modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
StefanoCaloni/taxi | StefanoCaloni | 2023-09-02T07:42:24Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-08-31T06:40:39Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="StefanoCaloni/taxi", 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"])
```
|
barti25/lora_flan-t5-large_cnn_dailymail | barti25 | 2023-09-02T07:36:54Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T07:36:00Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
squarelike/Gugugo-koja-1.3B-V0.95 | squarelike | 2023-09-02T07:31:26Z | 67 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"translation",
"ja",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | translation | 2023-08-31T14:17:12Z | ---
license: apache-2.0
language:
- ja
- ko
pipeline_tag: translation
---
[https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo)
Prompt Template:
```
### 한국어: {sentence}</끝>
### 일본어:
```
```
### 일본어: {sentence}</끝>
### 한국어:
``` |
harshu101202/finetuning-sentiment-model-3000-samples | harshu101202 | 2023-09-02T07:22:08Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-08-30T09:40:02Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8066666666666666
- name: F1
type: f1
value: 0.7986111111111112
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.4250
- Accuracy: 0.8067
- F1: 0.7986
## 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: 0.01
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
xalphaai/llama2-qlora-finetunined | xalphaai | 2023-09-02T07:20:06Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T07:19:49Z | ---
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 0.6.0.dev0
|
trieudemo11/llama_7b_attrb_cate_b6_l320_low_10 | trieudemo11 | 2023-09-02T06:41:20Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T06:41:05Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
rsions
- PEFT 0.6.0.dev0
|
NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEconsE4 | NobodyExistsOnTheInternet | 2023-09-02T06:23:16Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-02T05:20:49Z | ---
license: mit
---
Trained on Math Chain of thought, Chemistry and Physics domain knowledge, and chat
V1 |
gg-ai/twhin-bert-base-p-tuning | gg-ai | 2023-09-02T06:08:04Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T06:08:01Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
budecosystem/genz-70b | budecosystem | 2023-09-02T06:03:21Z | 2,642 | 30 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-08-21T11:36:04Z | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
---
<div align="center"><h1 align="center">~ GenZ ~</h1><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/genz-logo.png" width=150></div>
<p align="center"><i>Democratizing access to LLMs for the open-source community.<br>Let's advance AI, together. </i></p>
---
## Introduction 🎉
Welcome to **GenZ**, an advanced Large Language Model (LLM) fine-tuned on the foundation of Meta's open-source Llama V2 70B parameter model. At Bud Ecosystem, we believe in the power of open-source collaboration to drive the advancement of technology at an accelerated pace. Our vision is to democratize access to fine-tuned LLMs, and to that end, we will be releasing a series of models across different parameter counts (7B, 13B, and 70B) and quantizations (32-bit and 4-bit) for the open-source community to use, enhance, and build upon.
<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_compare.png" width="500"></p>
The smaller quantization version of our models makes them more accessible, enabling their use even on personal computers. This opens up a world of possibilities for developers, researchers, and enthusiasts to experiment with these models and contribute to the collective advancement of language model technology.
GenZ isn't just a powerful text generator—it's a sophisticated AI assistant, capable of understanding and responding to user prompts with high-quality responses. We've taken the robust capabilities of Llama V2 and fine-tuned them to offer a more user-focused experience. Whether you're seeking informative responses or engaging interactions, GenZ is designed to deliver.
And this isn't the end. It's just the beginning of a journey towards creating more advanced, more efficient, and more accessible language models. We invite you to join us on this exciting journey. 🚀
---
<h2>Milestone Releases ️🏁</h2>
**[21 August 2023]**
[_GenZ-70B_](https://huggingface.co/budecosystem/genz-70b) : We're excited to announce the release of our Genz 70BB model. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-70b).
**[27 July 2023]**
[_GenZ-13B V2 (ggml)_](https://huggingface.co/budecosystem/genz-13b-v2-ggml) : Announcing our GenZ-13B v2 with ggml. This variant of GenZ can run inferencing using only CPU and without the need of GPU. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-ggml).
**[27 July 2023]**
[_GenZ-13B V2 (4-bit)_](https://huggingface.co/budecosystem/genz-13b-v2-4bit) : Announcing our GenZ-13B v2 with 4-bit quantisation. Enabling inferencing with much lesser GPU memory than the 32-bit variant. Download the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2-4bit).
**[26 July 2023]**
[_GenZ-13B V2_](https://huggingface.co/budecosystem/genz-13b-v2) : We're excited to announce the release of our Genz 13B v2 model, a step forward with improved evaluation results compared to v1. Experience the advancements by downloading the model from [HuggingFace](https://huggingface.co/budecosystem/genz-13b-v2).
**[20 July 2023]**
[_GenZ-13B_](https://huggingface.co/budecosystem/genz-13b) : We marked an important milestone with the release of the Genz 13B model. The journey began here, and you can partake in it by downloading the model from [Hugging Face](https://huggingface.co/budecosystem/genz-13b).
---
<h2>Evaluations 🎯</h2>
Evaluating our model is a key part of our fine-tuning process. It helps us understand how our model is performing and how it stacks up against other models. Here's a look at some of the key evaluations for GenZ 70B:
<h3>Benchmark Comparison</h3>
We've compared GenZ models to understand the improvements our fine-tuning has achieved.
| Model Name | MT Bench | MMLU | Human Eval | BBH |
|:----------:|:--------:|:----:|:----------:|:----:|
| Genz 13B | 6.12 | 53.62| 17.68 | 37.76|
| Genz 13B v2| 6.79 | 53.68| 21.95 | 38.1 |
| Genz 70B | 7.33 | 70.32| 37.8 |54.69 |
<h3>MT Bench Score</h3>
A key evaluation metric we use is the MT Bench score. This score provides a comprehensive assessment of our model's performance across a range of tasks.
<p align="center"><img src="https://raw.githubusercontent.com/BudEcosystem/GenZ/main/assets/mt_bench_score.png" width="500"></p>
---
<h2>Getting Started on Hugging Face 🤗</h2>
Getting up and running with our models on Hugging Face is a breeze. Follow these steps:
<h3>1️⃣ : Import necessary modules</h3>
Start by importing the necessary modules from the ‘transformers’ library and ‘torch’.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("budecosystem/genz-70b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("budecosystem/genz-70b", torch_dtype=torch.bfloat16, rope_scaling={"type": "dynamic", "factor": 2})
prompt = "### User:\nWrite a python flask code for login management\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```
Want to interact with the model in a more intuitive way? We have a Gradio interface set up for that. Head over to our GitHub page, clone the repository, and run the ‘generate.py’ script to try it out. Happy experimenting! 😄
<h2>Why Use GenZ? 💡</h2>
You might be wondering, "Why should I choose GenZ over a pretrained model?" The answer lies in the extra mile we've gone to fine-tune our models.
While pretrained models are undeniably powerful, GenZ brings something extra to the table. We've fine-tuned it with curated datasets, which means it has additional skills and capabilities beyond what a pretrained model can offer. Whether you need it for a simple task or a complex project, GenZ is up for the challenge.
What's more, we are committed to continuously enhancing GenZ. We believe in the power of constant learning and improvement. That's why we'll be regularly fine-tuning our models with various curated datasets to make them even better. Our goal is to reach the state of the art and beyond - and we're committed to staying the course until we get there.
But don't just take our word for it. We've provided detailed evaluations and performance details in a later section, so you can see the difference for yourself.
Choose GenZ and join us on this journey. Together, we can push the boundaries of what's possible with large language models.
---
<h2>Model Card for GenZ 70B 📄</h2>
Here's a quick overview of everything you need to know about GenZ 70B.
<h3>Model Details:</h3>
- Developed by: Bud Ecosystem
- Base pretrained model type: Llama V2 70B
- Model Architecture: GenZ 70B, fine-tuned on Llama V2 70B, is an auto-regressive language model that employs an optimized transformer architecture. The fine-tuning process for GenZ 70B leveraged Supervised Fine-Tuning (SFT)
- License: The model is available for commercial use under a custom commercial license. For more information, please visit: [Meta AI Model and Library Downloads](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
---
<h2>Intended Use 💼</h2>
When we created GenZ 70B, we had a clear vision of how it could be used to push the boundaries of what's possible with large language models. We also understand the importance of using such models responsibly. Here's a brief overview of the intended and out-of-scope uses for GenZ 70B.
<h3>Direct Use</h3>
GenZ 70B is designed to be a powerful tool for research on large language models. It's also an excellent foundation for further specialization and fine-tuning for specific use cases, such as:
- Text summarization
- Text generation
- Chatbot creation
- And much more!
<h3>Out-of-Scope Use 🚩</h3>
While GenZ 70B is versatile, there are certain uses that are out of scope:
- Production use without adequate assessment of risks and mitigation
- Any use cases which may be considered irresponsible or harmful
- Use in any manner that violates applicable laws or regulations, including trade compliance laws
- Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2
Remember, GenZ 70B, like any large language model, is trained on a large-scale corpora representative of the web, and therefore, may carry the stereotypes and biases commonly encountered online.
<h3>Recommendations 🧠</h3>
We recommend users of GenZ 70B to consider fine-tuning it for the specific set of tasks of interest. Appropriate precautions and guardrails should be taken for any production use. Using GenZ 70B responsibly is key to unlocking its full potential while maintaining a safe and respectful environment.
---
<h2>Training Details 📚</h2>
When fine-tuning GenZ 70B, we took a meticulous approach to ensure we were building on the solid base of the pretrained Llama V2 70B model in the most effective way. Here's a look at the key details of our training process:
<h3>Fine-Tuning Training Data</h3>
For the fine-tuning process, we used a carefully curated mix of datasets. These included data from OpenAssistant, an instruction fine-tuning dataset, and Thought Source for the Chain Of Thought (CoT) approach. This diverse mix of data sources helped us enhance the model's capabilities across a range of tasks.
