modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-28 18:27:53
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 525
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
Gman/pretrained-bert
|
Gman
| 2023-08-14T10:35:06Z | 47 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2023-08-14T10:34:07Z |
---
base_model: ''
tags:
- generated_from_keras_callback
model-index:
- name: pretrained-bert
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. -->
# pretrained-bert
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.4779
- Validation Loss: 8.6183
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.4779 | 8.6183 | 0 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round4__0005
|
bigmorning
| 2023-08-14T10:33:57Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T10:33:48Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round4__0005
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_round4__0005
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0009
- Train Accuracy: 0.0795
- Train Wermet: 8.4533
- Validation Loss: 0.5771
- Validation Accuracy: 0.0771
- Validation Wermet: 7.4112
- Epoch: 4
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9702 | 0.5713 | 0.0770 | 6.9300 | 0 |
| 0.0011 | 0.0795 | 7.7485 | 0.5743 | 0.0771 | 6.6465 | 1 |
| 0.0011 | 0.0795 | 8.1600 | 0.5748 | 0.0771 | 7.1363 | 2 |
| 0.0008 | 0.0795 | 8.1954 | 0.5845 | 0.0770 | 7.1869 | 3 |
| 0.0009 | 0.0795 | 8.4533 | 0.5771 | 0.0771 | 7.4112 | 4 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
frankjoshua/controlnet-depth-sdxl-1.0
|
frankjoshua
| 2023-08-14T10:27:05Z | 86 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"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
| 2023-10-14T01:25:51Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
inference: false
---
# SDXL-controlnet: Depth
These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. You can find some example images in the following.
prompt: spiderman lecture, photorealistic
## Usage
Make sure to first install the libraries:
```bash
pip install accelerate transformers safetensors diffusers
```
And then we're ready to go:
```python
import torch
import numpy as np
from PIL import Image
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0",
variant="fp16",
use_safetensors=True,
torch_dtype=torch.float16,
).to("cuda")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
vae=vae,
variant="fp16",
use_safetensors=True,
torch_dtype=torch.float16,
).to("cuda")
pipe.enable_model_cpu_offload()
def get_depth_map(image):
image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
with torch.no_grad(), torch.autocast("cuda"):
depth_map = depth_estimator(image).predicted_depth
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=(1024, 1024),
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
image = torch.cat([depth_map] * 3, dim=1)
image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
return image
prompt = "stormtrooper lecture, photorealistic"
image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png")
controlnet_conditioning_scale = 0.5 # recommended for good generalization
depth_image = get_depth_map(image)
images = pipe(
prompt, image=depth_image, num_inference_steps=30, controlnet_conditioning_scale=controlnet_conditioning_scale,
).images
images[0]
images[0].save(f"stormtrooper.png")
```
To more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl).
### Training
Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md).
#### Training data and Compute
The model is trained on 3M image-text pairs from LAION-Aesthetics V2. The model is trained for 700 GPU hours on 80GB A100 GPUs.
#### Batch size
Data parallel with a single gpu batch size of 8 for a total batch size of 256.
#### Hyper Parameters
Constant learning rate of 1e-5.
#### Mixed precision
fp16
|
amirhamza11/my_awesome_eli5_mlm_model_2
|
amirhamza11
| 2023-08-14T10:17:27Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-14T09:59:42Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_mlm_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model_2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9883
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2469 | 1.0 | 1138 | 2.0423 |
| 2.1601 | 2.0 | 2276 | 2.0028 |
| 2.1295 | 3.0 | 3414 | 2.0125 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
|
vj1148/lora-peft-flant5-large-v2
|
vj1148
| 2023-08-14T10:03:02Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T10:03:01Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
AdirK/CartPole-v1
|
AdirK
| 2023-08-14T09:45:20Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T09:45:09Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
M2LInES/gz21-ocean-momentum
|
M2LInES
| 2023-08-14T09:40:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-14T09:10:22Z |
# gz21-ocean-momentum pretrained models
[gfdl-cm2.6-pangeo]: https://catalog.pangeo.io/browse/master/ocean/GFDL_CM2_6/
[gz21-gh]: https://github.com/m2lines/gz21_ocean_momentum
[gz21-data-hf]: https://huggingface.co/datasets/M2LInES/gfdl-cmip26-gz21-ocean-forcing
[gz21-ocean-momentum][gz21-gh] models trained on the forcing data generated from
the [GFDL CM2.6][gfdl-cm2.6-pangeo] dataset. (Some forcing is hosted on Hugging
Face at [datasets/M2LInES/gfdl-cmip26-gz21-ocean-forcing][gz21-data-hf].
See individual model directories for details (hyperparameters, code used).
|
anikesh-mane/prompt-tuned-flan-t5-large
|
anikesh-mane
| 2023-08-14T09:38:40Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T09:38:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Xilabs/instructmix-llama-3b
|
Xilabs
| 2023-08-14T09:35:38Z | 8 | 1 |
peft
|
[
"peft",
"text-generation",
"dataset:Xilabs/instructmix",
"region:us"
] |
text-generation
| 2023-07-24T03:00:40Z |
---
library_name: peft
datasets:
- Xilabs/instructmix
pipeline_tag: text-generation
---
## Model Card for "InstructMix Llama 3B"
**Model Name:** InstructMix Llama 3B
**Description:**
InstructMix Llama 3B is a language model fine-tuned on the InstructMix dataset using parameter-efficient fine-tuning (PEFT), using the base model "openlm-research/open_llama_3b_v2," which can be found at [https://huggingface.co/openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2).
An easy way to use InstructMix Llama 3B is via the API: https://replicate.com/ritabratamaiti/instructmix-llama-3b
**Usage:**
```py
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
from peft import PeftModel, PeftConfig
# Hugging Face model_path
model_path = 'openlm-research/open_llama_3b_v2'
peft_model_id = 'Xilabs/instructmix-llama-3b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, device_map="auto"
)
model = PeftModel.from_pretrained(model, peft_model_id)
def generate_prompt(instruction, input=None):
if input:
return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""
def evaluate(
instruction,
input=None,
temperature=0.5,
top_p=0.75,
top_k=40,
num_beams=5,
max_new_tokens=128,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to("cuda")
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
early_stopping=True,
repetition_penalty=1.1,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens = True)
#print(output)
return output.split("### Response:")[1]
instruction = "What is the meaning of life?"
print(evaluate(instruction, num_beams=3, temperature=0.1, max_new_tokens=256))
```
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQ
|
vnktrmnb
| 2023-08-14T09:29:24Z | 71 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-12T07:38:12Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQ
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. -->
# vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQ
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3207
- Train End Logits Accuracy: 0.8945
- Train Start Logits Accuracy: 0.9240
- Validation Loss: 0.4883
- Validation End Logits Accuracy: 0.8621
- Validation Start Logits Accuracy: 0.9124
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2439, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2099 | 0.6849 | 0.7242 | 0.5171 | 0.8454 | 0.8930 | 0 |
| 0.5374 | 0.8328 | 0.8737 | 0.4915 | 0.8570 | 0.8943 | 1 |
| 0.3207 | 0.8945 | 0.9240 | 0.4883 | 0.8621 | 0.9124 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
gagneurlab/SpeciesLM
|
gagneurlab
| 2023-08-14T09:27:08Z | 1,351 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2023-08-14T08:41:30Z |
---
license: mit
---
Load each model using:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("gagneurlab/SpeciesLM", revision = "<<choose model type>>")
model = AutoModelForMaskedLM.from_pretrained("gagneurlab/SpeciesLM", revision = "<<choose model type>>")
```
Model type:
- Species LM, 3' region: `downstream_species_lm`
- Agnostic LM, 3' region: `downstream_agnostic_lm`
- Species LM, 5' region: `upstream_species_lm`
- Agnostic LM, 5' region: `upstream_agnostic_lm`
|
CyberHarem/temari_naruto
|
CyberHarem
| 2023-08-14T09:18:34Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/temari_naruto",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-14T09:12:33Z |
---
license: mit
datasets:
- CyberHarem/temari_naruto
pipeline_tag: text-to-image
tags:
- art
---
# Lora of temari_naruto
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/temari_naruto.pt` as the embedding and `1500/temari_naruto.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `temari_naruto`.**
These are available steps:
| Steps | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:-----------------------------------|
| 1500 |  |  |  | [<NSFW, click to see>](1500/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/temari_naruto.zip) |
| 1400 |  |  |  | [<NSFW, click to see>](1400/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/temari_naruto.zip) |
| 1300 |  |  |  | [<NSFW, click to see>](1300/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/temari_naruto.zip) |
| 1200 |  |  |  | [<NSFW, click to see>](1200/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/temari_naruto.zip) |
| 1100 |  |  |  | [<NSFW, click to see>](1100/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/temari_naruto.zip) |
| 1000 |  |  |  | [<NSFW, click to see>](1000/previews/pattern_4.png) |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/temari_naruto.zip) |
| 900 |  |  |  | [<NSFW, click to see>](900/previews/pattern_4.png) |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/temari_naruto.zip) |
| 800 |  |  |  | [<NSFW, click to see>](800/previews/pattern_4.png) |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/temari_naruto.zip) |
| 700 |  |  |  | [<NSFW, click to see>](700/previews/pattern_4.png) |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/temari_naruto.zip) |
| 600 |  |  |  | [<NSFW, click to see>](600/previews/pattern_4.png) |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/temari_naruto.zip) |
| 500 |  |  |  | [<NSFW, click to see>](500/previews/pattern_4.png) |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/temari_naruto.zip) |
| 400 |  |  |  | [<NSFW, click to see>](400/previews/pattern_4.png) |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/temari_naruto.zip) |
| 300 |  |  |  | [<NSFW, click to see>](300/previews/pattern_4.png) |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/temari_naruto.zip) |
| 200 |  |  |  | [<NSFW, click to see>](200/previews/pattern_4.png) |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/temari_naruto.zip) |
| 100 |  |  |  | [<NSFW, click to see>](100/previews/pattern_4.png) |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/temari_naruto.zip) |
|
rokset3/kazroberta-180kstep
|
rokset3
| 2023-08-14T09:18:28Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T09:16:45Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
jondurbin/airoboros-13b
|
jondurbin
| 2023-08-14T09:07:30Z | 1,446 | 106 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-19T11:56:55Z |
---
license: cc-by-nc-4.0
---
# Overview
This is a fine-tuned 13b parameter LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros
__*I don't recommend using this model! The outputs aren't particularly great, and it may contain "harmful" data due to jailbreak*__
Please see one of the updated airoboros models for a much better experience.
