093 | 0.5771 | 0.0768 | 7.2637 | 52 |
| 0.0021 | 0.0795 | 7.8370 | 0.5680 | 0.0768 | 7.0030 | 53 |
| 0.0014 | 0.0795 | 7.7408 | 0.5661 | 0.0769 | 7.1664 | 54 |
| 0.0009 | 0.0795 | 7.7601 | 0.5639 | 0.0769 | 6.9567 | 55 |
| 0.0006 | 0.0795 | 7.8589 | 0.5667 | 0.0769 | 7.3058 | 56 |
| 0.0013 | 0.0795 | 7.9766 | 0.5741 | 0.0768 | 6.8820 | 57 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e2_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T20:53:01Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:19:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e6_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:47:03Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:47:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_5_e0_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:45:28Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T18:03:26Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e9_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T20:38:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:38:21Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e0_s55555_v4_l5_v50
|
KingKazma
| 2023-08-13T20:37:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:04:51Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0054
|
bigmorning
| 2023-08-13T20:35:53Z | 58 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:35:47Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0054
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. -->
# whisper_charsplit_new_round2__0054
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0021
- Train Accuracy: 0.0795
- Train Wermet: 7.8370
- Validation Loss: 0.5680
- Validation Accuracy: 0.0768
- Validation Wermet: 7.0030
- Epoch: 53
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
| 0.0008 | 0.0795 | 8.0277 | 0.5581 | 0.0769 | 6.9081 | 47 |
| 0.0005 | 0.0795 | 7.6084 | 0.5604 | 0.0769 | 6.7158 | 48 |
| 0.0006 | 0.0795 | 8.0561 | 0.5729 | 0.0767 | 7.4189 | 49 |
| 0.0014 | 0.0795 | 8.2875 | 0.5658 | 0.0768 | 7.5768 | 50 |
| 0.0011 | 0.0795 | 8.4376 | 0.5665 | 0.0768 | 7.2469 | 51 |
| 0.0018 | 0.0795 | 8.3093 | 0.5771 | 0.0768 | 7.2637 | 52 |
| 0.0021 | 0.0795 | 7.8370 | 0.5680 | 0.0768 | 7.0030 | 53 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_8_e-1_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:27:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:27:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0052
|
bigmorning
| 2023-08-13T20:27:13Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:27:05Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0052
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. -->
# whisper_charsplit_new_round2__0052
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0011
- Train Accuracy: 0.0795
- Train Wermet: 8.4376
- Validation Loss: 0.5665
- Validation Accuracy: 0.0768
- Validation Wermet: 7.2469
- Epoch: 51
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
| 0.0008 | 0.0795 | 8.0277 | 0.5581 | 0.0769 | 6.9081 | 47 |
| 0.0005 | 0.0795 | 7.6084 | 0.5604 | 0.0769 | 6.7158 | 48 |
| 0.0006 | 0.0795 | 8.0561 | 0.5729 | 0.0767 | 7.4189 | 49 |
| 0.0014 | 0.0795 | 8.2875 | 0.5658 | 0.0768 | 7.5768 | 50 |
| 0.0011 | 0.0795 | 8.4376 | 0.5665 | 0.0768 | 7.2469 | 51 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e2_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:19:21Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:19:20Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0050
|
bigmorning
| 2023-08-13T20:18:38Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:18:18Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0050
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. -->
# whisper_charsplit_new_round2__0050
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0006
- Train Accuracy: 0.0795
- Train Wermet: 8.0561
- Validation Loss: 0.5729
- Validation Accuracy: 0.0767
- Validation Wermet: 7.4189
- Epoch: 49
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
| 0.0008 | 0.0795 | 8.0277 | 0.5581 | 0.0769 | 6.9081 | 47 |
| 0.0005 | 0.0795 | 7.6084 | 0.5604 | 0.0769 | 6.7158 | 48 |
| 0.0006 | 0.0795 | 8.0561 | 0.5729 | 0.0767 | 7.4189 | 49 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e6_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T20:18:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:22:24Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_8_e9_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T20:13:10Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:13:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0048
|
bigmorning
| 2023-08-13T20:09:40Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:09:32Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0048
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. -->
# whisper_charsplit_new_round2__0048
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0008
- Train Accuracy: 0.0795
- Train Wermet: 8.0277
- Validation Loss: 0.5581
- Validation Accuracy: 0.0769
- Validation Wermet: 6.9081
- Epoch: 47
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