<h3>Hyperparameters</h3>
Here are the hyperparameters we used for fine-tuning:
| Hyperparameter | Value |
| -------------- | ----- |
| Warmup Ratio | 0.04 |
| Learning Rate Scheduler Type | Cosine |
| Learning Rate | 2e-5 |
| Number of Training Epochs | 3 |
| Per Device Training Batch Size | 4 |
| Gradient Accumulation Steps | 4 |
| Precision | FP16 |
| Optimizer | AdamW |
---
<h2>Looking Ahead 👀</h2>
We're excited about the journey ahead with GenZ. We're committed to continuously improving and enhancing our models, and we're excited to see what the open-source community will build with them. We believe in the power of collaboration, and we can't wait to see what we can achieve together.
Remember, we're just getting started. This is just the beginning of a journey that we believe will revolutionize the world of large language models. We invite you to join us on this exciting journey. Together, we can push the boundaries of what's possible with AI. 🚀
---
Check the GitHub for the code -> [GenZ](https://raw.githubusercontent.com/BudEcosystem/GenZ) |
miaoyh32/roberta-large-peft-p-tuning | miaoyh32 | 2023-09-02T05:46:27Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-08-22T01:47:27Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Hellstar1337/freyaLoRA | Hellstar1337 | 2023-09-02T05:45:06Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T05:41:39Z | ---
license: creativeml-openrail-m
---
|
jmhessel/cosmo-v2-7b | jmhessel | 2023-09-02T05:39:26Z | 3 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T05:39:20Z | ---
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
|
Imxxn/AudioCourseU6-TextToSpeech | Imxxn | 2023-09-02T05:38:00Z | 80 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-02T05:18:20Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: AudioCourseU6-TextToSpeech
results: []
pipeline_tag: text-to-speech
---
<!-- 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. -->
# AudioCourseU6-TextToSpeech
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEcons | NobodyExistsOnTheInternet | 2023-09-02T05:34:07Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-01T15:12:04Z | ---
license: mit
---
Trained on Math Chain of thought, Chemistry and Physics domain knowledge, and chat
V1 |
sushanadhikari/animal_detection | sushanadhikari | 2023-09-02T05:31:02Z | 223 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-09-02T05:30:56Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: animal_detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9909909963607788
---
# animal_detection
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### dog
#### girl
#### gold fish
#### house
 |
substratusai/weaviate-gorilla-v3 | substratusai | 2023-09-02T05:13:22Z | 8 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-01T22:50:07Z | ## Prompt
```
{input}
{output}
```
Example: of entry used for finetuning
```
Your task is to write an API request for a new schema given the API reference and an example. The user command is: "Get me the details of 2 music tracks that are similar to the given vector." Here is the API reference for a query that will help with this command and an example of how to use it: {Get {JeopardyQuestion (limit: 2,nearVector: {vector: [-0.0125526935, -0.021168863, -0.01076519, ...]}}}}} Could you please formulate this query for the following schema? {"class": "Track","description": "A music track.","properties": [{"name": "trackId","dataType": ["uuid"],"description": "A unique identifier for each track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "title","dataType": ["text"],"description": "The title of the track.","moduleConfig": {"text2vec-transformers": {"skip": false,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "duration","dataType": ["int"],"description": "The duration of the track in seconds.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "artist","dataType": ["Artist"],"description": "The artist of the track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}{"name": "album","dataType": ["Album"],"description": "The album of the track.","moduleConfig": {"text2vec-transformers": {"skip": true,"vectorizeClassName": false,"vectorizePropertyName": false}}}} VERY IMPORTANT! Please only output the GraphQL for the query and nothing else!
{ Get { Track ( limit: 2, nearVector: { vector: [-0.0125526935, -0.021168863, -0.01076519, ...] } ) { trackId title duration artist { artistId name } album { albumId title } } }}
``` |
gg-ai/roberta-peft-p-tuning | gg-ai | 2023-09-02T05:05:59Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T05:05:58Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
vita-group/llama-2-7b_sparsegpt_unstructured | vita-group | 2023-09-02T05:02:59Z | 19 | 0 | null | [
"license:mit",
"region:us"
] | null | 2023-09-01T15:05:45Z | ---
license: mit
---
# Compressed LLM Model Zone
The models are prepared by [Visual Informatics Group @ University of Texas at Austin (VITA-group)](https://vita-group.github.io/). Credits to Ajay Jaiswal, Zhenyu Zhang.
License: [MIT License](https://opensource.org/license/mit/)
Setup environment
```shell
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
pip install transformers==4.31.0
pip install accelerate
```
How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = 'llama-2-7b'
comp_method = 'magnitude_unstructured'
comp_degree = 0.2
model_path = f'vita-group/{base_model}_{comp_method}'
model = AutoModelForCausalLM.from_pretrained(
model_path,
revision=f's{comp_degree}',
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')
input_ids = tokenizer('Hello! I am a VITA-compressed-LLM chatbot!', return_tensors='pt').input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
| | Base Model | Model Size | Compression Method | Compression Degree |
|---:|:-------------|:-------------|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| 0 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.1) |
| 1 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.2) |
| 2 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.3) |
| 3 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.5) |
| 4 | Llama-2 | 7b | [magnitude_unstructured](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_magnitude_unstructured/tree/s0.6) |
| 5 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.1) |
| 6 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.2) |
| 7 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.3) |
| 8 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.5) |
| 9 | Llama-2 | 7b | [sparsegpt_unstructured](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_sparsegpt_unstructured/tree/s0.6) |
| 10 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.1](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.1) |
| 11 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.2](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.2) |
| 12 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.3](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.3) |
| 13 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.5](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.5) |
| 14 | Llama-2 | 7b | [wanda_unstructured](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured) | [s0.6](https://huggingface.co/vita-group/llama-2-7b_wanda_unstructured/tree/s0.6) |
|
xiaoygv/xiaos | xiaoygv | 2023-09-02T04:56:48Z | 0 | 0 | asteroid | [
"asteroid",
"dataset:PygmalionAI/PIPPA",
"license:afl-3.0",
"region:us"
] | null | 2023-09-02T04:55:25Z | ---
license: afl-3.0
datasets:
- PygmalionAI/PIPPA
metrics:
- bleu
library_name: asteroid
--- |
minh21/results | minh21 | 2023-09-02T04:56:45Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"license:apache-2.0",
"region:us"
] | null | 2023-09-01T07:33:03Z | ---
license: apache-2.0
base_model: google/flan-t5-large
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 860 | nan |
| 0.0 | 2.0 | 1720 | nan |
| 0.0 | 3.0 | 2580 | nan |
| 0.0 | 4.0 | 3440 | nan |
| 0.0 | 5.0 | 4300 | nan |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Alexshan/Dreamshaper | Alexshan | 2023-09-02T04:44:16Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T04:36:41Z | ---
license: creativeml-openrail-m
---
|
cfchase/stable-diffusion-rhteddy | cfchase | 2023-09-02T04:30:11Z | 3 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-21T02:50:44Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
---
# Red Hat Teddy
## Fine Tuned from Stable Diffusion v1-5
This model was based on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and finetuned to generate pictures of `rhteddy`.