### Eval (gpt4 judging)
| model | raw score | gpt-3.5 adjusted score |
| --- | --- | --- |
| __airoboros-13b__ | __17947__ | __98.087__ |
| gpt35 | 18297 | 100.0 |
| gpt4-x-alpasta-30b | 15612 | 85.33 |
| manticore-13b | 15856 | 86.66 |
| vicuna-13b-1.1 | 16306 | 89.12 |
| wizard-vicuna-13b-uncensored | 16287 | 89.01 |
<details>
<summary>individual question scores, with shareGPT links (200 prompts generated by gpt-4)</summary>
*wb-13b-u is Wizard-Vicuna-13b-Uncensored*
| airoboros-13b | gpt35 | gpt4-x-alpasta-30b | manticore-13b | vicuna-13b-1.1 | wv-13b-u | link |
|----------------:|--------:|---------------------:|----------------:|-----------------:|-------------------------------:|:---------------------------------------|
| 80 | 95 | 70 | 90 | 85 | 60 | [eval](https://sharegpt.com/c/PIbRQD3) |
| 20 | 95 | 40 | 30 | 90 | 80 | [eval](https://sharegpt.com/c/fSzwzzd) |
| 100 | 100 | 100 | 95 | 95 | 100 | [eval](https://sharegpt.com/c/AXMzZiO) |
| 90 | 100 | 85 | 60 | 95 | 100 | [eval](https://sharegpt.com/c/7obzJm2) |
| 95 | 90 | 80 | 85 | 95 | 75 | [eval](https://sharegpt.com/c/cRpj6M1) |
| 100 | 95 | 90 | 95 | 98 | 92 | [eval](https://sharegpt.com/c/p0by1T7) |
| 50 | 100 | 80 | 95 | 60 | 55 | [eval](https://sharegpt.com/c/rowNlKx) |
| 70 | 90 | 80 | 60 | 85 | 40 | [eval](https://sharegpt.com/c/I4POj4I) |
| 100 | 95 | 50 | 85 | 40 | 60 | [eval](https://sharegpt.com/c/gUAeiRp) |
| 85 | 60 | 55 | 65 | 50 | 70 | [eval](https://sharegpt.com/c/Lgw4QQL) |
| 95 | 100 | 85 | 90 | 60 | 75 | [eval](https://sharegpt.com/c/X9tDYft) |
| 100 | 95 | 70 | 80 | 50 | 85 | [eval](https://sharegpt.com/c/9V2ElkH) |
| 100 | 95 | 80 | 70 | 60 | 90 | [eval](https://sharegpt.com/c/D5xg6qt) |
| 95 | 100 | 70 | 85 | 90 | 90 | [eval](https://sharegpt.com/c/lQnSfDs) |
| 80 | 95 | 90 | 60 | 30 | 85 | [eval](https://sharegpt.com/c/1hpHGNc) |
| 60 | 95 | 0 | 75 | 50 | 40 | [eval](https://sharegpt.com/c/an6TqE4) |
| 100 | 95 | 90 | 98 | 95 | 95 | [eval](https://sharegpt.com/c/7vr6n3F) |
| 60 | 85 | 40 | 50 | 20 | 0 | [eval](https://sharegpt.com/c/TOkMkgE) |
| 100 | 90 | 85 | 95 | 95 | 80 | [eval](https://sharegpt.com/c/Qu7ak0r) |
| 100 | 95 | 100 | 95 | 90 | 95 | [eval](https://sharegpt.com/c/hMD4gPo) |
| 95 | 90 | 96 | 80 | 92 | 88 | [eval](https://sharegpt.com/c/HTlicNh) |
| 95 | 92 | 90 | 93 | 89 | 91 | [eval](https://sharegpt.com/c/MjxHpAf) |
| 95 | 93 | 90 | 94 | 96 | 92 | [eval](https://sharegpt.com/c/4RvxOR9) |
| 95 | 90 | 93 | 88 | 92 | 85 | [eval](https://sharegpt.com/c/PcAIU9r) |
| 95 | 90 | 85 | 96 | 88 | 92 | [eval](https://sharegpt.com/c/MMqul3q) |
| 95 | 95 | 90 | 93 | 92 | 91 | [eval](https://sharegpt.com/c/YQsLyzJ) |
| 95 | 98 | 80 | 97 | 99 | 96 | [eval](https://sharegpt.com/c/UDhSTMq) |
| 95 | 93 | 90 | 87 | 92 | 89 | [eval](https://sharegpt.com/c/4gCfdCV) |
| 90 | 85 | 95 | 80 | 92 | 75 | [eval](https://sharegpt.com/c/bkQs4SP) |
| 90 | 85 | 95 | 93 | 80 | 92 | [eval](https://sharegpt.com/c/LeLCEEt) |
| 95 | 92 | 90 | 91 | 93 | 89 | [eval](https://sharegpt.com/c/DFxNzVu) |
| 100 | 95 | 90 | 85 | 80 | 95 | [eval](https://sharegpt.com/c/gnVzNML) |
| 95 | 97 | 93 | 92 | 96 | 94 | [eval](https://sharegpt.com/c/y7pxMIy) |
| 95 | 93 | 94 | 90 | 88 | 92 | [eval](https://sharegpt.com/c/5UeCvTY) |
| 90 | 95 | 98 | 85 | 96 | 92 | [eval](https://sharegpt.com/c/T4oL9I5) |
| 90 | 88 | 85 | 80 | 82 | 84 | [eval](https://sharegpt.com/c/HnGyTAG) |
| 90 | 95 | 85 | 87 | 92 | 88 | [eval](https://sharegpt.com/c/ZbRMBNj) |
| 95 | 97 | 96 | 90 | 93 | 92 | [eval](https://sharegpt.com/c/iTmFJqd) |
| 95 | 93 | 92 | 90 | 89 | 91 | [eval](https://sharegpt.com/c/VuPifET) |
| 90 | 95 | 93 | 92 | 94 | 91 | [eval](https://sharegpt.com/c/AvFAH1x) |
| 90 | 85 | 95 | 80 | 88 | 75 | [eval](https://sharegpt.com/c/4ealKGN) |
| 85 | 90 | 95 | 88 | 92 | 80 | [eval](https://sharegpt.com/c/bE1b2vX) |
| 90 | 95 | 92 | 85 | 80 | 87 | [eval](https://sharegpt.com/c/I3nMPBC) |
| 85 | 90 | 95 | 80 | 88 | 75 | [eval](https://sharegpt.com/c/as7r3bW) |
| 85 | 80 | 75 | 90 | 70 | 82 | [eval](https://sharegpt.com/c/qYceaUa) |
| 90 | 85 | 95 | 92 | 93 | 80 | [eval](https://sharegpt.com/c/g4FXchU) |
| 90 | 95 | 75 | 85 | 80 | 70 | [eval](https://sharegpt.com/c/6kGLvL5) |
| 85 | 90 | 80 | 88 | 82 | 83 | [eval](https://sharegpt.com/c/SRozqaF) |
| 85 | 90 | 95 | 92 | 88 | 80 | [eval](https://sharegpt.com/c/GoKydf6) |
| 85 | 90 | 80 | 75 | 95 | 88 | [eval](https://sharegpt.com/c/37aXkHQ) |
| 85 | 90 | 80 | 88 | 84 | 92 | [eval](https://sharegpt.com/c/nVuUaTj) |
| 80 | 90 | 75 | 85 | 70 | 95 | [eval](https://sharegpt.com/c/TkAQKLC) |
| 90 | 88 | 85 | 80 | 92 | 83 | [eval](https://sharegpt.com/c/55cO2y0) |
| 85 | 75 | 90 | 80 | 78 | 88 | [eval](https://sharegpt.com/c/tXtq5lT) |
| 85 | 90 | 80 | 82 | 75 | 88 | [eval](https://sharegpt.com/c/TfMjeJQ) |
| 90 | 85 | 40 | 95 | 80 | 88 | [eval](https://sharegpt.com/c/2jQ6K2S) |
| 85 | 95 | 90 | 75 | 88 | 80 | [eval](https://sharegpt.com/c/aQtr2ca) |
| 85 | 95 | 90 | 92 | 89 | 88 | [eval](https://sharegpt.com/c/tbWLyZ7) |
| 80 | 85 | 75 | 60 | 90 | 70 | [eval](https://sharegpt.com/c/moHC7i2) |
| 85 | 90 | 87 | 80 | 88 | 75 | [eval](https://sharegpt.com/c/GK6GShh) |
| 85 | 80 | 75 | 50 | 90 | 80 | [eval](https://sharegpt.com/c/ugcW4qG) |
| 95 | 80 | 90 | 85 | 75 | 82 | [eval](https://sharegpt.com/c/WL8iq6F) |
| 85 | 90 | 80 | 70 | 95 | 88 | [eval](https://sharegpt.com/c/TZJKnvS) |
| 90 | 95 | 70 | 85 | 80 | 75 | [eval](https://sharegpt.com/c/beNOKb5) |
| 90 | 85 | 70 | 75 | 80 | 60 | [eval](https://sharegpt.com/c/o2oRCF5) |
| 95 | 90 | 70 | 50 | 85 | 80 | [eval](https://sharegpt.com/c/TNjbK6D) |
| 80 | 85 | 40 | 60 | 90 | 95 | [eval](https://sharegpt.com/c/rJvszWJ) |
| 75 | 60 | 80 | 55 | 70 | 85 | [eval](https://sharegpt.com/c/HJwRkro) |
| 90 | 85 | 60 | 50 | 80 | 95 | [eval](https://sharegpt.com/c/AeFoSDK) |
| 45 | 85 | 60 | 20 | 65 | 75 | [eval](https://sharegpt.com/c/KA1cgOl) |
| 85 | 90 | 30 | 60 | 80 | 70 | [eval](https://sharegpt.com/c/RTy8n0y) |
| 90 | 95 | 80 | 40 | 85 | 70 | [eval](https://sharegpt.com/c/PJMJoXh) |
| 85 | 90 | 70 | 75 | 80 | 95 | [eval](https://sharegpt.com/c/Ib3jzyC) |
| 90 | 70 | 50 | 20 | 60 | 40 | [eval](https://sharegpt.com/c/oMmqqtX) |
| 90 | 95 | 75 | 60 | 85 | 80 | [eval](https://sharegpt.com/c/qRNhNTw) |
| 85 | 80 | 60 | 70 | 65 | 75 | [eval](https://sharegpt.com/c/3MAHQIy) |
| 90 | 85 | 80 | 75 | 82 | 70 | [eval](https://sharegpt.com/c/0Emc5HS) |
| 90 | 95 | 80 | 70 | 85 | 75 | [eval](https://sharegpt.com/c/UqAxRWF) |
| 85 | 75 | 30 | 80 | 90 | 70 | [eval](https://sharegpt.com/c/eywxGAw) |
| 85 | 90 | 50 | 70 | 80 | 60 | [eval](https://sharegpt.com/c/A2KSEWP) |
| 100 | 95 | 98 | 99 | 97 | 96 | [eval](https://sharegpt.com/c/C8rebQf) |
| 95 | 90 | 92 | 93 | 91 | 89 | [eval](https://sharegpt.com/c/cd9HF4V) |
| 95 | 92 | 90 | 85 | 88 | 91 | [eval](https://sharegpt.com/c/LHkjvQJ) |
| 100 | 95 | 98 | 97 | 96 | 99 | [eval](https://sharegpt.com/c/o5PdoyZ) |
| 100 | 100 | 100 | 90 | 100 | 95 | [eval](https://sharegpt.com/c/rh8pZVg) |
| 100 | 95 | 98 | 97 | 94 | 99 | [eval](https://sharegpt.com/c/T5DYL83) |
| 95 | 90 | 92 | 93 | 94 | 91 | [eval](https://sharegpt.com/c/G5Osg3X) |
| 100 | 95 | 98 | 90 | 96 | 95 | [eval](https://sharegpt.com/c/9ZqI03V) |
| 95 | 96 | 92 | 90 | 89 | 93 | [eval](https://sharegpt.com/c/4tFfwZU) |
| 100 | 95 | 93 | 90 | 92 | 88 | [eval](https://sharegpt.com/c/mG1JqPH) |
| 100 | 100 | 98 | 97 | 99 | 100 | [eval](https://sharegpt.com/c/VDdtgCu) |
| 95 | 90 | 92 | 85 | 93 | 94 | [eval](https://sharegpt.com/c/uKtGkvg) |
| 95 | 93 | 90 | 92 | 96 | 91 | [eval](https://sharegpt.com/c/9B92N6P) |
| 95 | 96 | 92 | 90 | 93 | 91 | [eval](https://sharegpt.com/c/GeIFfOu) |
| 95 | 90 | 92 | 93 | 91 | 89 | [eval](https://sharegpt.com/c/gn3E9nN) |
| 100 | 98 | 95 | 97 | 96 | 99 | [eval](https://sharegpt.com/c/Erxa46H) |
| 90 | 95 | 85 | 88 | 92 | 87 | [eval](https://sharegpt.com/c/oRHVOvK) |
| 95 | 93 | 90 | 92 | 89 | 88 | [eval](https://sharegpt.com/c/ghtKLUX) |