| 0.0008 | 0.0795 | 8.0277 | 0.5581 | 0.0769 | 6.9081 | 47 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e0_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T20:05:30Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T20:05:29Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0047
|
bigmorning
| 2023-08-13T20:05:14Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T20:05:07Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0047
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. -->
# whisper_charsplit_new_round2__0047
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0006
- Train Accuracy: 0.0795
- Train Wermet: 8.0596
- Validation Loss: 0.5573
- Validation Accuracy: 0.0768
- Validation Wermet: 6.9489
- Epoch: 46
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
| 0.0001 | 0.0795 | 7.9155 | 0.5752 | 0.0769 | 6.4900 | 43 |
| 0.0095 | 0.0793 | 8.3244 | 0.5662 | 0.0767 | 6.9524 | 44 |
| 0.0019 | 0.0795 | 7.8491 | 0.5533 | 0.0769 | 6.9541 | 45 |
| 0.0006 | 0.0795 | 8.0596 | 0.5573 | 0.0768 | 6.9489 | 46 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
redstonehero/lofi_v3
|
redstonehero
| 2023-08-13T20:05:07Z | 32 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T18:40:18Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
redstonehero/m4rv3lsdungeonsv40
|
redstonehero
| 2023-08-13T20:05:01Z | 5 | 0 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-13T18:43:05Z |
---
license: creativeml-openrail-m
library_name: diffusers
---
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e4_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T20:04:40Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:08:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
JapGuy/MichalHruza_V1_1000Epochs_RVC_v2
|
JapGuy
| 2023-08-13T20:04:01Z | 0 | 0 | null |
[
"music",
"rvc",
"michal",
"hruza",
"model",
"audio-to-audio",
"cs",
"license:openrail",
"region:us"
] |
audio-to-audio
| 2023-08-13T19:57:52Z |
---
license: openrail
language:
- cs
pipeline_tag: audio-to-audio
tags:
- music
- rvc
- michal
- hruza
- model
---
# Michal Hrůza [CZ] (v1)
# 1000 Epochs - RVC V2 - mangio-creep - 64 Hop Length
Trained on 14 minutes of isolated acapellas using UVR (Voc FT + Reverb HQ) + Audacity to remove parts with double vocals and vocals from others (+Noise Gate)
|
Ridhto/TomatsuHaruka
|
Ridhto
| 2023-08-13T20:03:25Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-04T07:26:17Z |
---
license: creativeml-openrail-m
---
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e-1_s55555_v4_l4_v100
|
KingKazma
| 2023-08-13T19:58:35Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:58:34Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e3_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:57:57Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:01:34Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
sherif1311/flan-t5-base-intent
|
sherif1311
| 2023-08-13T19:57:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-08-13T17:45:07Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: flan-t5-base-intent
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. -->
# flan-t5-base-intent
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- F1: 100.0
- Gen Len: 2.3333
## Model description
Use double quotation for any tweet. 0: Anti-tobacco 1: Neutral 2: Pro-tobacco
## Intended uses & limitations
The fine tuned model by STOP is intended for Anti-tobacco/ Pro-tobacco monitoring for social media.
## Training and evaluation data
The model was developed and fine tuned in STOP, University of Bath, UK
Data used is sherif1311/intend which was collected, augmented and trained by STOP.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 5
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 1.12.1+cu116
- Datasets 2.14.4
- Tokenizers 0.12.1
|
KingKazma/cnn_dailymail_gpt2_prompt_tuning_500_10_3000_8_e7_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:55:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:55:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
s3nh/flozi00-Llama-2-13B-german-assistant-v3-GGML
|
s3nh
| 2023-08-13T19:51:57Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-13T19:51:56Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGML Format model files for [This project](https://huggingface.co/Photolens/OpenOrcaxOpenChat-2-13b-langchain-chat).
### inference
```python
import ctransformers
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")
manual_input: str = "Tell me about your last dream, please."
llm(manual_input,
max_new_tokens=256,
temperature=0.9,
top_p= 0.7)
```
# Original model card
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e2_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:51:14Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T18:54:36Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e9_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:47:51Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:47:50Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