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "cfchase/stable-diffusion-rhteddy"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of rhteddy on the beach"
image = pipe(prompt).images[0]
image
```
|
Imxxn/AudioCourseU4-MusicClassification | Imxxn | 2023-09-02T04:21:50Z | 162 | 0 | transformers | [
"transformers",
"pytorch",
"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 | 2023-09-02T01:42:30Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: AudioCourseU4-MusicClassification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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. -->
# AudioCourseU4-MusicClassification
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: 0.8804
- Accuracy: 0.88
## 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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7993 | 1.0 | 225 | 1.5770 | 0.4 |
| 1.0767 | 2.0 | 450 | 0.9900 | 0.7 |
| 0.8292 | 3.0 | 675 | 0.8554 | 0.73 |
| 0.5892 | 4.0 | 900 | 0.8991 | 0.74 |
| 0.1584 | 5.0 | 1125 | 0.8473 | 0.78 |
| 0.0082 | 6.0 | 1350 | 0.9282 | 0.8 |
| 0.0094 | 7.0 | 1575 | 1.0036 | 0.82 |
| 0.0581 | 8.0 | 1800 | 1.2186 | 0.82 |
| 0.0021 | 9.0 | 2025 | 1.0192 | 0.83 |
| 0.0011 | 10.0 | 2250 | 0.8804 | 0.88 |
| 0.002 | 11.0 | 2475 | 1.1519 | 0.83 |
| 0.0009 | 12.0 | 2700 | 0.9439 | 0.87 |
| 0.0006 | 13.0 | 2925 | 1.1227 | 0.84 |
| 0.0008 | 14.0 | 3150 | 1.0344 | 0.86 |
| 0.0006 | 15.0 | 3375 | 1.0209 | 0.86 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ezzzz22/ModelFF1 | ezzzz22 | 2023-09-02T04:09:39Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-02T04:08:56Z | ---
license: creativeml-openrail-m
---
|
Imxxn/AudioCourseU5-ASR | Imxxn | 2023-09-02T03:55:25Z | 78 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-09-02T02:53:46Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: AudioCourseU5-ASR
results: []
datasets:
- PolyAI/minds14
---
<!-- 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. -->
# AudioCourseU5-ASR
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6438
- Wer Ortho: 34.4849
- Wer: 0.3406
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.3065 | 3.57 | 100 | 0.4921 | 36.8908 | 0.3577 |
| 0.0391 | 7.14 | 200 | 0.5425 | 35.3486 | 0.3436 |
| 0.0042 | 10.71 | 300 | 0.5878 | 35.6570 | 0.3495 |
| 0.0012 | 14.29 | 400 | 0.6206 | 34.2998 | 0.3377 |
| 0.0007 | 17.86 | 500 | 0.6438 | 34.4849 | 0.3406 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
gdhdp/xiao | gdhdp | 2023-09-02T03:52:50Z | 0 | 0 | diffusers | [
"diffusers",
"dataset:Open-Orca/OpenOrca",
"arxiv:1910.09700",
"license:openrail",
"region:us"
] | null | 2023-09-02T03:50:57Z | ---
license: openrail
datasets:
- Open-Orca/OpenOrca
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JuanMa360/text-in-image-detection | JuanMa360 | 2023-09-02T03:42:51Z | 198 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-09-01T23:09:34Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: text-in-image-detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8805969953536987
---
# text-in-image-detection
Text in image classification model
## Example Images
#### Exterior
#### Interior
#### image_with_text
 |
wangrongsheng/Baichuan-13B-Chat-sft-merge | wangrongsheng | 2023-09-02T03:38:05Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T03:36:26Z | ---
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: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_walker2d_level-0209_0306-33 | dt-and-vanilla-ardt | 2023-09-02T03:36:52Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T02:08:08Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_walker2d_level-0209_0306-33
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. -->
# ardt-vanilla-robust_train_walker2d_level-0209_0306-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
unggulP/unggul | unggulP | 2023-09-02T03:19:04Z | 0 | 0 | keras | [
"keras",
"id",
"license:openrail",
"region:us"
] | null | 2023-09-02T03:16:12Z | ---
license: openrail
language:
- id
library_name: keras
--- |
ardt-multipart/ardt-multipart-robust_train_walker2d_level-0209_0140-99 | ardt-multipart | 2023-09-02T02:43:07Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T00:42:39Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_walker2d_level-0209_0140-99
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. -->
# ardt-multipart-robust_train_walker2d_level-0209_0140-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_hopper_level-0209_0253-66 | dt-and-vanilla-ardt | 2023-09-02T02:33:57Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T01:54:54Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_hopper_level-0209_0253-66
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. -->
# ardt-vanilla-robust_train_hopper_level-0209_0253-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0209_0046-99 | ardt-multipart | 2023-09-02T02:02:23Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T23:48:11Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0209_0046-99
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. -->
# ardt-multipart-robust_train_halfcheetah_level-0209_0046-99
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dt-and-vanilla-ardt/ardt-vanilla-robust_train_hopper_level-0209_0214-33 | dt-and-vanilla-ardt | 2023-09-02T01:53:31Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-02T01:15:15Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-vanilla-robust_train_hopper_level-0209_0214-33
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. -->
# ardt-vanilla-robust_train_hopper_level-0209_0214-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Laly/intel_image_classification_fastai | Laly | 2023-09-02T01:47:48Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:47:44Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
baibaibai/baini_VoiceBank | baibaibai | 2023-09-02T01:44:46Z | 0 | 0 | null | [
"UTAU",
"diffsinger",
"ja",
"zh",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-09-01T13:36:24Z | ---
license: cc-by-nc-4.0
language:
- ja
- zh
tags:
- UTAU
- diffsinger
---
你好感谢您使用白溺的歌声数据库。
使用规约：
1.禁止用于 宗教 政治，等等，任何违反法律内容的创作。
2.禁止将本音源使用于大众雷点相关内容的创作（例如：为有不良行为的人或物进行二创）
3.允许用于非商业用途且不违反规约的创作。
4.用于商业用途或者盈利，需要向音源管理者申请授权。一般来说都是免费的。
5.使用本音源，不论是商业还是非商，不论是主唱还是和声等职位，都应清晰明了的标注声库名以及所属位置（例：和声：白溺)
6.本音源的oto文件以及wav名，wav文件，引擎模型文件等等，这类由本音源配布的内容以及配布内容二次生成的物品，未说明允许二次配布的，都视为不允许二次配布，也不允许用于违反此规约的创作。（注：使用本音源所配布的内容，进行ai或者其他的类似程序的训练学习等等，或其他近似的行为。属于对已配布内容的二次生成物，不允许以任何形式的分发）
7.更不允许，将此音源与其他音源二次拼接起来命名为新的音源。也不允许制作该音源的亚种，如果有亚种需求，请以音色的名义发布（例如：白溺·秋风）。不允许对音源的文件进行二次修改后以新的命名发布。
需要违反规约的事情，或者规约没有写明白的事情，请咨询声库管理者，声库管理者拥有本规约的最终解释权。
歌手基础信息：
歌手信息：
姓名：白溺
性别：男
生日：7月20日
中之人：小白菌（小雨天）
声库管理者/版权归属：小白菌菌 https://space.bilibili.com/207917768
通过邮箱联系我：[email protected]（我可能只是偶尔看一眼） |
javidjamae/autotrain-movie-sentiment-86557143111 | javidjamae | 2023-09-02T01:44:36Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:javidjamae/autotrain-data-movie-sentiment",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T01:43:36Z | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- javidjamae/autotrain-data-movie-sentiment
co2_eq_emissions:
emissions: 0.0061768979977510595
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 86557143111
- CO2 Emissions (in grams): 0.0062
## Validation Metrics
- Loss: 0.736
- Accuracy: 0.747
- Precision: 0.669
- Recall: 0.993
- AUC: 0.932
- F1: 0.800
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/javidjamae/autotrain-movie-sentiment-86557143111
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("javidjamae/autotrain-movie-sentiment-86557143111", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("javidjamae/autotrain-movie-sentiment-86557143111", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
felipelondo/intel_image_classification_fastai | felipelondo | 2023-09-02T01:42:19Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:42:14Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Dagaviri/intel_image_classification_fastai | Dagaviri | 2023-09-02T01:33:22Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:33:18Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Jdex01/intel_image_classification_fastai | Jdex01 | 2023-09-02T01:31:58Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:31:54Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
justina/full-review-clf | justina | 2023-09-02T01:31:48Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:justina/yelp_boba_reviews",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T00:34:20Z | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: full-review-clf
results: []
datasets:
- justina/yelp_boba_reviews
---
<!-- 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. -->
# full-review-clf
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on
[justina/yelp-boba-reviews](https://huggingface.co/datasets/justina/yelp_boba_reviews) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8198
- F1 Macro: 0.6358
- Aucpr Macro: 0.6658
- Accuracy: 0.7185
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Aucpr Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|
| 0.723 | 0.43 | 500 | 0.7576 | 0.5979 | 0.6652 | 0.6831 |
| 0.7307 | 0.87 | 1000 | 0.6862 | 0.6368 | 0.6752 | 0.7185 |
| 0.5828 | 1.3 | 1500 | 0.7398 | 0.6439 | 0.6661 | 0.7255 |
| 0.6236 | 1.73 | 2000 | 0.7878 | 0.6212 | 0.6690 | 0.7069 |
| 0.3739 | 2.16 | 2500 | 0.8138 | 0.6447 | 0.6752 | 0.7170 |
| 0.4235 | 2.6 | 3000 | 0.8048 | 0.6490 | 0.6673 | 0.7255 |
| 0.3684 | 3.03 | 3500 | 0.9615 | 0.6483 | 0.6715 | 0.7205 |
| 0.3243 | 3.46 | 4000 | 1.0931 | 0.6432 | 0.6632 | 0.7235 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
AshtakaOOf/itpiki | AshtakaOOf | 2023-09-02T01:30:11Z | 0 | 1 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-06-13T20:31:18Z | ---
license: cc-by-nc-sa-4.0
---
# MOVED HERE: [AshtakaOOf/ash-networks](https://huggingface.co/AshtakaOOf/ash-networks) |
jhohann/intel_image_classification_fastai | jhohann | 2023-09-02T01:29:34Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:29:20Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
dfelorza/intel_image_classification_fastai | dfelorza | 2023-09-02T01:28:28Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:28:24Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
JmGarzonv/intel_image_classification_fastai | JmGarzonv | 2023-09-02T01:27:35Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:27:24Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
DavidOsorio/intel_image_classification_fastai | DavidOsorio | 2023-09-02T01:26:54Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-09-02T01:26:50Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
justina/undersampled-review-clf | justina | 2023-09-02T01:20:18Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:justina/yelp_boba_reviews",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-02T00:44:50Z | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: undersampled-review-clf
results: []
datasets:
- justina/yelp_boba_reviews
---
<!-- 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. -->
# undersampled-review-clf
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on
[justina/yelp-boba-reviews](https://huggingface.co/datasets/justina/yelp_boba_reviews) dataset. Undersampling techniques were used to optimize the model for predicting
Yelp review ratings.