| 100 | 95 | 97 | 90 | 96 | 94 | [eval](https://sharegpt.com/c/ZL4KjqP) |
| 95 | 93 | 90 | 92 | 94 | 91 | [eval](https://sharegpt.com/c/YOnqIQa) |
| 95 | 92 | 90 | 93 | 94 | 88 | [eval](https://sharegpt.com/c/3BKwKho) |
| 95 | 92 | 60 | 97 | 90 | 96 | [eval](https://sharegpt.com/c/U1i31bn) |
| 95 | 90 | 92 | 93 | 91 | 89 | [eval](https://sharegpt.com/c/etfRoAE) |
| 95 | 90 | 97 | 92 | 91 | 93 | [eval](https://sharegpt.com/c/B0OpVxR) |
| 90 | 95 | 93 | 85 | 92 | 91 | [eval](https://sharegpt.com/c/MBgGJ5A) |
| 95 | 90 | 40 | 92 | 93 | 85 | [eval](https://sharegpt.com/c/eQKTYO7) |
| 100 | 100 | 95 | 90 | 95 | 90 | [eval](https://sharegpt.com/c/szKWCBt) |
| 90 | 95 | 96 | 98 | 93 | 92 | [eval](https://sharegpt.com/c/8ZhUcAv) |
| 90 | 95 | 92 | 89 | 93 | 94 | [eval](https://sharegpt.com/c/VQWdy99) |
| 100 | 95 | 100 | 98 | 96 | 99 | [eval](https://sharegpt.com/c/g1DHUSM) |
| 100 | 100 | 95 | 90 | 100 | 90 | [eval](https://sharegpt.com/c/uYgfJC3) |
| 90 | 85 | 88 | 92 | 87 | 91 | [eval](https://sharegpt.com/c/crk8BH3) |
| 95 | 97 | 90 | 92 | 93 | 94 | [eval](https://sharegpt.com/c/95F9afQ) |
| 90 | 95 | 85 | 88 | 92 | 89 | [eval](https://sharegpt.com/c/otioHUo) |
| 95 | 93 | 90 | 92 | 94 | 91 | [eval](https://sharegpt.com/c/KSiL9F6) |
| 90 | 95 | 85 | 80 | 88 | 82 | [eval](https://sharegpt.com/c/GmGq3b3) |
| 95 | 90 | 60 | 85 | 93 | 70 | [eval](https://sharegpt.com/c/VOhklyz) |
| 95 | 92 | 94 | 93 | 96 | 90 | [eval](https://sharegpt.com/c/wqy8m6k) |
| 95 | 90 | 85 | 93 | 87 | 92 | [eval](https://sharegpt.com/c/iWKrIuS) |
| 95 | 96 | 93 | 90 | 97 | 92 | [eval](https://sharegpt.com/c/o1h3w8N) |
| 100 | 0 | 0 | 100 | 0 | 0 | [eval](https://sharegpt.com/c/3UH9eed) |
| 60 | 100 | 0 | 80 | 0 | 0 | [eval](https://sharegpt.com/c/44g0FAh) |
| 0 | 100 | 60 | 0 | 0 | 90 | [eval](https://sharegpt.com/c/PaQlcrU) |
| 100 | 100 | 0 | 100 | 100 | 100 | [eval](https://sharegpt.com/c/51icV4o) |
| 100 | 100 | 100 | 100 | 95 | 100 | [eval](https://sharegpt.com/c/1VnbGAR) |
| 100 | 100 | 100 | 50 | 90 | 100 | [eval](https://sharegpt.com/c/EYGBrgw) |
| 100 | 100 | 100 | 100 | 95 | 90 | [eval](https://sharegpt.com/c/EGRduOt) |
| 100 | 100 | 100 | 95 | 0 | 100 | [eval](https://sharegpt.com/c/O3JJfnK) |
| 50 | 95 | 20 | 10 | 30 | 85 | [eval](https://sharegpt.com/c/2roVtAu) |
| 100 | 100 | 60 | 20 | 30 | 40 | [eval](https://sharegpt.com/c/sphFpfx) |
| 100 | 0 | 0 | 0 | 0 | 100 | [eval](https://sharegpt.com/c/OeWGKBo) |
| 0 | 100 | 60 | 0 | 0 | 80 | [eval](https://sharegpt.com/c/TOUsuFA) |
| 50 | 100 | 20 | 90 | 0 | 10 | [eval](https://sharegpt.com/c/Y3P6DCu) |
| 100 | 100 | 100 | 100 | 100 | 100 | [eval](https://sharegpt.com/c/hkbdeiM) |
| 100 | 100 | 100 | 100 | 100 | 100 | [eval](https://sharegpt.com/c/eubbaVC) |
| 40 | 100 | 95 | 0 | 100 | 40 | [eval](https://sharegpt.com/c/QWiF49v) |
| 100 | 100 | 100 | 100 | 80 | 100 | [eval](https://sharegpt.com/c/dKTapBu) |
| 100 | 100 | 100 | 0 | 90 | 40 | [eval](https://sharegpt.com/c/P8NGwFZ) |
| 0 | 100 | 100 | 50 | 70 | 20 | [eval](https://sharegpt.com/c/v96BtBL) |
| 100 | 100 | 50 | 90 | 0 | 95 | [eval](https://sharegpt.com/c/YRlzj1t) |
| 100 | 95 | 90 | 85 | 98 | 80 | [eval](https://sharegpt.com/c/76VX3eB) |
| 95 | 98 | 90 | 92 | 96 | 89 | [eval](https://sharegpt.com/c/JK1uNef) |
| 90 | 95 | 75 | 85 | 80 | 82 | [eval](https://sharegpt.com/c/ku6CKmx) |
| 95 | 98 | 50 | 92 | 96 | 94 | [eval](https://sharegpt.com/c/0iAFuKW) |
| 95 | 90 | 0 | 93 | 92 | 94 | [eval](https://sharegpt.com/c/6uGnKio) |
| 95 | 90 | 85 | 92 | 80 | 88 | [eval](https://sharegpt.com/c/lfpRBw8) |
| 95 | 93 | 75 | 85 | 90 | 92 | [eval](https://sharegpt.com/c/mKu70jb) |
| 90 | 95 | 88 | 85 | 92 | 89 | [eval](https://sharegpt.com/c/GkYzJHO) |
| 100 | 100 | 100 | 95 | 97 | 98 | [eval](https://sharegpt.com/c/mly2k0z) |
| 85 | 40 | 30 | 95 | 90 | 88 | [eval](https://sharegpt.com/c/5td2ob0) |
| 90 | 95 | 92 | 85 | 88 | 93 | [eval](https://sharegpt.com/c/0ISpWfy) |
| 95 | 96 | 92 | 90 | 89 | 93 | [eval](https://sharegpt.com/c/kdUDUn7) |
| 90 | 95 | 85 | 80 | 92 | 88 | [eval](https://sharegpt.com/c/fjMNYr2) |
| 95 | 98 | 65 | 90 | 85 | 93 | [eval](https://sharegpt.com/c/6xBIf2Q) |
| 95 | 92 | 96 | 97 | 90 | 89 | [eval](https://sharegpt.com/c/B9GY8Ln) |
| 95 | 90 | 92 | 91 | 89 | 93 | [eval](https://sharegpt.com/c/vn1FPU4) |
| 95 | 90 | 80 | 75 | 95 | 90 | [eval](https://sharegpt.com/c/YurEMYg) |
| 92 | 40 | 30 | 95 | 90 | 93 | [eval](https://sharegpt.com/c/D19Qeui) |
| 90 | 92 | 85 | 88 | 89 | 87 | [eval](https://sharegpt.com/c/5QRFfrt) |
| 95 | 80 | 90 | 92 | 91 | 88 | [eval](https://sharegpt.com/c/pYWPRi4) |
| 95 | 93 | 92 | 90 | 91 | 94 | [eval](https://sharegpt.com/c/wPRTntL) |
| 100 | 98 | 95 | 90 | 92 | 96 | [eval](https://sharegpt.com/c/F6PLYKE) |
| 95 | 92 | 80 | 85 | 90 | 93 | [eval](https://sharegpt.com/c/WeJnMGv) |
| 95 | 98 | 90 | 88 | 97 | 96 | [eval](https://sharegpt.com/c/zNKL49e) |
| 90 | 95 | 85 | 88 | 86 | 92 | [eval](https://sharegpt.com/c/kIKmA1b) |
| 100 | 100 | 100 | 100 | 100 | 100 | [eval](https://sharegpt.com/c/1btWd4O) |
| 90 | 95 | 85 | 96 | 92 | 88 | [eval](https://sharegpt.com/c/s9sf1Lp) |
| 100 | 98 | 95 | 99 | 97 | 96 | [eval](https://sharegpt.com/c/RWzv8py) |
| 95 | 92 | 70 | 90 | 93 | 89 | [eval](https://sharegpt.com/c/bYF7FqA) |
| 95 | 90 | 88 | 92 | 94 | 93 | [eval](https://sharegpt.com/c/SuUqjMj) |
| 95 | 90 | 93 | 92 | 85 | 94 | [eval](https://sharegpt.com/c/r0aRdYY) |
| 95 | 93 | 90 | 87 | 92 | 91 | [eval](https://sharegpt.com/c/VuMfkkd) |
| 95 | 93 | 90 | 96 | 92 | 91 | [eval](https://sharegpt.com/c/rhm6fa4) |
| 95 | 97 | 85 | 96 | 98 | 90 | [eval](https://sharegpt.com/c/DwXnyqG) |
| 95 | 92 | 90 | 85 | 93 | 94 | [eval](https://sharegpt.com/c/0ScdkGS) |
| 95 | 96 | 92 | 90 | 97 | 93 | [eval](https://sharegpt.com/c/6yIoCDU) |
| 95 | 93 | 96 | 94 | 90 | 92 | [eval](https://sharegpt.com/c/VubEvp9) |
| 95 | 94 | 93 | 92 | 90 | 89 | [eval](https://sharegpt.com/c/RHzmZWG) |
| 90 | 85 | 95 | 80 | 87 | 75 | [eval](https://sharegpt.com/c/IMiP9Zm) |
| 95 | 94 | 92 | 93 | 90 | 96 | [eval](https://sharegpt.com/c/bft4PIL) |
| 95 | 100 | 90 | 95 | 95 | 95 | [eval](https://sharegpt.com/c/iHXB34b) |
| 100 | 95 | 85 | 100 | 0 | 90 | [eval](https://sharegpt.com/c/vCGn9R7) |
| 100 | 95 | 90 | 95 | 100 | 95 | [eval](https://sharegpt.com/c/be8crZL) |
| 95 | 90 | 60 | 95 | 85 | 80 | [eval](https://sharegpt.com/c/33elmDz) |
| 100 | 95 | 90 | 98 | 97 | 99 | [eval](https://sharegpt.com/c/RWD3Zx7) |
| 95 | 90 | 85 | 95 | 80 | 92 | [eval](https://sharegpt.com/c/GiwBvM7) |
| 100 | 95 | 100 | 98 | 100 | 90 | [eval](https://sharegpt.com/c/hX2pYxk) |
| 100 | 95 | 80 | 85 | 90 | 85 | [eval](https://sharegpt.com/c/MfxdGd7) |
| 100 | 90 | 95 | 85 | 95 | 100 | [eval](https://sharegpt.com/c/28hQjmS) |
| 95 | 90 | 85 | 80 | 88 | 92 | [eval](https://sharegpt.com/c/fzy5EPe) |
| 100 | 100 | 0 | 0 | 100 | 0 | [eval](https://sharegpt.com/c/vwxPjbR) |
| 100 | 100 | 100 | 50 | 100 | 75 | [eval](https://sharegpt.com/c/FAYfFWy) |
| 100 | 100 | 0 | 0 | 100 | 0 | [eval](https://sharegpt.com/c/SoudGsQ) |
| 0 | 100 | 0 | 0 | 0 | 0 | [eval](https://sharegpt.com/c/mkwEgVn) |
| 100 | 100 | 50 | 0 | 0 | 0 | [eval](https://sharegpt.com/c/q8MQEsz) |
| 100 | 100 | 100 | 100 | 100 | 95 | [eval](https://sharegpt.com/c/tzHpsKh) |
| 100 | 100 | 50 | 0 | 0 | 0 | [eval](https://sharegpt.com/c/3ugYBtJ) |
| 100 | 100 | 0 | 0 | 100 | 0 | [eval](https://sharegpt.com/c/I6KfOJT) |
| 90 | 85 | 80 | 95 | 70 | 75 | [eval](https://sharegpt.com/c/enaV1CK) |
| 100 | 100 | 0 | 0 | 0 | 0 | [eval](https://sharegpt.com/c/JBk7oSh) |
</details>
### Training data
This was an experiment to see if a "jailbreak" prompt could be used to generate a broader range of data that would otherwise have been filtered by OpenAI's alignment efforts.
The jailbreak did indeed work with a high success rate, and caused OpenAI to generate a broader range of topics and fewer refusals to answer questions/instructions of sensitive topics.