KingKazma/xsum_gpt2_prompt_tuning_500_10_3000_8_e9_s108_v4_l5_v50
|
KingKazma
| 2023-08-13T19:47:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:47:45Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0043
|
bigmorning
| 2023-08-13T19:47:37Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T19:47:30Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0043
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. -->
# whisper_charsplit_new_round2__0043
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 8.2925
- Validation Loss: 0.5648
- Validation Accuracy: 0.0770
- Validation Wermet: 7.1917
- Epoch: 42
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
| 0.0001 | 0.0795 | 8.2925 | 0.5648 | 0.0770 | 7.1917 | 42 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/xsum_gpt2_prefix_tuning_500_10_3000_8_e1_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:44:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T18:47:40Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0042
|
bigmorning
| 2023-08-13T19:43:07Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T19:43:01Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0042
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. -->
# whisper_charsplit_new_round2__0042
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 8.2484
- Validation Loss: 0.5678
- Validation Accuracy: 0.0769
- Validation Wermet: 7.6993
- Epoch: 41
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
| 0.0001 | 0.0795 | 8.2484 | 0.5678 | 0.0769 | 7.6993 | 41 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
KingKazma/cnn_dailymail_gpt2_prefix_tuning_500_10_3000_8_e8_s108_v4_l4_v100
|
KingKazma
| 2023-08-13T19:40:55Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-13T19:40:54Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round2__0041
|
bigmorning
| 2023-08-13T19:38:44Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-13T19:38:36Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round2__0041
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. -->
# whisper_charsplit_new_round2__0041
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 8.1912
- Validation Loss: 0.5632
- Validation Accuracy: 0.0770
- Validation Wermet: 7.1929
- Epoch: 40
## 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': 1e-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 | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0010 | 0.0795 | 8.7507 | 0.5575 | 0.0767 | 7.6778 | 0 |
| 0.0013 | 0.0795 | 8.9468 | 0.5652 | 0.0766 | 8.3360 | 1 |
| 0.0025 | 0.0795 | 8.7338 | 0.5673 | 0.0765 | 8.3770 | 2 |
| 0.0019 | 0.0795 | 8.9450 | 0.5623 | 0.0766 | 7.7117 | 3 |
| 0.0011 | 0.0795 | 8.9053 | 0.5609 | 0.0767 | 7.5155 | 4 |
| 0.0012 | 0.0795 | 8.8862 | 0.5667 | 0.0767 | 8.2913 | 5 |
| 0.0009 | 0.0795 | 8.7510 | 0.5642 | 0.0766 | 7.9083 | 6 |
| 0.0037 | 0.0795 | 9.3428 | 0.5717 | 0.0764 | 8.2631 | 7 |
| 0.0031 | 0.0795 | 9.2135 | 0.5636 | 0.0766 | 8.2384 | 8 |
| 0.0011 | 0.0795 | 8.9730 | 0.5605 | 0.0767 | 8.3958 | 9 |
| 0.0005 | 0.0795 | 9.3749 | 0.5552 | 0.0768 | 8.0800 | 10 |
| 0.0003 | 0.0795 | 9.3340 | 0.5584 | 0.0768 | 8.1322 | 11 |
| 0.0005 | 0.0795 | 9.2292 | 0.5687 | 0.0767 | 8.5576 | 12 |
| 0.0037 | 0.0795 | 9.2838 | 0.5751 | 0.0765 | 7.4189 | 13 |
| 0.0038 | 0.0795 | 8.7270 | 0.5605 | 0.0767 | 7.7098 | 14 |
| 0.0012 | 0.0795 | 8.8259 | 0.5563 | 0.0768 | 8.2647 | 15 |
| 0.0005 | 0.0795 | 9.0553 | 0.5620 | 0.0768 | 8.5020 | 16 |
| 0.0004 | 0.0795 | 9.1734 | 0.5607 | 0.0768 | 8.0252 | 17 |
| 0.0003 | 0.0795 | 9.0084 | 0.5571 | 0.0769 | 8.1563 | 18 |
| 0.0014 | 0.0795 | 8.7153 | 0.5804 | 0.0765 | 7.8654 | 19 |
| 0.0058 | 0.0794 | 8.8460 | 0.5706 | 0.0766 | 7.4342 | 20 |
| 0.0020 | 0.0795 | 8.6599 | 0.5612 | 0.0767 | 7.7369 | 21 |
| 0.0007 | 0.0795 | 8.6456 | 0.5543 | 0.0768 | 7.4625 | 22 |
| 0.0008 | 0.0795 | 8.3246 | 0.5620 | 0.0768 | 7.4475 | 23 |
| 0.0012 | 0.0795 | 7.9451 | 0.5615 | 0.0768 | 7.0907 | 24 |
| 0.0025 | 0.0795 | 8.1065 | 0.5619 | 0.0768 | 7.7020 | 25 |
| 0.0011 | 0.0795 | 8.4237 | 0.5710 | 0.0768 | 7.4035 | 26 |
| 0.0009 | 0.0795 | 8.3074 | 0.5641 | 0.0768 | 7.1747 | 27 |
| 0.0007 | 0.0795 | 8.5183 | 0.5688 | 0.0768 | 7.4310 | 28 |
| 0.0014 | 0.0795 | 8.6604 | 0.5750 | 0.0767 | 8.0751 | 29 |
| 0.0022 | 0.0795 | 8.2353 | 0.5789 | 0.0767 | 7.4442 | 30 |
| 0.0019 | 0.0795 | 8.6037 | 0.5715 | 0.0767 | 7.6157 | 31 |
| 0.0009 | 0.0795 | 8.4768 | 0.5611 | 0.0769 | 7.6392 | 32 |
| 0.0005 | 0.0795 | 8.2728 | 0.5669 | 0.0768 | 7.1451 | 33 |
| 0.0010 | 0.0795 | 8.1006 | 0.5918 | 0.0766 | 7.4447 | 34 |
| 0.0036 | 0.0795 | 8.9171 | 0.5687 | 0.0767 | 7.6962 | 35 |
| 0.0018 | 0.0795 | 8.4062 | 0.5713 | 0.0768 | 7.2127 | 36 |
| 0.0012 | 0.0795 | 8.3370 | 0.5683 | 0.0768 | 7.1040 | 37 |
| 0.0005 | 0.0795 | 7.9931 | 0.5658 | 0.0769 | 6.8043 | 38 |
| 0.0002 | 0.0795 | 7.9500 | 0.5660 | 0.0769 | 7.0891 | 39 |
| 0.0001 | 0.0795 | 8.1912 | 0.5632 | 0.0770 | 7.1929 | 40 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
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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.