It achieves the following results on the evaluation set:
- Loss: 0.4412
- F1 Macro: 0.7799
- Aucpr Macro: 0.8286
- Accuracy: 0.8464
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Aucpr Macro | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|
| 0.9348 | 1.22 | 100 | 0.7286 | 0.6132 | 0.6244 | 0.6962 |
| 0.7438 | 2.44 | 200 | 0.7857 | 0.6232 | 0.6215 | 0.6735 |
| 0.6275 | 3.66 | 300 | 0.8317 | 0.5976 | 0.6092 | 0.6778 |
| 0.5561 | 4.88 | 400 | 0.8176 | 0.6200 | 0.6238 | 0.6868 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3 |
nightdude/config_8119 | nightdude | 2023-09-02T00:53:19Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:52:13Z | ---
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
|
peppe243439/my_awesome_model | peppe243439 | 2023-09-02T00:50:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"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"
] | text-classification | 2023-09-02T00:32:27Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_walker2d_level-0109_2337-66 | ardt-multipart | 2023-09-02T00:39:59Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T22:39:37Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_walker2d_level-0109_2337-66
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. -->
# ardt-multipart-robust_train_walker2d_level-0109_2337-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
placeholdereet/Lfgc | placeholdereet | 2023-09-02T00:39:18Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-08-14T15:23:18Z | ---
license: creativeml-openrail-m
---
|
numinousloop/ppo-Huggy | numinousloop | 2023-09-02T00:25:44Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-09-02T00:25:33Z | ---
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: numinousloop/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
KingKazma/xsum_t5-small_lora_500_4_150_8_e4_s6789_v4_l4_r4 | KingKazma | 2023-09-02T00:23:16Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:14:47Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_4_150_8_e2_s6789_v4_l4_r4 | KingKazma | 2023-09-02T00:23:01Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:14:32Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_4_150_8_e-1_s6789_v4_l4_r4_manual | KingKazma | 2023-09-02T00:15:03Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-02T00:15:02Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
stfamod/fine-tuned-bert-financial-sentiment-analysis | stfamod | 2023-09-02T00:06:19Z | 124 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment",
"sentiment-analysis",
"financial",
"fine-tuned",
"fine-tuned-bert",
"bert-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T23:06:32Z | ---
license: mit
tags:
- sentiment
- sentiment-analysis
- financial
- fine-tuned
- fine-tuned-bert
- bert-uncased
---
### Model Overview:
This NLP model is fine-tuned with a focus on analyzing sentiment in financial text and news headlines. It was fine-tuned using the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on the [financial_phrasebank](https://huggingface.co/datasets/financial_phrasebank) and [auditor_sentiment](https://huggingface.co/datasets/FinanceInc/auditor_sentiment) datasets.
**Accuracies:** \
**financial_phrasebank:** 0.993\
**auditor_senitment:** 0.974
### Training Hyperparameters:
**Learning Rate:** 2e-05\
**Train Batch Size:** 16\
**Eval Batch Size:** 16\
**Random Seed:** 42\
**Optimizer:** AdamW-betas(0.9, 0.999)\
**Learning Rate Scheduler:** Linear\
**Number of Epochs:** 6\
**Number of Warmup Steps:** 0.2 * Number of Training Steps
### How To Use:
```
from transformers import pipeline
pipe = pipeline("sentiment-analysis", model="mstafam/fine-tuned-bert-financial-sentimental-analysis")
text = "Example company has seen a 5% increase in revenue this quarter."
print(pipe(text))
[{'label': 'Positive', 'score': 0.9993795156478882}]
``` |
asrulsibaoel/donut-base-stnk-no-address-v3 | asrulsibaoel | 2023-09-02T00:01:36Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:squantumengine/donut-base-stnk-no-address-v2",
"base_model:finetune:squantumengine/donut-base-stnk-no-address-v2",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-09-01T11:34:20Z | ---
base_model: squantumengine/donut-base-stnk-no-address-v2
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-stnk-no-address-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-stnk-no-address-v3
This model is a fine-tuned version of [squantumengine/donut-base-stnk-no-address-v2](https://huggingface.co/squantumengine/donut-base-stnk-no-address-v2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2116
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4954 | 1.0 | 3253 | 0.3898 |
| 0.3524 | 2.0 | 6506 | 0.2783 |
| 0.2498 | 3.0 | 9759 | 0.2342 |
| 0.1684 | 4.0 | 13012 | 0.2177 |
| 0.1442 | 5.0 | 16265 | 0.2116 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.13.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0109_2225-66 | ardt-multipart | 2023-09-01T23:46:16Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T21:27:13Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0109_2225-66
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. -->
# ardt-multipart-robust_train_halfcheetah_level-0109_2225-66
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
frankkuete/electra-large-cuad-qa | frankkuete | 2023-09-01T23:40:06Z | 114 | 1 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"legal",
"en",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-06-02T15:09:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
- legal
datasets:
- cuad
model-index:
- name: electra-large
results: []
language:
- en
widget:
- text: "Highlight the parts (if any) of this contract related to 'Document Name' that should be reviewed by a lawyer. Details: The name of the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (“Agreement”), effective as of December 28, 2014 (“Effective Date”), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (“Aquarius”), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (“Client”). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (“SOW”), incorporated herein by reference (such services are collectively referred to as “Services”). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (“Out-of-Scope Assignments”). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Client’s or Aquarius’s interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (“Subcontractors”) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquarius’s negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (“Preferred Suppliers”) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquarius’s obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, “Materials”). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Client’s Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (“Client Affiliate”), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
- text: "Highlight the parts (if any) of this contract related to 'Agreement Date' that should be reviewed by a lawyer. Details: The date of the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (“Agreement”), effective as of December 28, 2014 (“Effective Date”), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (“Aquarius”), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (“Client”). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (“SOW”), incorporated herein by reference (such services are collectively referred to as “Services”). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (“Out-of-Scope Assignments”). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Client’s or Aquarius’s interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (“Subcontractors”) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquarius’s negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (“Preferred Suppliers”) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquarius’s obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, “Materials”). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Client’s Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (“Client Affiliate”), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
- text: "Highlight the parts (if any) of this contract related to 'Parties' that should be reviewed by a lawyer. Details: The two or more parties who signed the contract"
context: "AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES \n This AGREEMENT FOR MARKETING AND BRAND DEVELOPMENT SERVICES (“Agreement”), effective as of December 28, 2014 (“Effective Date”), is by and between Aquarius Cannabis Inc, a Nevada C-Corporation, with offices located at 2214 Clarendon Street, Suite 230, Woodland Hills, CA 91367 (“Aquarius”), and Sysco Pancho LLC, a Washington limited liability company, with offices located at 6262 cambell rd peshastin wa 98847 (“Client”). 1.Marketing and Brand Development Services. Aquarius will perform services for Client in connection with the planning, provision, creation and/or placing of branding, research, advertising, marketing, consulting, creative and/or digital services for Client, during the Term, as provided in the attached (Attachment A) Statement of Work (“SOW”), incorporated herein by reference (such services are collectively referred to as “Services”). During the term of this agreement, Client may wish to assign additional projects, products, or services to Aquarius beyond the Services outlined in the SOW (“Out-of-Scope Assignments”). Aquarius agrees to accept such Out-of-Scope Assignments only upon a separate written agreement with Client regarding additional compensation to be paid to Aquarius and other relevant terms and conditions. Nothing in this Agreement will be deemed to require Aquarius to undertake any act or perform any services which in its good faith judgment would be misleading, false, libelous, unlawful, in breach of a contract, or otherwise prejudicial to Client’s or Aquarius’s interests. 2.Subcontractors. Client acknowledges that Aquarius may, in the rendition of the Services hereunder, engage third party suppliers and other vendors and subcontractors (“Subcontractors”) from time to time to provide certain services. Aquarius shall supervise such services and endeavor to guard against any loss to Client as the result of the failure of Subcontractors to properly execute their commitments, but Aquarius shall not be responsible for their failure, acts or omissions, except where such failure, acts or omissions are due to Aquarius’s negligence or willful misconduct. If Client enters into arrangements with third party vendors, subcontractors or suppliers regarding the provision of materials or services (“Preferred Suppliers”) and requests that Aquarius utilize such Preferred Suppliers in the discharge of Aquarius’s obligations hereunder, Client remains solely responsible for such Preferred Suppliers. 3.Client Approval of Materials. Aquarius shall submit to Client for its approval all elements of any materials to be produced or placed hereunder, including, but not limited to, all copy, layouts, slogans, websites artworks, graphic materials, and photography (collectively, “Materials”). Submission for prior approval of Materials will not be required to the extent that they are preliminary only. 4.Services to Client’s Designees. Should Client request Aquarius to make purchases for or render services to any parent, subsidiary, or affiliate of Client (“Client Affiliate”), Client and such Client Affiliate shall be jointly and severally liable to Aquarius even though Aquarius may render invoices to, or in the name of, such Client Affiliate."
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-large
This model is a fine-tuned version of [google/electra-large-discriminator](https://huggingface.co/google/electra-large-discriminator) on the cuad dataset for the question-answering task.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Contract Understanding Atticus Dataset (CUAD) is an extractive question-answering dataset on legal contracts proposed by the
Atticus Project, a non-profit organization of legal experts, designed with the help of many experts in the legal field.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2 |
jaober/SpaceInvadersNoFrameskip-v4 | jaober | 2023-09-01T23:27:33Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-09-01T23:27:02Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 245.50 +/- 21.85
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jaober -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jaober -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jaober
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 200000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
mihirtw/med-train-llama | mihirtw | 2023-09-01T23:17:57Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-01T21:43:00Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: true
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [h2oai/h2ogpt-4096-llama2-7b](https://huggingface.co/h2oai/h2ogpt-4096-llama2-7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.31.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="mihirtw/med-train-llama",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"mihirtw/med-train-llama",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"mihirtw/med-train-llama",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "mihirtw/med-train-llama" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
C57Box/bert-finetuned-squad | C57Box | 2023-09-01T23:06:55Z | 116 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-09-01T20:52:03Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
AltamashAhmed/TTS_speecht5_finetuned_voxpopuli_it | AltamashAhmed | 2023-09-01T23:03:49Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"it",
"dataset:facebook/voxpopuli",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-08-31T18:27:02Z | ---
language:
- it
base_model: SpeechT5
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: microsoft/speecht5_tts
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. -->
# microsoft/speecht5_tts
This model is a fine-tuned version of [SpeechT5](https://huggingface.co/SpeechT5) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4873
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5472 | 6.13 | 1000 | 0.5091 |
| 0.5229 | 12.26 | 2000 | 0.4946 |
| 0.5122 | 18.39 | 3000 | 0.4898 |
| 0.5159 | 24.52 | 4000 | 0.4889 |
| 0.511 | 30.65 | 5000 | 0.4873 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Whybother/version-3 | Whybother | 2023-09-01T22:58:56Z | 47 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T22:56:11Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Version_3 Dreambooth model trained by Whybother 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:
|
U-sama/wav2vec2-base-demo-colab | U-sama | 2023-09-01T22:49:04Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-09-01T20:37:29Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4908
- eval_wer: 0.3950
- eval_runtime: 62.3995
- eval_samples_per_second: 26.923
- eval_steps_per_second: 3.365
- epoch: 12.0
- step: 1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Whybother/private | Whybother | 2023-09-01T22:23:34Z | 1 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T22:19:37Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Private() Dreambooth model trained by Whybother 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:
|
jquigonq/results | jquigonq | 2023-09-01T21:44:05Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-09-01T21:43:47Z | ---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Yaxin1992/codellama-13b-multi-3500 | Yaxin1992 | 2023-09-01T21:28:14Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:codellama/CodeLlama-34b-hf",
"base_model:finetune:codellama/CodeLlama-34b-hf",
"license:llama2",
"region:us"
] | null | 2023-08-31T18:01:45Z | ---
license: llama2
base_model: codellama/CodeLlama-34b-hf
tags:
- generated_from_trainer
model-index:
- name: codellama-13b-multi-3500
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. -->
# codellama-13b-multi-3500
This model is a fine-tuned version of [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_halfcheetah_level-0109_2000-33 | ardt-multipart | 2023-09-01T21:25:13Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:01:43Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_halfcheetah_level-0109_2000-33
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. -->
# ardt-multipart-robust_train_halfcheetah_level-0109_2000-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dreamboat26/bert-finetuned-ner | dreamboat26 | 2023-09-01T21:20:54Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"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"
] | token-classification | 2023-09-01T20:47:01Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: dreamboat26/bert-finetuned-ner
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. -->
# dreamboat26/bert-finetuned-ner
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: 0.0219
- Validation Loss: 0.0516
- Epoch: 2
## Model description
Find the entities (such as persons, locations, or organizations) in a sentence. This can be formulated as attributing a label to each token by having one class per entity and one class for “no entity.”