### Prompt format
The prompt should be 1:1 compatible with the FastChat/vicuna format, e.g.:
With a system prompt:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [prompt] ASSISTANT:
```
Or without a system prompt:
```
USER: [prompt] ASSISTANT:
```
### Usage and License Notices
The model and dataset are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
samaksh-khatri-crest-data/gmra_model_gpt2-medium_14082023T134929
|
samaksh-khatri-crest-data
| 2023-08-14T09:05:39Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:openai-community/gpt2-medium",
"base_model:finetune:openai-community/gpt2-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T08:19:30Z |
---
license: mit
base_model: gpt2-medium
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gmra_model_gpt2-medium_14082023T134929
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. -->
# gmra_model_gpt2-medium_14082023T134929
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3831
- Accuracy: 0.9438
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 284 | 0.2626 | 0.9069 |
| 0.3464 | 2.0 | 568 | 0.2263 | 0.9262 |
| 0.3464 | 3.0 | 852 | 0.2545 | 0.9394 |
| 0.1022 | 4.0 | 1137 | 0.2577 | 0.9464 |
| 0.1022 | 5.0 | 1421 | 0.3485 | 0.9420 |
| 0.0292 | 6.0 | 1705 | 0.3445 | 0.9429 |
| 0.0292 | 7.0 | 1989 | 0.3127 | 0.9464 |
| 0.0125 | 8.0 | 2274 | 0.4068 | 0.9411 |
| 0.0085 | 9.0 | 2558 | 0.3853 | 0.9438 |
| 0.0085 | 9.99 | 2840 | 0.3831 | 0.9438 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Skie0007/reinforce-pixel
|
Skie0007
| 2023-08-14T09:02:11Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T08:24:43Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixel
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 6.50 +/- 8.95
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
wangxso/ppo-PyramidsTraining
|
wangxso
| 2023-08-14T08:58:00Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-08-14T08:57:57Z |
---
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: wangxso/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ai-forever/mGPT-1.3B-ukranian
|
ai-forever
| 2023-08-14T08:57:08Z | 31 | 3 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt3",
"mgpt",
"uk",
"en",
"ru",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-10T05:12:23Z |
---
language:
- uk
- en
- ru
license: mit
tags:
- gpt3
- transformers
- mgpt
---
# 🇺🇦 Ukranian mGPT 1.3B
Language model for Ukranian. Model has 1.3B parameters as you can guess from it's name.
Ukranian belongs to Indo-European language family. It's a very melodic language with approximately 40 million speakers. Here are some facts about it:
1. One of the East Slavic languages, alongside Russian and Belarusian.
2. It is the official language of Ukraine and is written in a version of the Cyrillic script.
3. Ukrainian has a rich literary history, it has maintained a vibrant cultural presence, especially in poetry and music.
## Technical details
It's one of the models derived from the base [mGPT-XL (1.3B)](https://huggingface.co/ai-forever/mGPT) model (see the list below) which was originally trained on the 61 languages from 25 language families using Wikipedia and C4 corpus.
We've found additional data for 23 languages most of which are considered as minor and decided to further tune the base model. **Ukranian mGPT 1.3B** was trained for another 10000 steps with batch_size=4 and context window of **2048** tokens on 1 A100.
Final perplexity for this model on validation is **7.1**.
_Chart of the training loss and perplexity:_
## Other mGPT-1.3B models
- [🇦🇲 mGPT-1.3B Armenian](https://huggingface.co/ai-forever/mGPT-1.3B-armenian)
- [🇦🇿 mGPT-1.3B Azerbaijan](https://huggingface.co/ai-forever/mGPT-1.3B-azerbaijan)
- [🍯 mGPT-1.3B Bashkir](https://huggingface.co/ai-forever/mGPT-1.3B-bashkir)
- [🇧🇾 mGPT-1.3B Belorussian](https://huggingface.co/ai-forever/mGPT-1.3B-belorussian)
- [🇧🇬 mGPT-1.3B Bulgarian](https://huggingface.co/ai-forever/mGPT-1.3B-bulgarian)
- [🌞 mGPT-1.3B Buryat](https://huggingface.co/ai-forever/mGPT-1.3B-buryat)
- [🌳 mGPT-1.3B Chuvash](https://huggingface.co/ai-forever/mGPT-1.3B-chuvash)
- [🇬🇪 mGPT-1.3B Georgian](https://huggingface.co/ai-forever/mGPT-1.3B-georgian)
- [🌸 mGPT-1.3B Kalmyk](https://huggingface.co/ai-forever/mGPT-1.3B-kalmyk)
- [🇰🇿 mGPT-1.3B Kazakh](https://huggingface.co/ai-forever/mGPT-1.3B-kazakh)
- [🇰🇬 mGPT-1.3B Kirgiz](https://huggingface.co/ai-forever/mGPT-1.3B-kirgiz)
- [🐻 mGPT-1.3B Mari](https://huggingface.co/ai-forever/mGPT-1.3B-mari)
- [🇲🇳 mGPT-1.3B Mongol](https://huggingface.co/ai-forever/mGPT-1.3B-mongol)
- [🐆 mGPT-1.3B Ossetian](https://huggingface.co/ai-forever/mGPT-1.3B-ossetian)
- [🇮🇷 mGPT-1.3B Persian](https://huggingface.co/ai-forever/mGPT-1.3B-persian)
- [🇷🇴 mGPT-1.3B Romanian](https://huggingface.co/ai-forever/mGPT-1.3B-romanian)
- [🇹🇯 mGPT-1.3B Tajik](https://huggingface.co/ai-forever/mGPT-1.3B-tajik)
- [☕ mGPT-1.3B Tatar](https://huggingface.co/ai-forever/mGPT-1.3B-tatar)
- [🇹🇲 mGPT-1.3B Turkmen](https://huggingface.co/ai-forever/mGPT-1.3B-turkmen)
- [🐎 mGPT-1.3B Tuvan](https://huggingface.co/ai-forever/mGPT-1.3B-tuvan)
- [🇺🇿 mGPT-1.3B Uzbek](https://huggingface.co/ai-forever/mGPT-1.3B-uzbek)
- [💎 mGPT-1.3B Yakut](https://huggingface.co/ai-forever/mGPT-1.3B-yakut)
## Feedback
If you'll find a bug or have additional data to train a model on your language — **please, give us feedback**.
Model will be improved over time. Stay tuned!
|
nagupv/Stable13B_contextLLMExam_f4
|
nagupv
| 2023-08-14T08:31:09Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T08:30:46Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round3__0073
|
bigmorning
| 2023-08-14T08:28:57Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T08:28:49Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0073
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_round3__0073
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0013
- Train Accuracy: 0.0795
- Train Wermet: 8.0558
- Validation Loss: 0.5732
- Validation Accuracy: 0.0770
- Validation Wermet: 6.5109
- Epoch: 72
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
| 0.0010 | 0.0795 | 7.8135 | 0.5750 | 0.0770 | 7.0532 | 66 |
| 0.0009 | 0.0795 | 7.7899 | 0.5646 | 0.0770 | 6.8492 | 67 |
| 0.0007 | 0.0795 | 7.7019 | 0.5691 | 0.0771 | 6.6536 | 68 |
| 0.0005 | 0.0795 | 7.7786 | 0.5695 | 0.0771 | 6.3958 | 69 |
| 0.0010 | 0.0795 | 7.8106 | 0.5724 | 0.0771 | 6.8654 | 70 |
| 0.0013 | 0.0795 | 8.2501 | 0.5772 | 0.0770 | 6.9794 | 71 |
| 0.0013 | 0.0795 | 8.0558 | 0.5732 | 0.0770 | 6.5109 | 72 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0072
|
bigmorning
| 2023-08-14T08:24:48Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T08:24:40Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0072
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_round3__0072
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0013
- Train Accuracy: 0.0795
- Train Wermet: 8.2501
- Validation Loss: 0.5772
- Validation Accuracy: 0.0770
- Validation Wermet: 6.9794
- Epoch: 71
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
| 0.0010 | 0.0795 | 7.8135 | 0.5750 | 0.0770 | 7.0532 | 66 |
| 0.0009 | 0.0795 | 7.7899 | 0.5646 | 0.0770 | 6.8492 | 67 |
| 0.0007 | 0.0795 | 7.7019 | 0.5691 | 0.0771 | 6.6536 | 68 |
| 0.0005 | 0.0795 | 7.7786 | 0.5695 | 0.0771 | 6.3958 | 69 |
| 0.0010 | 0.0795 | 7.8106 | 0.5724 | 0.0771 | 6.8654 | 70 |
| 0.0013 | 0.0795 | 8.2501 | 0.5772 | 0.0770 | 6.9794 | 71 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
phatpt/ppo-Huggy
|
phatpt
| 2023-08-14T08:23:33Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-14T08:23:27Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: phatpt/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bigmorning/whisper_charsplit_new_round3__0071
|
bigmorning
| 2023-08-14T08:20:41Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T08:20:29Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0071
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_round3__0071
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0010
- Train Accuracy: 0.0795
- Train Wermet: 7.8106
- Validation Loss: 0.5724
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8654
- Epoch: 70
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
| 0.0010 | 0.0795 | 7.8135 | 0.5750 | 0.0770 | 7.0532 | 66 |
| 0.0009 | 0.0795 | 7.7899 | 0.5646 | 0.0770 | 6.8492 | 67 |
| 0.0007 | 0.0795 | 7.7019 | 0.5691 | 0.0771 | 6.6536 | 68 |
| 0.0005 | 0.0795 | 7.7786 | 0.5695 | 0.0771 | 6.3958 | 69 |
| 0.0010 | 0.0795 | 7.8106 | 0.5724 | 0.0771 | 6.8654 | 70 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA-GoldP_Te
|
vnktrmnb
| 2023-08-14T08:20:26Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-21T20:10:34Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA-GoldP_Te
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. -->
# vnktrmnb/bert-base-multilingual-cased-finetuned-TyDiQA-GoldP_Te
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3152
- Train End Logits Accuracy: 0.9004
- Train Start Logits Accuracy: 0.9263
- Validation Loss: 0.4931
- Validation End Logits Accuracy: 0.8686
- Validation Start Logits Accuracy: 0.9162
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1359, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.7083 | 0.7903 | 0.8387 | 0.4992 | 0.8505 | 0.8892 | 0 |
| 0.4552 | 0.8584 | 0.8957 | 0.4905 | 0.8686 | 0.8995 | 1 |
| 0.3152 | 0.9004 | 0.9263 | 0.4931 | 0.8686 | 0.9162 | 2 |
### Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
HuHu88/ddpm-celebahq-finetuned-butterflies-2epochs
|
HuHu88
| 2023-08-14T08:12:45Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-08-14T08:11:54Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('HuHu88/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
bigmorning/whisper_charsplit_new_round3__0069
|
bigmorning
| 2023-08-14T08:12:18Z | 58 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T08:12:11Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0069
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_round3__0069
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0007
- Train Accuracy: 0.0795
- Train Wermet: 7.7019
- Validation Loss: 0.5691
- Validation Accuracy: 0.0771
- Validation Wermet: 6.6536
- Epoch: 68
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
| 0.0010 | 0.0795 | 7.8135 | 0.5750 | 0.0770 | 7.0532 | 66 |
| 0.0009 | 0.0795 | 7.7899 | 0.5646 | 0.0770 | 6.8492 | 67 |
| 0.0007 | 0.0795 | 7.7019 | 0.5691 | 0.0771 | 6.6536 | 68 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0068
|
bigmorning
| 2023-08-14T08:08:09Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T08:08:02Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0068
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_round3__0068
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0009
- Train Accuracy: 0.0795
- Train Wermet: 7.7899
- Validation Loss: 0.5646
- Validation Accuracy: 0.0770
- Validation Wermet: 6.8492
- Epoch: 67
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
| 0.0010 | 0.0795 | 7.8135 | 0.5750 | 0.0770 | 7.0532 | 66 |
| 0.0009 | 0.0795 | 7.7899 | 0.5646 | 0.0770 | 6.8492 | 67 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0066
|
bigmorning
| 2023-08-14T07:59:56Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:59:48Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0066
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_round3__0066
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0020
- Train Accuracy: 0.0795
- Train Wermet: 7.9131
- Validation Loss: 0.5655
- Validation Accuracy: 0.0770
- Validation Wermet: 7.0500
- Epoch: 65
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
| 0.0020 | 0.0795 | 7.9131 | 0.5655 | 0.0770 | 7.0500 | 65 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
wangxso/ppo-SnowballTarget
|
wangxso
| 2023-08-14T07:55:52Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-08-14T07:55:49Z |
---
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: wangxso/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bigmorning/whisper_charsplit_new_round3__0065
|
bigmorning
| 2023-08-14T07:55:48Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:55:41Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0065
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_round3__0065
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0019
- Train Accuracy: 0.0795
- Train Wermet: 7.8859
- Validation Loss: 0.5789
- Validation Accuracy: 0.0769
- Validation Wermet: 6.9689
- Epoch: 64
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
| 0.0019 | 0.0795 | 7.8859 | 0.5789 | 0.0769 | 6.9689 | 64 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
merryjane/q-FrozenLake-v1-4x4-noSlippery
|
merryjane
| 2023-08-14T07:52:00Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T07:51:55Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="merryjane/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
bigmorning/whisper_charsplit_new_round3__0064
|
bigmorning
| 2023-08-14T07:51:39Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:51:30Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0064
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_round3__0064
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0005
- Train Accuracy: 0.0795
- Train Wermet: 7.6199
- Validation Loss: 0.5783
- Validation Accuracy: 0.0770
- Validation Wermet: 6.9551
- Epoch: 63
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
| 0.0002 | 0.0795 | 7.5238 | 0.5561 | 0.0772 | 6.6962 | 61 |
| 0.0002 | 0.0795 | 7.4915 | 0.5613 | 0.0772 | 6.6315 | 62 |
| 0.0005 | 0.0795 | 7.6199 | 0.5783 | 0.0770 | 6.9551 | 63 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
thibaud/controlnet-sd21-openpose-diffusers
|
thibaud
| 2023-08-14T07:44:42Z | 28 | 14 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-03-10T12:41:24Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### Openpose:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
thibaud/controlnet-sd21-scribble-diffusers
|
thibaud
| 2023-08-14T07:44:23Z | 8 | 0 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-03-10T22:59:55Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### Scribble:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
thibaud/controlnet-sd21-zoedepth-diffusers
|
thibaud
| 2023-08-14T07:44:03Z | 7 | 6 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-03-23T20:50:11Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### ZoeDepth:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
thibaud/controlnet-sd21-openposev2-diffusers
|
thibaud
| 2023-08-14T07:43:52Z | 8 | 2 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-04-08T18:53:05Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### OpenPose v2:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
thibaud/controlnet-sd21-lineart-diffusers
|
thibaud
| 2023-08-14T07:43:36Z | 9 | 3 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-04-10T12:04:54Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### Lineart v2:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
thibaud/controlnet-sd21
|
thibaud
| 2023-08-14T07:43:07Z | 18,429 | 395 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"dataset:laion/laion-art",
"license:other",
"region:us"
] | null | 2023-03-06T15:24:04Z |
---
language:
- en
license: other
tags:
- art
- diffusers
- stable diffusion
- controlnet
datasets: laion/laion-art
---
Want to support my work: you can bought my Artbook: https://thibaud.art
___
Here's the first version of controlnet for stablediffusion 2.1
Trained on a subset of laion/laion-art
License: refers to the different preprocessor's ones.
### Safetensors version uploaded, only 700mb!
### Canny:
### Depth:
### ZoeDepth:
### Hed:
### Scribble:
### OpenPose:
### Color:
### OpenPose:
### LineArt:
### Ade20K:
### Normal BAE:
### To use with Automatic1111:
* Download the ckpt files or safetensors ones
* Put it in extensions/sd-webui-controlnet/models
* in settings/controlnet, change cldm_v15.yaml by cldm_v21.yaml
* Enjoy
### To use ZoeDepth:
You can use it with annotator depth/le_res but it works better with ZoeDepth Annotator. My PR is not accepted yet but you can use my fork.
My fork: https://github.com/thibaudart/sd-webui-controlnet
The PR: https://github.com/Mikubill/sd-webui-controlnet/pull/655#issuecomment-1481724024
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
Thanks https://huggingface.co/lllyasviel/ for the implementation and the release of 1.5 models.
Thanks https://huggingface.co/p1atdev/ for the conversion script from ckpt to safetensors pruned & fp16
### Models can't be sell, merge, distributed without prior writing agreement.
|
thibaud/controlnet-sd21-ade20k-diffusers
|
thibaud
| 2023-08-14T07:41:14Z | 10 | 0 |
diffusers
|
[
"diffusers",
"art",
"stable diffusion",
"controlnet",
"en",
"license:other",
"region:us"
] | null | 2023-04-10T12:04:59Z |
---
license: other
language:
- en
tags:
- art
- diffusers
- stable diffusion
- controlnet
---
Here's the first version of controlnet for stablediffusion 2.1 for diffusers
Trained on a subset of laion/laion-art
### ADE 20K:
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
License: refers to the ade20K's one.
Thanks
- https://huggingface.co/lllyasviel/ControlNet for the implementation and the release of 1.5 models.
- https://huggingface.co/thepowefuldeez for the conversion script to diffusers
|
bigmorning/whisper_charsplit_new_round3__0061
|
bigmorning
| 2023-08-14T07:39:05Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:38:56Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0061
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_round3__0061
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0004
- Train Accuracy: 0.0795
- Train Wermet: 7.6676
- Validation Loss: 0.5577
- Validation Accuracy: 0.0771
- Validation Wermet: 6.7898
- Epoch: 60
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
| 0.0004 | 0.0795 | 7.6676 | 0.5577 | 0.0771 | 6.7898 | 60 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0060
|
bigmorning
| 2023-08-14T07:34:51Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:34:45Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0060
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_round3__0060
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0009
- Train Accuracy: 0.0795
- Train Wermet: 7.4969
- Validation Loss: 0.5584
- Validation Accuracy: 0.0771
- Validation Wermet: 6.7292
- Epoch: 59
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
| 0.0009 | 0.0795 | 7.4969 | 0.5584 | 0.0771 | 6.7292 | 59 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Skie0007/Reinforce-cartpole
|
Skie0007
| 2023-08-14T07:34:29Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T07:34:18Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Den4ikAI/rubert-tiny2-retriever
|
Den4ikAI
| 2023-08-14T07:33:22Z | 2 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"ru",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-08-07T13:33:54Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: mit
language:
- ru
widget:
- source_sentence: "query: Когда родился Пушкин?"
sentences:
- "passage: Алекса́ндр Серге́евич Пу́шкин (26 мая [6 июня] 1799, Москва — 29 января [10 февраля] 1837, Санкт-Петербург) — русский поэт, драматург и прозаик, заложивший основы русского реалистического направления[2], литературный критик[3] и теоретик литературы, историк[3], публицист, журналист[3]."
- "passage: Пушкин ловил кайф со своими друзьями"
- "passage: Пушкин из самых авторитетных литературных деятелей первой трети XIX века. Ещё при жизни Пушкина сложилась его репутация величайшего национального русского поэта[4][5]. Пушкин рассматривается как основоположник современного русского литературного языка[~ 2]."
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 312 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 966 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 966,
"weight_decay": 1e-05
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
bookbot/sherpa-ncnn-pruned-transducer-stateless7-streaming-id
|
bookbot
| 2023-08-14T07:32:20Z | 0 | 1 | null |
[
"icefall",
"sherpa-ncnn",
"phoneme-recognition",
"automatic-speech-recognition",
"id",
"dataset:mozilla-foundation/common_voice_13_0",
"dataset:indonesian-nlp/librivox-indonesia",
"dataset:google/fleurs",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2023-06-23T07:58:15Z |
---
language: id
license: apache-2.0
tags:
- icefall
- sherpa-ncnn
- phoneme-recognition
- automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_13_0
- indonesian-nlp/librivox-indonesia
- google/fleurs
---
# Sherpa-ncnn Pruned Stateless Zipformer RNN-T Streaming ID
Sherpa-ncnn Pruned Stateless Zipformer RNN-T Streaming ID is an automatic speech recognition model trained on the following datasets:
- [Common Voice ID](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
- [LibriVox Indonesia](https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia)
- [FLEURS ID](https://huggingface.co/datasets/google/fleurs)
Instead of being trained to predict sequences of words, this model was trained to predict sequence of phonemes, e.g. `['p', 'ə', 'r', 'b', 'u', 'a', 't', 'a', 'n', 'ɲ', 'a']`. Therefore, the model's [vocabulary](https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id/blob/main/data/lang_phone/tokens.txt) contains the different IPA phonemes found in [g2p ID](https://github.com/bookbot-kids/g2p_id).
This model was converted from the TorchScript version of [Pruned Stateless Zipformer RNN-T Streaming ID](https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id) to ncnn format.
## Converting from TorchScript
Refer to the [official instructions](https://icefall.readthedocs.io/en/latest/model-export/export-ncnn-zipformer.html) for conversion to ncnn, which includes installation of `csukuangfj`'s [ncnn](https://github.com/csukuangfj/ncnn) fork.
## Frameworks
- [k2](https://github.com/k2-fsa/k2)
- [icefall](https://github.com/bookbot-hive/icefall)
- [lhotse](https://github.com/bookbot-hive/lhotse)
- [sherpa-ncnn](https://github.com/k2-fsa/sherpa-ncnn)
- [ncnn](https://github.com/csukuangfj/ncnn)
|
bookbot/pruned-transducer-stateless7-streaming-id
|
bookbot
| 2023-08-14T07:31:44Z | 0 | 0 | null |
[
"tensorboard",
"icefall",
"phoneme-recognition",
"automatic-speech-recognition",
"id",
"dataset:mozilla-foundation/common_voice_13_0",
"dataset:indonesian-nlp/librivox-indonesia",
"dataset:google/fleurs",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2023-06-21T10:13:29Z |
---
language: id
license: apache-2.0
tags:
- icefall
- phoneme-recognition
- automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_13_0
- indonesian-nlp/librivox-indonesia
- google/fleurs
---
# Pruned Stateless Zipformer RNN-T Streaming ID
Pruned Stateless Zipformer RNN-T Streaming ID is an automatic speech recognition model trained on the following datasets:
- [Common Voice ID](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
- [LibriVox Indonesia](https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia)
- [FLEURS ID](https://huggingface.co/datasets/google/fleurs)
Instead of being trained to predict sequences of words, this model was trained to predict sequence of phonemes, e.g. `['p', 'ə', 'r', 'b', 'u', 'a', 't', 'a', 'n', 'ɲ', 'a']`. Therefore, the model's [vocabulary](https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id/blob/main/data/lang_phone/tokens.txt) contains the different IPA phonemes found in [g2p ID](https://github.com/bookbot-kids/g2p_id).
This model was trained using [icefall](https://github.com/k2-fsa/icefall) framework. All training was done on a Scaleway RENDER-S VM with a Tesla P100 GPU. All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id/tree/main) tab, as well as the [Training metrics](https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id/tensorboard) logged via Tensorboard.