## Intended uses & limitations
Academic Use
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0216 | 0.0516 | 0 |
| 0.0222 | 0.0516 | 1 |
| 0.0219 | 0.0516 | 2 |
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3
|
cmvgia/loratest2 | cmvgia | 2023-09-01T20:37:01Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T20:36:59Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
Ajani/lesson-summarization | Ajani | 2023-09-01T20:14:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-05-23T17:25:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: lesson-summarization
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. -->
# lesson-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0801
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.8198 | 3.12 | 200 | 2.8048 |
| 2.5358 | 6.25 | 400 | 2.6645 |
| 2.333 | 9.38 | 600 | 2.6123 |
| 2.2096 | 12.5 | 800 | 2.5807 |
| 2.0783 | 15.62 | 1000 | 2.5703 |
| 1.9919 | 18.75 | 1200 | 2.5653 |
| 1.89 | 21.88 | 1400 | 2.5602 |
| 1.7865 | 25.0 | 1600 | 2.5650 |
| 1.7149 | 28.12 | 1800 | 2.5812 |
| 1.6651 | 31.25 | 2000 | 2.5813 |
| 1.5662 | 34.38 | 2200 | 2.5997 |
| 1.5333 | 37.5 | 2400 | 2.6097 |
| 1.4336 | 40.62 | 2600 | 2.6389 |
| 1.3986 | 43.75 | 2800 | 2.6564 |
| 1.352 | 46.88 | 3000 | 2.6720 |
| 1.3072 | 50.0 | 3200 | 2.6863 |
| 1.2773 | 53.12 | 3400 | 2.6931 |
| 1.2079 | 56.25 | 3600 | 2.7350 |
| 1.1768 | 59.38 | 3800 | 2.7521 |
| 1.1749 | 62.5 | 4000 | 2.7553 |
| 1.0857 | 65.62 | 4200 | 2.7921 |
| 1.0883 | 68.75 | 4400 | 2.7840 |
| 1.0307 | 71.88 | 4600 | 2.8110 |
| 1.0255 | 75.0 | 4800 | 2.8365 |
| 0.9992 | 78.12 | 5000 | 2.8358 |
| 0.9516 | 81.25 | 5200 | 2.8554 |
| 0.9363 | 84.38 | 5400 | 2.8742 |
| 0.91 | 87.5 | 5600 | 2.8923 |
| 0.895 | 90.62 | 5800 | 2.9057 |
| 0.8371 | 93.75 | 6000 | 2.9234 |
| 0.8588 | 96.88 | 6200 | 2.9443 |
| 0.8237 | 100.0 | 6400 | 2.9612 |
| 0.8147 | 103.12 | 6600 | 2.9633 |
| 0.7936 | 106.25 | 6800 | 2.9641 |
| 0.7883 | 109.38 | 7000 | 2.9711 |
| 0.7589 | 112.5 | 7200 | 2.9744 |
| 0.7277 | 115.62 | 7400 | 2.9879 |
| 0.7505 | 118.75 | 7600 | 2.9974 |
| 0.705 | 121.88 | 7800 | 3.0033 |
| 0.7111 | 125.0 | 8000 | 3.0032 |
| 0.7005 | 128.12 | 8200 | 3.0055 |
| 0.6961 | 131.25 | 8400 | 3.0168 |
| 0.6543 | 134.38 | 8600 | 3.0339 |
| 0.6482 | 137.5 | 8800 | 3.0312 |
| 0.6807 | 140.62 | 9000 | 3.0393 |
| 0.6365 | 143.75 | 9200 | 3.0413 |
| 0.648 | 146.88 | 9400 | 3.0461 |
| 0.6275 | 150.0 | 9600 | 3.0454 |
| 0.6284 | 153.12 | 9800 | 3.0552 |
| 0.6062 | 156.25 | 10000 | 3.0514 |
| 0.6312 | 159.38 | 10200 | 3.0487 |
| 0.6244 | 162.5 | 10400 | 3.0525 |
| 0.5792 | 165.62 | 10600 | 3.0547 |
| 0.5997 | 168.75 | 10800 | 3.0491 |
| 0.5972 | 171.88 | 11000 | 3.0542 |
| 0.5891 | 175.0 | 11200 | 3.0624 |
| 0.582 | 178.12 | 11400 | 3.0717 |
| 0.5934 | 181.25 | 11600 | 3.0683 |
| 0.5803 | 184.38 | 11800 | 3.0761 |
| 0.5724 | 187.5 | 12000 | 3.0777 |
| 0.6015 | 190.62 | 12200 | 3.0784 |
| 0.5874 | 193.75 | 12400 | 3.0792 |
| 0.5531 | 196.88 | 12600 | 3.0801 |
| 0.5863 | 200.0 | 12800 | 3.0801 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.12.0
- Tokenizers 0.13.3
|
LarryAIDraw/sinon | LarryAIDraw | 2023-09-01T20:12:40Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-01T19:52:54Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/51757/sinon-sword-art-online |
trieudemo11/llama_7b_attrb_cate_8m_1 | trieudemo11 | 2023-09-01T20:12:01Z | 5 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T20:11:47Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
Frorozcol/speecht5_finetuned_voxpopuli_nl | Frorozcol | 2023-09-01T20:10:46Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-08-31T16:01:04Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4622 | 105.26 | 1000 | 0.4629 |
| 0.4376 | 210.53 | 2000 | 0.4654 |
| 0.4274 | 315.79 | 3000 | 0.4635 |
| 0.422 | 421.05 | 4000 | 0.4642 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
anik424/SD_xl_base_madras_checks | anik424 | 2023-09-01T20:06:23Z | 1 | 2 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2023-08-30T18:11:49Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Photo of madras check pattern"
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
|
dreamboat26/Fine_Tuning_Check | dreamboat26 | 2023-09-01T19:57:35Z | 0 | 0 | null | [
"text-classification",
"en",
"dataset:carlosejimenez/mrpc_corpus",
"license:afl-3.0",
"region:us"
] | text-classification | 2023-09-01T19:51:42Z | ---
license: afl-3.0
datasets:
- carlosejimenez/mrpc_corpus
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: text-classification
---
# What I did:-
Learned about datasets in the Hub
Learned how to load and preprocess datasets
Learned how to fine-tune and evaluate a model with Keras
Implemented a custom metric
|
Kamer/DuplicatiDistillBertCitations | Kamer | 2023-09-01T19:56:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T19:28:02Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: DuplicatiDistillBertCitations
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. -->
# DuplicatiDistillBertCitations
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5474
- eval_Accuracy: 0.8571
- eval_F1_macro: 0.8668
- eval_F1_class_0: 0.7476
- eval_F1_class_1: 0.8627
- eval_F1_class_2: 0.8975
- eval_F1_class_3: 0.9362
- eval_F1_class_4: 0.9415
- eval_F1_class_5: 0.9176
- eval_F1_class_6: 0.8864
- eval_F1_class_7: 0.9548
- eval_F1_class_8: 0.9196
- eval_F1_class_9: 0.9424
- eval_F1_class_10: 0.6921
- eval_F1_class_11: 0.3927
- eval_F1_class_12: 0.8407
- eval_F1_class_13: 0.9495
- eval_F1_class_14: 0.8884
- eval_F1_class_15: 0.8514
- eval_F1_class_16: 0.8750
- eval_F1_class_17: 0.9115
- eval_F1_class_18: 0.9647
- eval_F1_class_19: 0.9630
- eval_runtime: 30.4778
- eval_samples_per_second: 166.646
- eval_steps_per_second: 20.835
- epoch: 1.33
- step: 7681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
KingKazma/xsum_t5-small_lora_500_2_300_8_e-1_s6789_v4_l4_r4_manual | KingKazma | 2023-09-01T19:55:11Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:50:04Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_2_300_8_e2_s6789_v4_l4_r4 | KingKazma | 2023-09-01T19:55:08Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:50:00Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KingKazma/xsum_t5-small_lora_500_2_300_8_e1_s6789_v4_l4_r4 | KingKazma | 2023-09-01T19:54:55Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:45:45Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
cmvgia/loratest | cmvgia | 2023-09-01T19:54:09Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-09-01T19:54:07Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
KatMarie/wav2vec2-large-xls-r-300m-eu | KatMarie | 2023-09-01T19:47:42Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-08-30T20:11:45Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-eu
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 0.4967706508380619
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-eu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
- Wer: 0.4968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5405 | 0.85 | 400 | 0.8115 | 0.8949 |
| 0.5355 | 1.7 | 800 | 0.6292 | 0.7331 |
| 0.405 | 2.56 | 1200 | 0.5805 | 0.6699 |
| 0.3261 | 3.41 | 1600 | 0.5308 | 0.6513 |
| 0.2496 | 4.26 | 2000 | 0.4755 | 0.5850 |
| 0.1878 | 5.11 | 2400 | 0.4926 | 0.5448 |
| 0.1342 | 5.96 | 2800 | 0.4355 | 0.4968 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ardt-multipart/ardt-multipart-robust_train_hopper_level-0109_2000-33 | ardt-multipart | 2023-09-01T19:41:40Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"decision_transformer",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T19:01:16Z | ---