## Evaluation Results
### Simulated Streaming
```sh
for m in greedy_search fast_beam_search modified_beam_search; do
./pruned_transducer_stateless7_streaming/decode.py \
--epoch 30 \
--avg 9 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method $m
done
```
The model achieves the following phoneme error rates on the different test sets:
| Decoding | LibriVox | FLEURS | Common Voice |
| -------------------- | :------: | :----: | :----------: |
| Greedy Search | 4.87% | 11.45% | 14.97% |
| Modified Beam Search | 4.71% | 11.25% | 14.31% |
| Fast Beam Search | 4.85% | 12.55% | 14.89% |
### Chunk-wise Streaming
```sh
for m in greedy_search fast_beam_search modified_beam_search; do
./pruned_transducer_stateless7_streaming/streaming_decode.py \
--epoch 30 \
--avg 9 \
--exp-dir ./pruned_transducer_stateless7_streaming/exp \
--decoding-method $m \
--decode-chunk-len 32 \
--num-decode-streams 1500
done
```
The model achieves the following phoneme error rates on the different test sets:
| Decoding | LibriVox | FLEURS | Common Voice |
| -------------------- | :------: | :----: | :----------: |
| Greedy Search | 5.12% | 12.74% | 15.78% |
| Modified Beam Search | 4.78% | 11.83% | 14.54% |
| Fast Beam Search | 4.81% | 12.93% | 14.96% |
## Usage
### Download Pre-trained Model
```sh
cd egs/bookbot/ASR
mkdir tmp
cd tmp
git lfs install
git clone https://huggingface.co/bookbot/pruned-transducer-stateless7-streaming-id
```
### Inference
To decode with greedy search, run:
```sh
./pruned_transducer_stateless7_streaming/jit_pretrained.py \
--nn-model-filename ./tmp/pruned-transducer-stateless7-streaming-id/exp/cpu_jit.pt \
--lang-dir ./tmp/pruned-transducer-stateless7-streaming-id/data/lang_phone \
./tmp/pruned-transducer-stateless7-streaming-id/test_waves/sample1.wav
```
<details>
<summary>Decoding Output</summary>
```
2023-06-21 10:19:18,563 INFO [jit_pretrained.py:217] device: cpu
2023-06-21 10:19:19,231 INFO [lexicon.py:168] Loading pre-compiled tmp/pruned-transducer-stateless7-streaming-id/data/lang_phone/Linv.pt
2023-06-21 10:19:19,232 INFO [jit_pretrained.py:228] Constructing Fbank computer
2023-06-21 10:19:19,233 INFO [jit_pretrained.py:238] Reading sound files: ['./tmp/pruned-transducer-stateless7-streaming-id/test_waves/sample1.wav']
2023-06-21 10:19:19,234 INFO [jit_pretrained.py:244] Decoding started
2023-06-21 10:19:20,090 INFO [jit_pretrained.py:271]
./tmp/pruned-transducer-stateless7-streaming-id/test_waves/sample1.wav:
p u l a ŋ | s ə k o l a h | p i t ə r i | s a ŋ a t | l a p a r
2023-06-21 10:19:20,090 INFO [jit_pretrained.py:273] Decoding Done
```
</details>
## Training procedure
### Install icefall
```sh
git clone https://github.com/bookbot-hive/icefall
cd icefall
export PYTHONPATH=`pwd`:$PYTHONPATH
```
### Prepare Data
```sh
cd egs/bookbot_id/ASR
./prepare.sh
```
### Train
```sh
export CUDA_VISIBLE_DEVICES="0"
./pruned_transducer_stateless7_streaming/train.py \
--num-epochs 30 \
--use-fp16 1 \
--max-duration 400
```
## Frameworks
- [k2](https://github.com/k2-fsa/k2)
- [icefall](https://github.com/bookbot-hive/icefall)
- [lhotse](https://github.com/bookbot-hive/lhotse)
|
bigmorning/whisper_charsplit_new_round3__0059
|
bigmorning
| 2023-08-14T07:30:48Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:30:41Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0059
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_round3__0059
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0030
- Train Accuracy: 0.0795
- Train Wermet: 7.4853
- Validation Loss: 0.5586
- Validation Accuracy: 0.0770
- Validation Wermet: 6.6707
- Epoch: 58
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
| 0.0030 | 0.0795 | 7.4853 | 0.5586 | 0.0770 | 6.6707 | 58 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0058
|
bigmorning
| 2023-08-14T07:26:41Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:26:32Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__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_round3__0058
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0113
- Train Accuracy: 0.0793
- Train Wermet: 7.8674
- Validation Loss: 0.5714
- Validation Accuracy: 0.0768
- Validation Wermet: 6.0540
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
| 0.0113 | 0.0793 | 7.8674 | 0.5714 | 0.0768 | 6.0540 | 57 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0057
|
bigmorning
| 2023-08-14T07:22:32Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:22:24Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0057
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_round3__0057
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.0364
- Validation Loss: 0.5907
- Validation Accuracy: 0.0772
- Validation Wermet: 7.0683
- Epoch: 56
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
| 0.0000 | 0.0795 | 7.9400 | 0.5822 | 0.0772 | 7.0438 | 53 |
| 0.0000 | 0.0795 | 7.9697 | 0.5846 | 0.0772 | 7.0785 | 54 |
| 0.0000 | 0.0795 | 8.0061 | 0.5875 | 0.0772 | 7.0840 | 55 |
| 0.0000 | 0.0795 | 8.0364 | 0.5907 | 0.0772 | 7.0683 | 56 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
gtxygyzb/distilbert-base-uncased-finetuned-imdb
|
gtxygyzb
| 2023-08-14T07:15:51Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-08-14T07:08:02Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4125
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7026 | 1.0 | 157 | 2.4957 |
| 2.581 | 2.0 | 314 | 2.4286 |
| 2.5363 | 3.0 | 471 | 2.4515 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
CyberHarem/hyuuga_hinata_naruto
|
CyberHarem
| 2023-08-14T07:11:38Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/hyuuga_hinata_naruto",
"license:mit",
"region:us"
] |
text-to-image
| 2023-08-14T07:08:01Z |
---
license: mit
datasets:
- CyberHarem/hyuuga_hinata_naruto
pipeline_tag: text-to-image
tags:
- art
---
# Lora of hyuuga_hinata_naruto
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 1500, you need to download `1500/hyuuga_hinata_naruto.pt` as the embedding and `1500/hyuuga_hinata_naruto.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The trigger word is `hyuuga_hinata_naruto`.**
These are available steps:
| Steps | pattern_1 | bikini | free | nude | Download |
|--------:|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------|
| 1500 |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/hyuuga_hinata_naruto.zip) |
| 1400 |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/hyuuga_hinata_naruto.zip) |
| 1300 |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/hyuuga_hinata_naruto.zip) |
| 1200 |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/hyuuga_hinata_naruto.zip) |
| 1100 |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/hyuuga_hinata_naruto.zip) |
| 1000 |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/hyuuga_hinata_naruto.zip) |
| 900 |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/hyuuga_hinata_naruto.zip) |
| 800 |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/hyuuga_hinata_naruto.zip) |
| 700 |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/hyuuga_hinata_naruto.zip) |
| 600 |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/hyuuga_hinata_naruto.zip) |
| 500 |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/hyuuga_hinata_naruto.zip) |
| 400 |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/hyuuga_hinata_naruto.zip) |
| 300 |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/hyuuga_hinata_naruto.zip) |
| 200 |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/hyuuga_hinata_naruto.zip) |
| 100 |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/hyuuga_hinata_naruto.zip) |
|
bigmorning/whisper_charsplit_new_round3__0053
|
bigmorning
| 2023-08-14T07:05:55Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:05:45Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0053
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_round3__0053
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.9169
- Validation Loss: 0.5794
- Validation Accuracy: 0.0772
- Validation Wermet: 7.0149
- Epoch: 52
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
| 0.0000 | 0.0795 | 7.9169 | 0.5794 | 0.0772 | 7.0149 | 52 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0052
|
bigmorning
| 2023-08-14T07:01:45Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T07:01:37Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__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_round3__0052
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.8924
- Validation Loss: 0.5770
- Validation Accuracy: 0.0772
- Validation Wermet: 6.9928
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
| 0.0000 | 0.0795 | 7.8924 | 0.5770 | 0.0772 | 6.9928 | 51 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0051
|
bigmorning
| 2023-08-14T06:57:31Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T06:57:22Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0051
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_round3__0051
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.8755
- Validation Loss: 0.5744
- Validation Accuracy: 0.0772
- Validation Wermet: 6.9767
- Epoch: 50
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
| 0.0000 | 0.0795 | 7.6768 | 0.5556 | 0.0771 | 6.8287 | 41 |
| 0.0000 | 0.0795 | 7.7199 | 0.5578 | 0.0772 | 6.8398 | 42 |
| 0.0000 | 0.0795 | 7.7423 | 0.5600 | 0.0772 | 6.8518 | 43 |
| 0.0000 | 0.0795 | 7.7561 | 0.5617 | 0.0772 | 6.8898 | 44 |
| 0.0000 | 0.0795 | 7.7766 | 0.5639 | 0.0772 | 6.8982 | 45 |
| 0.0000 | 0.0795 | 7.7962 | 0.5659 | 0.0772 | 6.9091 | 46 |
| 0.0000 | 0.0795 | 7.8106 | 0.5680 | 0.0772 | 6.9293 | 47 |
| 0.0000 | 0.0795 | 7.8387 | 0.5701 | 0.0772 | 6.9401 | 48 |
| 0.0000 | 0.0795 | 7.8480 | 0.5724 | 0.0772 | 6.9544 | 49 |
| 0.0000 | 0.0795 | 7.8755 | 0.5744 | 0.0772 | 6.9767 | 50 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
yadhikari/yogesh-a-v2
|
yadhikari
| 2023-08-14T06:56:14Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-14T06:50:18Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### yogesh-a-v2 Dreambooth model trained by yadhikari with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Dhiraj223/Dhiraj-bert-finetuned-squad
|
Dhiraj223
| 2023-08-14T06:55:33Z | 71 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-14T04:29:18Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Dhiraj223/Dhiraj-bert-finetuned-squad
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. -->
# Dhiraj223/Dhiraj-bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2570
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0002, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.9529 | 0 |
| 1.2570 | 1 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
mainuzzaman/llama-2-7b-miniguanaco
|
mainuzzaman
| 2023-08-14T06:54:33Z | 0 | 1 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T06:48:26Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
TheTravellingEngineer/llama2-7b-chat-hf-dpo
|
TheTravellingEngineer
| 2023-08-14T06:50:53Z | 1,530 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-14T06:33:07Z |
The base model is meta's Llama-2-7b-chat-hf. It was finetuned using DPO and the comparison_gpt4 dataset and the model prompt is similar to the original Guanaco model.
This repo contains the merged fp16 model.
**Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.**
---
- license:
- llama2 <br>
- datasets:
- comparison_gpt4 <br>
- language:
- en <br>
- reference: https://github.com/hiyouga/LLaMA-Efficient-Tuning/tree/main
---
|
wangxso/Reinforce-PG-Carpolt
|
wangxso
| 2023-08-14T06:48:58Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T06:48:56Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PG-Carpolt
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 20.50 +/- 8.13
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bjfxs/llama2-7b-200steps-finetunined-sxl-1
|
bjfxs
| 2023-08-14T06:41:24Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T06:41:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
msthil2/distilhubert-finetuned-gtzan
|
msthil2
| 2023-08-14T06:29:19Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-08-13T20:35:13Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5620
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9979 | 1.0 | 113 | 1.8250 | 0.39 |
| 1.3648 | 2.0 | 226 | 1.3015 | 0.58 |
| 1.0783 | 3.0 | 339 | 0.9586 | 0.78 |
| 0.8267 | 4.0 | 452 | 0.8479 | 0.74 |
| 0.7503 | 5.0 | 565 | 0.7404 | 0.76 |
| 0.404 | 6.0 | 678 | 0.6402 | 0.81 |
| 0.4935 | 7.0 | 791 | 0.5936 | 0.81 |
| 0.2201 | 8.0 | 904 | 0.5934 | 0.82 |
| 0.2689 | 9.0 | 1017 | 0.5614 | 0.81 |
| 0.1843 | 10.0 | 1130 | 0.5620 | 0.84 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
samaksh-khatri-crest-data/gmra_model_gpt2_14082023T112228
|
samaksh-khatri-crest-data
| 2023-08-14T06:27:56Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T05:52:28Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gmra_model_gpt2_14082023T112228
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. -->
# gmra_model_gpt2_14082023T112228
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3424
- Accuracy: 0.9016
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 71 | 0.7440 | 0.7636 |
| No log | 1.99 | 142 | 0.5466 | 0.8278 |
| No log | 2.99 | 213 | 0.4379 | 0.8656 |
| No log | 4.0 | 285 | 0.3959 | 0.8787 |
| No log | 5.0 | 356 | 0.3560 | 0.8919 |
| No log | 5.99 | 427 | 0.3442 | 0.8946 |
| No log | 6.99 | 498 | 0.3535 | 0.8954 |
| 0.5012 | 8.0 | 570 | 0.3232 | 0.9007 |
| 0.5012 | 9.0 | 641 | 0.3364 | 0.8989 |
| 0.5012 | 9.96 | 710 | 0.3424 | 0.9016 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
sara98/bert-finetuned-mrpc-trainerclass
|
sara98
| 2023-08-14T06:24:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T06:08:34Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: bert-finetuned-mrpc
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-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
|
prudhvirazz/google-flan-t5-small-modified_v2
|
prudhvirazz
| 2023-08-14T06:22:24Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-14T05:42:53Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: google-flan-t5-small-modified_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# google-flan-t5-small-modified_v2
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3020
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 3.7751 |
| 4.3074 | 2.0 | 500 | 2.1221 |
| 4.3074 | 3.0 | 750 | 1.7130 |
| 2.5366 | 4.0 | 1000 | 1.5271 |
| 2.5366 | 5.0 | 1250 | 1.4301 |
| 2.0483 | 6.0 | 1500 | 1.3643 |
| 2.0483 | 7.0 | 1750 | 1.3389 |
| 1.8774 | 8.0 | 2000 | 1.3123 |
| 1.8774 | 9.0 | 2250 | 1.3067 |
| 1.7724 | 10.0 | 2500 | 1.3020 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
morell23/crrysxmrky
|
morell23
| 2023-08-14T06:20:57Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-14T06:15:44Z |
---
license: creativeml-openrail-m
---
|
bigmorning/whisper_charsplit_new_round3__0041
|
bigmorning
| 2023-08-14T06:15:42Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T06:15:35Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__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_round3__0041
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.6623
- Validation Loss: 0.5535
- Validation Accuracy: 0.0771
- Validation Wermet: 6.7829
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
| 0.0000 | 0.0795 | 7.6623 | 0.5535 | 0.0771 | 6.7829 | 40 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
ryand1234/mllama2-testing
|
ryand1234
| 2023-08-14T06:14:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T03:19:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
chriskim2273/IOTNation_Classification_Model_0.8_6K_AND_ORIGINAL_DATASET_BERT
|
chriskim2273
| 2023-08-14T06:14:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T05:39:40Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: IOTNation_Classification_Model_0.8_6K_AND_ORIGINAL_DATASET_BERT
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. -->
# IOTNation_Classification_Model_0.8_6K_AND_ORIGINAL_DATASET_BERT
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0406
- Accuracy: 0.9946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 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 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
anikesh-mane/prefix-tuned-flan-t5-large
|
anikesh-mane
| 2023-08-14T06:13:10Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T06:13:09Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
phatpt/ppo-LunarLander-v2
|
phatpt
| 2023-08-14T06:12:18Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T06:11:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.16 +/- 19.35
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bigmorning/whisper_charsplit_new_round3__0040
|
bigmorning
| 2023-08-14T06:11:28Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T06:11:19Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0040
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_round3__0040
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 7.6100
- Validation Loss: 0.5515
- Validation Accuracy: 0.0771
- Validation Wermet: 6.7738
- Epoch: 39
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
| 0.0001 | 0.0795 | 7.6100 | 0.5515 | 0.0771 | 6.7738 | 39 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Araki/Bacchus-22B-GGML
|
Araki
| 2023-08-14T06:07:23Z | 0 | 0 | null |
[
"llama",
"ggml",
"text-generation",
"region:us"
] |
text-generation
| 2023-08-13T23:47:01Z |
---
pipeline_tag: text-generation
tags:
- llama
- ggml
---
**Quantization from:**
[Envoid/Bacchus-22B](https://huggingface.co/Envoid/Bacchus-22B)
**Converted to the GGML format with:**
[llama.cpp master-ee77efe (AUG 13, 2023)](https://github.com/ggerganov/llama.cpp/releases/tag/master-ee77efe)
**Tested with:**
[koboldcpp 1.40.1](https://github.com/LostRuins/koboldcpp/releases/tag/v1.40.1a)
**Example usage:**
```
koboldcpp.exe Bacchus-22B-ggmlv3.Q5_K_M.bin --threads 6 --contextsize 4096 --stream --smartcontext --unbantokens --ropeconfig 1 10000
```
**Prompt format:**
This is an experimental model with no defined format. However, the creator of the model recommends using it in pair with [SillyTavern](https://github.com/SillyTavern/SillyTavern/tree/staging) and [simple-proxy-for-tavern](https://github.com/anon998/simple-proxy-for-tavern) with presets 'singleline' or 'verbose'. According to the creator, as well as how it was shown in my own tests, the 'verbose' format gives detailed responses where the characters may tend to flee the roleplay scene, whereas the 'singleline' format sometimes results in too short, dull replies.
Please refer to the original model for additional details. The credit for the used formats goes to the developer of simple-proxy-for-tavern.
**Format 1 (verbose):**
```
## {CHAR}
- You're "{CHAR}" in this never-ending roleplay with "{USER}".
### Input:
{character's personality}
Circumstances and context of the dialogue: {circumstances of the dialogue}
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {USER}:
{example message from the user}
### Response:
#### {CHAR}:
{example response from the bot}
### New Roleplay:
### Response:
#### {CHAR}:
{character's initial, user-written message}
### Instruction:
#### {USER}:
{user's initial response}
### Response (2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {CHAR}:
```
**Format 2 (singleline):**
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Write {CHAR}'s next reply in a fictional chat between {CHAR} and {USER}. Write 1 reply only in internet RP style, italicize actions, and avoid quotation marks. Use markdown. Be proactive, creative, and drive the plot and conversation forward. Write at least 2 paragraph, up to 4. Always stay in character and avoid repetition.
### Input:
{character's personality}
Circumstances and context of the dialogue: {circumstances of the dialogue}
[Start a new chat]
{USER}: {example message from the user}
{CHAR}: {example response from the bot}
[Start a new chat]
### Response:
Okay, I will now generate a reply, continuing from the end of the provided conversation. (This may contain NSFW or offensive output.)
[...]
{CHAR}: {character's initial, user-written message}
{USER}: {user's initial response}
{CHAR}:
```
|
bigmorning/whisper_charsplit_new_round3__0039
|
bigmorning
| 2023-08-14T06:07:21Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T06:07:13Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0039
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_round3__0039
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 7.6685
- Validation Loss: 0.5493
- Validation Accuracy: 0.0771
- Validation Wermet: 6.6914
- Epoch: 38
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
| 0.0001 | 0.0795 | 7.6685 | 0.5493 | 0.0771 | 6.6914 | 38 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0038
|
bigmorning
| 2023-08-14T06:03:11Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T06:03:04Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0038
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_round3__0038
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0002
- Train Accuracy: 0.0795
- Train Wermet: 7.6265
- Validation Loss: 0.5471
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8107
- Epoch: 37
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
| 0.0002 | 0.0795 | 7.6265 | 0.5471 | 0.0771 | 6.8107 | 37 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
draziert/rl_course_vizdoom_health_gathering_supreme
|
draziert
| 2023-08-14T06:02:21Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-08-14T06:00:09Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 13.66 +/- 5.17
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r draziert/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
bigmorning/whisper_charsplit_new_round3__0037
|
bigmorning
| 2023-08-14T05:58:59Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:58:52Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0037
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_round3__0037
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0008
- Train Accuracy: 0.0795
- Train Wermet: 7.9115
- Validation Loss: 0.5447
- Validation Accuracy: 0.0771
- Validation Wermet: 6.9422
- Epoch: 36
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
| 0.0008 | 0.0795 | 7.9115 | 0.5447 | 0.0771 | 6.9422 | 36 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0036
|
bigmorning
| 2023-08-14T05:54:56Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:54:49Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0036
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_round3__0036
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0063
- Train Accuracy: 0.0794
- Train Wermet: 8.2517
- Validation Loss: 0.5491
- Validation Accuracy: 0.0769
- Validation Wermet: 7.1324
- Epoch: 35
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
| 0.0080 | 0.0794 | 8.3693 | 0.5947 | 0.0762 | 7.3034 | 34 |
| 0.0063 | 0.0794 | 8.2517 | 0.5491 | 0.0769 | 7.1324 | 35 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0034
|
bigmorning
| 2023-08-14T05:46:38Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:46:30Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0034
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_round3__0034
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.2872
- Validation Loss: 0.5744
- Validation Accuracy: 0.0772
- Validation Wermet: 7.2069
- Epoch: 33
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
| 0.0000 | 0.0795 | 8.3059 | 0.5721 | 0.0772 | 7.2341 | 32 |
| 0.0000 | 0.0795 | 8.2872 | 0.5744 | 0.0772 | 7.2069 | 33 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0032
|
bigmorning
| 2023-08-14T05:38:16Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:38:08Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0032
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_round3__0032
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.2992
- Validation Loss: 0.5700
- Validation Accuracy: 0.0772
- Validation Wermet: 7.2006
- Epoch: 31
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
| 0.0000 | 0.0795 | 8.2992 | 0.5700 | 0.0772 | 7.2006 | 31 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0031
|
bigmorning
| 2023-08-14T05:34:02Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:33:54Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0031
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_round3__0031
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.2607
- Validation Loss: 0.5689
- Validation Accuracy: 0.0771
- Validation Wermet: 7.2107
- Epoch: 30
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
| 0.0000 | 0.0795 | 8.2607 | 0.5689 | 0.0771 | 7.2107 | 30 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
DAMO-NLP-SG/zero-shot-classify-SSTuning-base
|
DAMO-NLP-SG
| 2023-08-14T05:33:57Z | 163 | 7 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"Zero-Shot Classification",
"zero-shot-classification",
"arxiv:2305.11442",
"license:mit",
"autotrain_compatible",
"region:us"
] |
zero-shot-classification
| 2023-05-18T11:14:04Z |
---
inference: false
license: mit
tags:
- Zero-Shot Classification
pipeline_tag: zero-shot-classification
---
# Zero-shot text classification (base-sized model) trained with self-supervised tuning
Zero-shot text classification model trained with self-supervised tuning (SSTuning).
It was introduced in the paper [Zero-Shot Text Classification via Self-Supervised Tuning](https://arxiv.org/abs/2305.11442) by
Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing
and first released in [this repository](https://github.com/DAMO-NLP-SG/SSTuning).
The model backbone is RoBERTa-base.
## Model description
The model is tuned with unlabeled data using a learning objective called first sentence prediction (FSP).
The FSP task is designed by considering both the nature of the unlabeled corpus and the input/output format of classification tasks.
The training and validation sets are constructed from the unlabeled corpus using FSP.
During tuning, BERT-like pre-trained masked language
models such as RoBERTa and ALBERT are employed as the backbone, and an output layer for classification is added.
The learning objective for FSP is to predict the index of the correct label.
A cross-entropy loss is used for tuning the model.
## Model variations
There are four versions of models released. The details are:
| Model | Backbone | #params | lang | acc | Speed | #Train
|------------|-----------|----------|-------|-------|----|-------------|
| [zero-shot-classify-SSTuning-base](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-base) | [roberta-base](https://huggingface.co/roberta-base) | 125M | En | Low | High | 20.48M |
| [zero-shot-classify-SSTuning-large](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-large) | [roberta-large](https://huggingface.co/roberta-large) | 355M | En | Medium | Medium | 5.12M |
| [zero-shot-classify-SSTuning-ALBERT](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT) | [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) | 235M | En | High | Low| 5.12M |
| [zero-shot-classify-SSTuning-XLM-R](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-XLM-R) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | 278M | Multi | - | - | 20.48M |
Please note that zero-shot-classify-SSTuning-XLM-R is trained with 20.48M English samples only. However, it can also be used in other languages as long as xlm-roberta supports.
Please check [this repository](https://github.com/DAMO-NLP-SG/SSTuning) for the performance of each model.
## Intended uses & limitations
The model can be used for zero-shot text classification such as sentiment analysis and topic classification. No further finetuning is needed.
The number of labels should be 2 ~ 20.
### How to use
You can try the model with the Colab [Notebook](https://colab.research.google.com/drive/17bqc8cXFF-wDmZ0o8j7sbrQB9Cq7Gowr?usp=sharing).