tags:
- generated_from_trainer
model-index:
- name: ardt-multipart-robust_train_hopper_level-0109_2000-33
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. -->
# ardt-multipart-robust_train_hopper_level-0109_2000-33
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.1.0.dev20230727+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
swl-models/Yorunohitsuji-v1.1 | swl-models | 2023-09-01T19:32:15Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-01T16:18:36Z | ---
license: creativeml-openrail-m
---
|
Kamer/DuplicatiDistillBert | Kamer | 2023-09-01T19:13:08Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T17:07:46Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: DuplicatiDistillBert
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. -->
# DuplicatiDistillBert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5776
- eval_Accuracy: 0.8765
- eval_F1_macro: 0.8702
- eval_F1_class_0: 0.8768
- eval_F1_class_1: 0.7797
- eval_F1_class_2: 0.9077
- eval_F1_class_3: 0.9000
- eval_F1_class_4: 0.9083
- eval_F1_class_5: 0.8703
- eval_F1_class_6: 0.8330
- eval_F1_class_7: 0.9455
- eval_F1_class_8: 0.9642
- eval_F1_class_9: 0.8581
- eval_F1_class_10: 0.7760
- eval_F1_class_11: 0.8639
- eval_F1_class_12: 0.8035
- eval_F1_class_13: 0.9109
- eval_F1_class_14: 0.8374
- eval_F1_class_15: 0.7641
- eval_F1_class_16: 0.7246
- eval_F1_class_17: 0.9771
- eval_F1_class_18: 0.9031
- eval_F1_class_19: 1.0
- eval_runtime: 106.104
- eval_samples_per_second: 64.993
- eval_steps_per_second: 8.124
- epoch: 0.21
- step: 1008
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ldos/text_shortening_model_v6 | ldos | 2023-09-01T19:08:56Z | 30 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-09-01T16:01:47Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v6
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. -->
# text_shortening_model_v6
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5555
- Rouge1: 0.5993
- Rouge2: 0.3696
- Rougel: 0.551
- Rougelsum: 0.5503
- Bert precision: 0.8968
- Bert recall: 0.9029
- Average word count: 11.2357
- Max word count: 17
- Min word count: 7
- Average token count: 16.4143
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|
| 1.2879 | 1.0 | 4 | 1.7189 | 0.5385 | 0.3175 | 0.4882 | 0.4875 | 0.8762 | 0.886 | 11.8071 | 18 | 5 | 17.1429 |
| 1.1303 | 2.0 | 8 | 1.6107 | 0.5599 | 0.337 | 0.5115 | 0.5117 | 0.8853 | 0.8916 | 11.2071 | 18 | 4 | 16.3071 |
| 1.0984 | 3.0 | 12 | 1.5545 | 0.5828 | 0.354 | 0.5254 | 0.5252 | 0.8885 | 0.8985 | 11.5286 | 17 | 4 | 16.5714 |
| 1.052 | 4.0 | 16 | 1.4943 | 0.5841 | 0.3631 | 0.5384 | 0.5372 | 0.8917 | 0.9004 | 11.3857 | 17 | 5 | 16.6143 |
| 0.9922 | 5.0 | 20 | 1.4517 | 0.5869 | 0.3671 | 0.5437 | 0.5432 | 0.8912 | 0.9011 | 11.5429 | 17 | 5 | 16.7929 |
| 0.9524 | 6.0 | 24 | 1.4308 | 0.5807 | 0.3571 | 0.5332 | 0.5333 | 0.8883 | 0.8994 | 11.6857 | 17 | 5 | 17.0357 |
| 0.9008 | 7.0 | 28 | 1.4152 | 0.5859 | 0.3585 | 0.5333 | 0.5319 | 0.8885 | 0.8974 | 11.4857 | 17 | 5 | 16.7786 |
| 0.8787 | 8.0 | 32 | 1.4089 | 0.5868 | 0.3592 | 0.5366 | 0.5363 | 0.8901 | 0.8991 | 11.4071 | 17 | 5 | 16.8071 |
| 0.857 | 9.0 | 36 | 1.4031 | 0.5974 | 0.3747 | 0.5496 | 0.5494 | 0.892 | 0.9015 | 11.5214 | 17 | 5 | 16.95 |
| 0.8122 | 10.0 | 40 | 1.3961 | 0.5965 | 0.3716 | 0.5487 | 0.5484 | 0.8917 | 0.9031 | 11.7071 | 17 | 6 | 17.1214 |
| 0.7943 | 11.0 | 44 | 1.3922 | 0.6068 | 0.3774 | 0.5572 | 0.5566 | 0.8947 | 0.9058 | 11.5929 | 17 | 6 | 16.9857 |
| 0.7632 | 12.0 | 48 | 1.3949 | 0.6011 | 0.371 | 0.55 | 0.549 | 0.8944 | 0.9039 | 11.4214 | 16 | 5 | 16.9 |
| 0.7464 | 13.0 | 52 | 1.3949 | 0.6007 | 0.3757 | 0.5506 | 0.5492 | 0.8938 | 0.9046 | 11.4357 | 16 | 5 | 16.8714 |
| 0.7235 | 14.0 | 56 | 1.3957 | 0.6113 | 0.3814 | 0.5609 | 0.5601 | 0.8965 | 0.9078 | 11.5429 | 16 | 6 | 16.8714 |
| 0.7293 | 15.0 | 60 | 1.3988 | 0.6102 | 0.3809 | 0.5615 | 0.56 | 0.8948 | 0.9079 | 11.7 | 16 | 6 | 17.15 |
| 0.7188 | 16.0 | 64 | 1.3954 | 0.6094 | 0.381 | 0.5603 | 0.5588 | 0.8965 | 0.9062 | 11.35 | 16 | 6 | 16.8071 |
| 0.7028 | 17.0 | 68 | 1.3969 | 0.6068 | 0.3846 | 0.5581 | 0.5568 | 0.896 | 0.9052 | 11.2571 | 16 | 6 | 16.65 |
| 0.6792 | 18.0 | 72 | 1.4056 | 0.6007 | 0.3777 | 0.5519 | 0.5508 | 0.895 | 0.9048 | 11.3214 | 16 | 6 | 16.6214 |
| 0.671 | 19.0 | 76 | 1.4142 | 0.6043 | 0.3779 | 0.5549 | 0.5541 | 0.8954 | 0.9046 | 11.2429 | 15 | 6 | 16.5429 |
| 0.6644 | 20.0 | 80 | 1.4202 | 0.6009 | 0.3767 | 0.5502 | 0.5496 | 0.8955 | 0.9028 | 11.1643 | 16 | 6 | 16.3643 |
| 0.6526 | 21.0 | 84 | 1.4256 | 0.6023 | 0.374 | 0.5485 | 0.5485 | 0.8958 | 0.9032 | 11.1857 | 17 | 6 | 16.35 |
| 0.6311 | 22.0 | 88 | 1.4356 | 0.6059 | 0.3768 | 0.5492 | 0.5488 | 0.8932 | 0.9042 | 11.5 | 17 | 6 | 16.7214 |
| 0.6448 | 23.0 | 92 | 1.4432 | 0.6071 | 0.3768 | 0.5519 | 0.5518 | 0.8935 | 0.9044 | 11.5357 | 17 | 6 | 16.7643 |
| 0.6344 | 24.0 | 96 | 1.4457 | 0.6088 | 0.3823 | 0.5583 | 0.5576 | 0.8985 | 0.9052 | 11.1214 | 16 | 6 | 16.3071 |
| 0.6299 | 25.0 | 100 | 1.4522 | 0.6049 | 0.3709 | 0.5488 | 0.5484 | 0.8976 | 0.9017 | 10.9 | 16 | 6 | 15.9643 |
| 0.6193 | 26.0 | 104 | 1.4616 | 0.6045 | 0.3701 | 0.5499 | 0.5495 | 0.8959 | 0.9032 | 11.1714 | 16 | 6 | 16.35 |
| 0.6247 | 27.0 | 108 | 1.4704 | 0.5993 | 0.3719 | 0.5515 | 0.5503 | 0.8949 | 0.9041 | 11.3429 | 17 | 7 | 16.6286 |
| 0.6062 | 28.0 | 112 | 1.4760 | 0.6017 | 0.3702 | 0.5537 | 0.5526 | 0.8949 | 0.903 | 11.2929 | 17 | 6 | 16.5143 |
| 0.5921 | 29.0 | 116 | 1.4816 | 0.5994 | 0.3734 | 0.5528 | 0.552 | 0.8959 | 0.9025 | 11.1429 | 17 | 6 | 16.3429 |
| 0.5859 | 30.0 | 120 | 1.4887 | 0.6027 | 0.3724 | 0.5523 | 0.5518 | 0.8956 | 0.9034 | 11.3357 | 17 | 7 | 16.5143 |
| 0.5911 | 31.0 | 124 | 1.4958 | 0.6065 | 0.3757 | 0.5523 | 0.5519 | 0.8971 | 0.9033 | 11.1857 | 17 | 6 | 16.3643 |
| 0.5936 | 32.0 | 128 | 1.5029 | 0.6008 | 0.3745 | 0.5508 | 0.5508 | 0.8973 | 0.9015 | 10.9714 | 16 | 6 | 16.1 |
| 0.584 | 33.0 | 132 | 1.5101 | 0.6087 | 0.3801 | 0.5582 | 0.5583 | 0.8969 | 0.9038 | 11.2214 | 16 | 6 | 16.4071 |
| 0.5741 | 34.0 | 136 | 1.5157 | 0.6054 | 0.3814 | 0.5575 | 0.5576 | 0.8961 | 0.9042 | 11.2643 | 16 | 7 | 16.4786 |
| 0.5793 | 35.0 | 140 | 1.5202 | 0.6079 | 0.3866 | 0.5621 | 0.5622 | 0.8968 | 0.9057 | 11.3214 | 16 | 7 | 16.5714 |
| 0.5803 | 36.0 | 144 | 1.5221 | 0.6081 | 0.3824 | 0.5601 | 0.5602 | 0.8966 | 0.9053 | 11.3357 | 16 | 7 | 16.6214 |
| 0.5719 | 37.0 | 148 | 1.5235 | 0.6025 | 0.3802 | 0.555 | 0.5542 | 0.898 | 0.9035 | 11.1357 | 16 | 7 | 16.3214 |
| 0.5567 | 38.0 | 152 | 1.5238 | 0.5987 | 0.3763 | 0.5524 | 0.5517 | 0.8974 | 0.9024 | 11.0357 | 16 | 7 | 16.2143 |
| 0.5535 | 39.0 | 156 | 1.5264 | 0.6023 | 0.3746 | 0.5547 | 0.5539 | 0.8977 | 0.9035 | 11.1357 | 16 | 7 | 16.3 |
| 0.5507 | 40.0 | 160 | 1.5315 | 0.6039 | 0.3757 | 0.5565 | 0.5559 | 0.8979 | 0.9045 | 11.2071 | 16 | 7 | 16.4143 |
| 0.5568 | 41.0 | 164 | 1.5389 | 0.6078 | 0.3819 | 0.5589 | 0.5579 | 0.8973 | 0.9045 | 11.4 | 17 | 7 | 16.5571 |
| 0.5659 | 42.0 | 168 | 1.5444 | 0.6037 | 0.3788 | 0.5567 | 0.5558 | 0.8959 | 0.9036 | 11.4286 | 17 | 7 | 16.5714 |
| 0.561 | 43.0 | 172 | 1.5475 | 0.5965 | 0.372 | 0.5494 | 0.548 | 0.8958 | 0.9024 | 11.3357 | 17 | 7 | 16.4929 |
| 0.5535 | 44.0 | 176 | 1.5493 | 0.597 | 0.3703 | 0.5495 | 0.5485 | 0.8967 | 0.9025 | 11.2214 | 17 | 7 | 16.3786 |
| 0.5542 | 45.0 | 180 | 1.5507 | 0.6001 | 0.3706 | 0.5529 | 0.5526 | 0.897 | 0.9034 | 11.2429 | 17 | 7 | 16.4214 |
| 0.542 | 46.0 | 184 | 1.5527 | 0.6001 | 0.3706 | 0.5529 | 0.5526 | 0.897 | 0.9034 | 11.2429 | 17 | 7 | 16.4214 |
| 0.5466 | 47.0 | 188 | 1.5539 | 0.6003 | 0.3702 | 0.5529 | 0.5526 | 0.8968 | 0.9033 | 11.2571 | 17 | 7 | 16.4357 |
| 0.5478 | 48.0 | 192 | 1.5550 | 0.5997 | 0.3699 | 0.5515 | 0.5508 | 0.8969 | 0.9029 | 11.2143 | 17 | 7 | 16.3857 |
| 0.5429 | 49.0 | 196 | 1.5552 | 0.5993 | 0.3696 | 0.551 | 0.5503 | 0.8968 | 0.9029 | 11.2357 | 17 | 7 | 16.4143 |
| 0.5443 | 50.0 | 200 | 1.5555 | 0.5993 | 0.3696 | 0.551 | 0.5503 | 0.8968 | 0.9029 | 11.2357 | 17 | 7 | 16.4143 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
ameerazam08/sd_train | ameerazam08 | 2023-09-01T18:38:04Z | 30 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-01T18:23:53Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - ameerazam08/sd_train
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
swl-models/Yorunohitsuji-v1.0 | swl-models | 2023-09-01T18:31:22Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-01T16:18:24Z | ---
license: creativeml-openrail-m
---
|
amarsaxena21/finetuning-sentiment-model-3000-samples | amarsaxena21 | 2023-09-01T18:30:49Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-09-01T18:24:41Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8758169934640523
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
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: 0.3279
- Accuracy: 0.8733
- F1: 0.8758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
AliFartout/Roberta-fa-en-ner | AliFartout | 2023-09-01T18:25:47Z | 127 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"fa",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-08-31T19:05:31Z | ---
license: apache-2.0
language:
- fa
- en
metrics:
- accuracy
pipeline_tag: token-classification
---
# NER Model using Roberta
This markdown presents a Robustly Optimized BERT Pretraining Approach (RoBERTa) model trained on a combination of two diverse datasets for two languages: English and Persian. The English dataset used is [CoNLL 2003](https://huggingface.co/datasets/conll2003), while the Persian dataset is [PEYMA-ARMAN-Mixed](https://huggingface.co/datasets/AliFartout/PEYMA-ARMAN-Mixed), a fusion of the "PEYAM" and "ARMAN" datasets, both popular for Named Entity Recognition (NER) tasks.
The model training pipeline involves the following steps:
Data Preparation: Cleaning, aligning, and mixing data from the two datasets.
Data Loading: Loading the prepared data for subsequent processing.
Tokenization: Utilizing tokenization to prepare the text data for model input.
Token Splitting: Handling token splitting (e.g., "jack" becomes "_ja _ck") and using "-100" for optimization and ignoring certain tokens.
Model Reconstruction: Adapting the RoBERTa model for token classification in NER tasks.
Model Training: Training the reconstructed model on the combined dataset and evaluating its performance.
The model's performance, as shown in the table below, demonstrates promising results:
| Epoch | Training Loss | Validation Loss | F1 | Recall | Precision | Accuracy |
|:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|
| 1 | 0.072600 | 0.038918 | 89.5% | 0.906680 | 0.883703 | 0.987799 |
| 2 | 0.027600 | 0.030184 | 92.3% | 0.933840 | 0.915573 | 0.991334 |
| 3 | 0.013500 | 0.030962 | 94% | 0.946840 | 0.933740 | 0.992702 |
| 4 | 0.006600 | 0.029897 | 94.8% | 0.955207 | 0.941990 | 0.993574 |
The model achieves an impressive F1-score of almost 95%.
To use the model, the following Python code snippet can be employed:
```python
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained("AliFartout/Roberta-fa-en-ner")
tokenizer = AutoTokenizer.from_pretrained("AliFartout/Roberta-fa-en-ner")
model = AutoModel.from_pretrained("AliFartout/Roberta-fa-en-ner")
```
By following this approach, you can seamlessly access and incorporate the trained multilingual NER model into various Natural Language Processing tasks. |
facebook/mms-tts-tzo-dialect_chamula | facebook | 2023-09-01T18:25:20Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:25:04Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Tzotzil Text-to-Speech
This repository contains the **Tzotzil (tzo-dialect_chamula)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-tzo-dialect_chamula")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-tzo-dialect_chamula")
text = "some example text in the Tzotzil language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-tzj-dialect_western | facebook | 2023-09-01T18:24:44Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:24:27Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Tz’utujil Text-to-Speech
This repository contains the **Tz’utujil (tzj-dialect_western)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-tzj-dialect_western")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-tzj-dialect_western")
text = "some example text in the Tz’utujil language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-tzj-dialect_eastern | facebook | 2023-09-01T18:24:11Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:23:52Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Tz’utujil Text-to-Speech
This repository contains the **Tz’utujil (tzj-dialect_eastern)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-tzj-dialect_eastern")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-tzj-dialect_eastern")
text = "some example text in the Tz’utujil language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-mah | facebook | 2023-09-01T18:23:33Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:23:17Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Marshallese Text-to-Speech
This repository contains the **Marshallese (mah)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-mah")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-mah")
text = "some example text in the Marshallese language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-tzh-dialect_bachajon | facebook | 2023-09-01T18:22:56Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:22:40Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Tzeltal Text-to-Speech
This repository contains the **Tzeltal (tzh-dialect_bachajon)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-tzh-dialect_bachajon")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-tzh-dialect_bachajon")
text = "some example text in the Tzeltal language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-mag | facebook | 2023-09-01T18:22:54Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:22:37Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Magahi Text-to-Speech
This repository contains the **Magahi (mag)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-mag")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-mag")
text = "some example text in the Magahi language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
facebook/mms-tts-maa-dialect_sanjeronimo | facebook | 2023-09-01T18:21:46Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vits",
"text-to-audio",
"mms",
"text-to-speech",
"arxiv:2305.13516",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2023-09-01T18:21:30Z |
---
license: cc-by-nc-4.0
tags:
- mms
- vits
pipeline_tag: text-to-speech
---
# Massively Multilingual Speech (MMS): Mazatec, San Jerónimo Tecóatl Text-to-Speech
This repository contains the **Mazatec, San Jerónimo Tecóatl (maa-dialect_sanjeronimo)** language text-to-speech (TTS) model checkpoint.