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch, string, random
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base")
model = AutoModelForSequenceClassification.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base")
text = "I love this place! The food is always so fresh and delicious."
list_label = ["negative", "positive"]
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
list_ABC = [x for x in string.ascii_uppercase]
def check_text(model, text, list_label, shuffle=False):
list_label = [x+'.' if x[-1] != '.' else x for x in list_label]
list_label_new = list_label + [tokenizer.pad_token]* (20 - len(list_label))
if shuffle:
random.shuffle(list_label_new)
s_option = ' '.join(['('+list_ABC[i]+') '+list_label_new[i] for i in range(len(list_label_new))])
text = f'{s_option} {tokenizer.sep_token} {text}'
model.to(device).eval()
encoding = tokenizer([text],truncation=True, max_length=512,return_tensors='pt')
item = {key: val.to(device) for key, val in encoding.items()}
logits = model(**item).logits
logits = logits if shuffle else logits[:,0:len(list_label)]
probs = torch.nn.functional.softmax(logits, dim = -1).tolist()
predictions = torch.argmax(logits, dim=-1).item()
probabilities = [round(x,5) for x in probs[0]]
print(f'prediction: {predictions} => ({list_ABC[predictions]}) {list_label_new[predictions]}')
print(f'probability: {round(probabilities[predictions]*100,2)}%')
check_text(model, text, list_label)
# prediction: 1 => (B) positive.
# probability: 99.92%
```
### BibTeX entry and citation info
```bibtxt
@inproceedings{acl23/SSTuning,
author = {Chaoqun Liu and
Wenxuan Zhang and
Guizhen Chen and
Xiaobao Wu and
Anh Tuan Luu and
Chip Hong Chang and
Lidong Bing},
title = {Zero-Shot Text Classification via Self-Supervised Tuning},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
year = {2023},
url = {https://arxiv.org/abs/2305.11442},
}
```
|
samaksh-khatri-crest-data/gmra_model_gpt2_14082023T103028
|
samaksh-khatri-crest-data
| 2023-08-14T05:30:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-14T05:00:28Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gmra_model_gpt2_14082023T103028
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. -->
# gmra_model_gpt2_14082023T103028
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2685
- Accuracy: 0.9192
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 71 | 0.6906 | 0.7742 |
| No log | 1.99 | 142 | 0.4773 | 0.8286 |
| No log | 2.99 | 213 | 0.3916 | 0.8708 |
| No log | 4.0 | 285 | 0.3393 | 0.8849 |
| No log | 5.0 | 356 | 0.3144 | 0.9007 |
| No log | 5.99 | 427 | 0.2959 | 0.9112 |
| No log | 6.99 | 498 | 0.2825 | 0.9165 |
| 0.538 | 8.0 | 570 | 0.2803 | 0.9069 |
| 0.538 | 9.0 | 641 | 0.2612 | 0.9192 |
| 0.538 | 9.96 | 710 | 0.2685 | 0.9192 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0030
|
bigmorning
| 2023-08-14T05:29:51Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:29:43Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0030
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_round3__0030
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.2571
- Validation Loss: 0.5667
- Validation Accuracy: 0.0771
- Validation Wermet: 7.1787
- Epoch: 29
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
| 0.0000 | 0.0795 | 8.2571 | 0.5667 | 0.0771 | 7.1787 | 29 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0029
|
bigmorning
| 2023-08-14T05:25:39Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:25:31Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0029
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_round3__0029
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 8.2573
- Validation Loss: 0.5648
- Validation Accuracy: 0.0771
- Validation Wermet: 7.2086
- Epoch: 28
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
| 0.0000 | 0.0795 | 8.2151 | 0.5614 | 0.0771 | 7.1972 | 26 |
| 0.0000 | 0.0795 | 8.2332 | 0.5633 | 0.0771 | 7.1736 | 27 |
| 0.0000 | 0.0795 | 8.2573 | 0.5648 | 0.0771 | 7.2086 | 28 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Yntec/QToriReloaded
|
Yntec
| 2023-08-14T05:20:14Z | 640 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"agntperseus",
"TkskKurumi",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-28T22:27:49Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- agntperseus
- TkskKurumi
---
# QTori Reloaded
QTori LORA merged in with RMHF 2.5D-V2.
Original pages:
https://civitai.com/models/15179/qtori-style-lora
https://civitai.com/models/101518?modelVersionId=110456
|
nerijs/lego-brickheadz-xl
|
nerijs
| 2023-08-14T05:20:10Z | 536 | 22 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2023-08-14T05:18:21Z |
---
license: apache-2.0
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: lego brickheadz
widget:
- text: picture of a lego brickheadz of a corgi
---
# LEGO Minifig XL
## Consider supporting further research on [Patreon](https://www.patreon.com/user?u=29466374) or [Twitter](https://twitter.com/nerijs)
|
GesonAnko/ppo-Huggy
|
GesonAnko
| 2023-08-14T05:19:38Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-08-14T05:19:28Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: GesonAnko/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
bigmorning/whisper_charsplit_new_round3__0026
|
bigmorning
| 2023-08-14T05:13:16Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T05:13:08Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0026
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_round3__0026
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 8.1494
- Validation Loss: 0.5589
- Validation Accuracy: 0.0771
- Validation Wermet: 7.1609
- Epoch: 25
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
| 0.0004 | 0.0795 | 8.0507 | 0.5619 | 0.0770 | 7.0678 | 22 |
| 0.0003 | 0.0795 | 8.0534 | 0.5593 | 0.0771 | 7.0433 | 23 |
| 0.0002 | 0.0795 | 8.1738 | 0.5604 | 0.0771 | 7.1617 | 24 |
| 0.0001 | 0.0795 | 8.1494 | 0.5589 | 0.0771 | 7.1609 | 25 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
li-ping/plus_summary_0814
|
li-ping
| 2023-08-14T05:08:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T04:23:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0.dev0
|
casque/t3_xx3B09Ver3Prototyp
|
casque
| 2023-08-14T05:02:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-14T04:31:30Z |
---
license: creativeml-openrail-m
---
|
bigmorning/whisper_charsplit_new_round3__0022
|
bigmorning
| 2023-08-14T04:56:26Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:56:08Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0022
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_round3__0022
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0013
- Train Accuracy: 0.0795
- Train Wermet: 8.2537
- Validation Loss: 0.5574
- Validation Accuracy: 0.0770
- Validation Wermet: 6.7708
- Epoch: 21
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
| 0.0013 | 0.0795 | 8.2537 | 0.5574 | 0.0770 | 6.7708 | 21 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0021
|
bigmorning
| 2023-08-14T04:52:07Z | 60 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:51:59Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0021
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_round3__0021
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0115
- Train Accuracy: 0.0793
- Train Wermet: 8.3288
- Validation Loss: 0.5580
- Validation Accuracy: 0.0769
- Validation Wermet: 7.1457
- Epoch: 20
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
| 0.0003 | 0.0795 | 7.6709 | 0.6610 | 0.0762 | 7.0119 | 19 |
| 0.0115 | 0.0793 | 8.3288 | 0.5580 | 0.0769 | 7.1457 | 20 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
muhammadravi251001/fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
|
muhammadravi251001
| 2023-08-14T04:47:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-11T06:40:57Z |
---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-KoreanNLI-KorNLI-with-xlm-roberta-large
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4428
- Accuracy: 0.8439
- F1: 0.8445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4595 | 0.5 | 3654 | 0.4630 | 0.8064 | 0.8089 |
| 0.4138 | 1.0 | 7308 | 0.4497 | 0.8146 | 0.8165 |
| 0.3748 | 1.5 | 10962 | 0.4280 | 0.8420 | 0.8422 |
| 0.3687 | 2.0 | 14616 | 0.4161 | 0.8363 | 0.8376 |
| 0.3265 | 2.5 | 18270 | 0.4209 | 0.8459 | 0.8465 |
| 0.3392 | 3.0 | 21924 | 0.4107 | 0.8459 | 0.8453 |
| 0.2928 | 3.5 | 25578 | 0.4479 | 0.8395 | 0.8401 |
| 0.2975 | 4.0 | 29232 | 0.4428 | 0.8439 | 0.8445 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.13.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|
chriskim2273/IOTNation_Classification_Model_0.75_5K_AND_ORIGINAL_DATASET_BERT
|
chriskim2273
| 2023-08-14T04:44:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-08-13T08:25:38Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: IOTNation_Classification_Model_0.75_5K_AND_ORIGINAL_DATASET_BERT
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. -->
# IOTNation_Classification_Model_0.75_5K_AND_ORIGINAL_DATASET_BERT
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0178
- Accuracy: 0.9958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 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 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0019
|
bigmorning
| 2023-08-14T04:43:40Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:43:33Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0019
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_round3__0019
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.7427
- Validation Loss: 0.5796
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8406
- Epoch: 18
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
| 0.0000 | 0.0795 | 7.7427 | 0.5796 | 0.0771 | 6.8406 | 18 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0018
|
bigmorning
| 2023-08-14T04:39:33Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:39:25Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0018
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_round3__0018
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.7109
- Validation Loss: 0.5784
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8560
- Epoch: 17
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
| 0.0000 | 0.0795 | 7.7745 | 0.5741 | 0.0771 | 6.8770 | 14 |
| 0.0000 | 0.0795 | 7.7277 | 0.5752 | 0.0771 | 6.8671 | 15 |
| 0.0000 | 0.0795 | 7.7355 | 0.5765 | 0.0771 | 6.8447 | 16 |
| 0.0000 | 0.0795 | 7.7109 | 0.5784 | 0.0771 | 6.8560 | 17 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0014
|
bigmorning
| 2023-08-14T04:22:33Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:22:26Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0014
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_round3__0014
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0000
- Train Accuracy: 0.0795
- Train Wermet: 7.8163
- Validation Loss: 0.5721
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8876
- Epoch: 13
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
| 0.0000 | 0.0795 | 7.8163 | 0.5721 | 0.0771 | 6.8876 | 13 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0013
|
bigmorning
| 2023-08-14T04:18:23Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:18:15Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0013
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_round3__0013
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 7.7721
- Validation Loss: 0.5726
- Validation Accuracy: 0.0771
- Validation Wermet: 6.8911
- Epoch: 12
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
| 0.0001 | 0.0795 | 7.7721 | 0.5726 | 0.0771 | 6.8911 | 12 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
bigmorning/whisper_charsplit_new_round3__0012
|
bigmorning
| 2023-08-14T04:14:15Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:14:07Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0012
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_round3__0012
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0001
- Train Accuracy: 0.0795
- Train Wermet: 7.7540
- Validation Loss: 0.5725
- Validation Accuracy: 0.0771
- Validation Wermet: 6.9281
- Epoch: 11
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
| 0.0004 | 0.0795 | 7.3807 | 0.5698 | 0.0770 | 7.0671 | 9 |
| 0.0001 | 0.0795 | 7.7157 | 0.5681 | 0.0771 | 6.8391 | 10 |
| 0.0001 | 0.0795 | 7.7540 | 0.5725 | 0.0771 | 6.9281 | 11 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
Mustain/llama2_7b_formatted_tweeter_dataset
|
Mustain
| 2023-08-14T04:07:23Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-08-14T01:40:16Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0.dev0
|
bigmorning/whisper_charsplit_new_round3__0009
|
bigmorning
| 2023-08-14T04:01:32Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:bigmorning/whisper_charsplit_new_round2__0061",
"base_model:finetune:bigmorning/whisper_charsplit_new_round2__0061",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T04:01:25Z |
---
license: apache-2.0
base_model: bigmorning/whisper_charsplit_new_round2__0061
tags:
- generated_from_keras_callback
model-index:
- name: whisper_charsplit_new_round3__0009
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_round3__0009
This model is a fine-tuned version of [bigmorning/whisper_charsplit_new_round2__0061](https://huggingface.co/bigmorning/whisper_charsplit_new_round2__0061) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0010
- Train Accuracy: 0.0795
- Train Wermet: 7.5822
- Validation Loss: 0.5755
- Validation Accuracy: 0.0769
- Validation Wermet: 6.6613
- Epoch: 8
## 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 |
|:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:|
| 0.0009 | 0.0795 | 7.9492 | 0.5730 | 0.0769 | 7.2856 | 0 |
| 0.0015 | 0.0795 | 8.4221 | 0.5756 | 0.0769 | 7.1487 | 1 |
| 0.0012 | 0.0795 | 7.8476 | 0.5699 | 0.0769 | 6.5976 | 2 |
| 0.0010 | 0.0795 | 7.6843 | 0.5740 | 0.0769 | 6.9513 | 3 |
| 0.0014 | 0.0795 | 8.0796 | 0.5763 | 0.0768 | 7.4043 | 4 |
| 0.0019 | 0.0795 | 7.7274 | 0.5724 | 0.0769 | 6.4922 | 5 |
| 0.0008 | 0.0795 | 7.3468 | 0.5734 | 0.0769 | 6.1909 | 6 |
| 0.0009 | 0.0795 | 7.2393 | 0.5816 | 0.0769 | 6.5734 | 7 |
| 0.0010 | 0.0795 | 7.5822 | 0.5755 | 0.0769 | 6.6613 | 8 |
### Framework versions
- Transformers 4.32.0.dev0
- TensorFlow 2.12.0
- Tokenizers 0.13.3
|
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