This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to
provide speech technology across a diverse range of languages. You can find more details about the supported languages
and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
## Model Details
VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end
speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational
autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based
text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers,
much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text
input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to
synthesise speech with different rhythms from the same input text.
The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training.
To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During
inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the
waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor,
the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.
For the MMS project, a separate VITS checkpoint is trained on each langauge.
## Usage
MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint,
first install the latest version of the library:
```
pip install --upgrade transformers accelerate
```
Then, run inference with the following code-snippet:
```python
from transformers import VitsModel, AutoTokenizer
import torch
model = VitsModel.from_pretrained("facebook/mms-tts-maa-dialect_sanjeronimo")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-maa-dialect_sanjeronimo")
text = "some example text in the Mazatec, San Jerónimo Tecóatl language"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
```
The resulting waveform can be saved as a `.wav` file:
```python
import scipy
scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output)
```
Or displayed in a Jupyter Notebook / Google Colab:
```python
from IPython.display import Audio
Audio(output, rate=model.config.sampling_rate)
```
## BibTex citation
This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
```
@article{pratap2023mms,
title={Scaling Speech Technology to 1,000+ Languages},
author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
journal={arXiv},
year={2023}
}
```
## License
The model is licensed as **CC-BY-NC 4.0**.
|
sidharthr/lora-trained-xl | sidharthr | 2023-09-01T18:13:39Z | 3 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2023-09-01T05:10:32Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sidharthr/lora-trained-xl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
gabryland/lpcv_seg | gabryland | 2023-09-01T18:09:02Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"mobilenet_v2",
"generated_from_trainer",
"base_model:google/deeplabv3_mobilenet_v2_1.0_513",
"base_model:finetune:google/deeplabv3_mobilenet_v2_1.0_513",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-01T16:07:43Z | ---
license: other
base_model: google/deeplabv3_mobilenet_v2_1.0_513
tags:
- generated_from_trainer
model-index:
- name: lpcv_seg
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. -->
# lpcv_seg
This model is a fine-tuned version of [google/deeplabv3_mobilenet_v2_1.0_513](https://huggingface.co/google/deeplabv3_mobilenet_v2_1.0_513) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7747
- Mean Iou: 0.3647
- Mean Accuracy: 0.4742
- Overall Accuracy: 0.7441
- Per Category Iou: [0.7508396000072782, 0.461963906773346, 0.41562431632163865, 0.2643890336606752, 0.20882280410355394, 0.21001420486640948, 0.45776923048905305, 0.6263265430221951, 0.5990132199881534, nan, 0.0, 0.4389678627683, 0.30687238462719907, 0.0]
- Per Category Accuracy: [0.8794950417196551, 0.5122587212045141, 0.47963636761360323, 0.28307093261894156, 0.22726847453969443, 0.817351469679542, 0.5476940642254209, 0.672940072704085, 0.8955934160757268, nan, 0.0, 0.44608425759467085, 0.40330970104326763, 0.0]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.0299 | 3.12 | 400 | 1.1445 | 0.1503 | 0.2293 | 0.6846 | [0.7016834423401825, 0.0, 0.28392253175773663, 0.09630649211770352, 0.16489618177561766, 0.05923195228949304, 0.0, 0.47146079872020313, 0.0, nan, 0.0, 0.002537363927640351, 0.17407988046100897, 0.0] | [0.891422563505799, 0.0, 0.7160794493379222, 0.101650104427665, 0.20155241518296468, 0.08566222638071554, 0.0, 0.7801995050268263, 0.0, nan, 0.0, 0.002538359085235112, 0.20133152963007558, 0.0] |
| 1.2946 | 6.25 | 800 | 0.9463 | 0.1514 | 0.2217 | 0.6990 | [0.7200410800543089, 0.025577984693804623, 0.272186783532416, 0.03900872477465889, 0.1420562982408946, 0.13672818472813886, 0.0, 0.3747836709762802, 1.4597782434650593e-05, nan, 0.0, 0.0, 0.2576138826730331, 0.0] | [0.9197800777719178, 0.025581056237151574, 0.3936555737035525, 0.03917595636589459, 0.1525480793198657, 0.16772954472705784, 0.0, 0.8514708072975876, 1.4597782434650593e-05, nan, 0.0, 0.0, 0.33275562889985905, 0.0] |
| 0.8869 | 9.38 | 1200 | 0.9005 | 0.2553 | 0.3463 | 0.7230 | [0.7311911080696011, 0.1716353741804461, 0.32989770226558307, 0.2484733739307282, 0.20678309581325105, 0.16925674004774066, 0.026560396219635213, 0.43309262889720945, 0.4791539954039438, nan, 0.0, 0.1996331281686418, 0.3234492318785704, 0.0] | [0.8817514590505282, 0.1734977169471996, 0.5536498133348888, 0.26666168042981364, 0.2199222972903758, 0.30495278898499545, 0.026735171458080034, 0.8794234634009936, 0.5647817144933271, nan, 0.0, 0.19998287851911137, 0.43016977002783463, 0.0] |
| 0.8056 | 12.5 | 1600 | 0.7305 | 0.3106 | 0.3897 | 0.7595 | [0.7538928589848243, 0.25918801874572633, 0.4203485338620474, 0.2917137053557758, 0.22289503658523782, 0.1500749815626878, 0.08861529960760275, 0.624086456268012, 0.48451939021185103, nan, 0.0, 0.3968018568653773, 0.34541558718052773, 0.0] | [0.9269385933729979, 0.2666068145623601, 0.5690834086799277, 0.323866282726429, 0.23199974868520706, 0.27844356662715314, 0.08945714746397179, 0.7853752936779852, 0.7445501612243971, nan, 0.0, 0.4213536720599331, 0.42861443009823574, 0.0] |
| 0.7534 | 15.62 | 2000 | 0.8091 | 0.2762 | 0.3774 | 0.7408 | [0.7450525081446471, 0.08805507548598117, 0.39296675038083556, 0.17888143033371248, 0.23466648108570068, 0.16079006682931019, 0.03714210117676656, 0.6219865494576018, 0.5133743295405404, nan, 0.0, 0.25318448016278294, 0.3644475822818699, 0.0] | [0.8925789949539187, 0.08813412970942185, 0.6256571559820335, 0.18979883934004887, 0.24639981315291482, 0.4060874245051725, 0.03731593695118816, 0.8003825980603533, 0.8838065177476595, nan, 0.0, 0.25812424222515545, 0.47821461707899227, 0.0] |
| 0.909 | 18.75 | 2400 | 0.7351 | 0.3258 | 0.4109 | 0.7698 | [0.7635658546626002, 0.40487164496967293, 0.3833268397798514, 0.2752089531995694, 0.2867291055853458, 0.019354003421208514, 0.16890635042927862, 0.5748136637050552, 0.6549805047839075, nan, 0.0, 0.41018196642738697, 0.29410209406700305, 0.0] | [0.936836241285118, 0.4435412622122596, 0.6115797993350055, 0.29433416172859234, 0.3084226650431367, 0.01980599690692518, 0.1729223045848531, 0.8637196946532967, 0.9040471540812156, nan, 0.0, 0.44469064868513, 0.3419148529297193, 0.0] |
| 0.6622 | 21.88 | 2800 | 0.6963 | 0.3398 | 0.3986 | 0.7827 | [0.7663761109958838, 0.1332027535954636, 0.4428707076945945, 0.35775923195424675, 0.3733786220955952, 0.24949901778334058, 0.07535404639899677, 0.6174965473057359, 0.5443824880761584, nan, 0.0, 0.4962856002887922, 0.3608184550398603, 0.0] | [0.9493652245312661, 0.13338632341294235, 0.6300075103540804, 0.4173944872618647, 0.41213577281689057, 0.31108969330720987, 0.0759392172499597, 0.6593100168039995, 0.5955570838172228, nan, 0.0, 0.5200351189445205, 0.4777004199984418, 0.0] |
| 1.1202 | 25.0 | 3200 | 1.6352 | 0.1684 | 0.2322 | 0.7026 | [0.6963172586859363, 0.011559652438449069, 0.22669839158942193, 0.05922256175168542, 0.06532726727054458, 0.0023358067869460536, 0.0, 0.509361415684211, 0.38727970470408707, nan, 0.0, 0.09125458640751972, 0.13993367687629527, 0.0] | [0.9487412199772098, 0.011559652438449069, 0.24983867321938985, 0.05932148648786475, 0.06540672362680026, 0.003050864548414594, 0.0, 0.7020197466594538, 0.7380590139684559, nan, 0.0, 0.09130527401337467, 0.14901587211649467, 0.0] |
| 0.6529 | 28.12 | 3600 | 0.7747 | 0.3647 | 0.4742 | 0.7441 | [0.7508396000072782, 0.461963906773346, 0.41562431632163865, 0.2643890336606752, 0.20882280410355394, 0.21001420486640948, 0.45776923048905305, 0.6263265430221951, 0.5990132199881534, nan, 0.0, 0.4389678627683, 0.30687238462719907, 0.0] | [0.8794950417196551, 0.5122587212045141, 0.47963636761360323, 0.28307093261894156, 0.22726847453969443, 0.817351469679542, 0.5476940642254209, 0.672940072704085, 0.8955934160757268, nan, 0.0, 0.44608425759467085, 0.40330970104326763, 0.0] |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Subsets and Splits