modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 00:47:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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nolanaatama/mrcllphllvnrvcdclkd44
|
nolanaatama
| 2023-07-03T20:45:27Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-03T19:11:59Z |
---
license: creativeml-openrail-m
---
|
Valinski/Spiderverse_trial_run
|
Valinski
| 2023-07-03T20:38:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"arxiv:1910.09700",
"license:artistic-2.0",
"region:us"
] | null | 2023-07-03T20:11:02Z |
---
license: artistic-2.0
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]
|
idealflaw/ppo-LunarLander-v2
|
idealflaw
| 2023-07-03T20:27:22Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T20:27:04Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.20 +/- 23.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LarryAIDraw/oshinoko-s1-step-60000
|
LarryAIDraw
| 2023-07-03T20:26:08Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-03T20:18:57Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/40182/oshinoko-characters-lohaloconfullckpt-oror-hoshino-ai-hoshino-aquamarine-hoshino-ruby-arima-kana-saito-miyako-kurokawa-akane-kotobuki-minami-shiranui-frill-sumi-yuki
|
espnet/brianyan918_mustc-v2_en-de_st_ctc_rnnt_asrinit_raw_en_de_bpe_tc4000_sp
|
espnet
| 2023-07-03T20:20:51Z | 1 | 0 | null |
[
"region:us"
] | null | 2023-07-03T20:18:41Z |
- Download model and run inference:
`./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_mustc-v2_en-de_st_ctc_rnnt_asrinit_raw_en_de_bpe_tc4000_sp --inference_config conf/tuning/decode_rnnt_tsd_mse4_scorenormduring_beam10.yaml`
|dataset|score|verbose_score|
|---|---|---|
|decode_rnnt_tsd_mse4_scorenormduring_beam10_st_model_valid.loss.ave_10best/tst-COMMON.en-de|27.6|60.2/33.6/21.0/13.7 (BP = 0.998 ratio = 0.998 hyp_len = 51602 ref_len = 51699)|
|
espnet/brianyan918_mustc-v2_en-de_st_ctc_md_conformer_asrinit_v3_raw_en_de_bpe_tc4000_sp
|
espnet
| 2023-07-03T20:17:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-03T20:15:03Z |
- Download model and run inference:
`./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_mustc-v2_en-de_st_ctc_md_conformer_asrinit_v3_raw_en_de_bpe_tc4000_sp --inference_config conf/tuning/decode_st_md_ctc0.3.yaml`
|dataset|score|verbose_score|
|---|---|---|
|decode_st_md_ctc0.3_st_model_valid.acc.ave_10best/tst-COMMON.en-de|28.8|61.5/35.0/22.2/14.7 (BP = 0.994 ratio = 0.994 hyp_len = 51386 ref_len = 51699)|
|
alphaduriendur/ner-deBERTa-v3-large-conll2003
|
alphaduriendur
| 2023-07-03T20:07:39Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-07-03T06:16:03Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-deBERTa-v3-large-conll2003
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9235068110373734
- name: Recall
type: recall
value: 0.9362606232294618
- name: F1
type: f1
value: 0.9298399859328293
- name: Accuracy
type: accuracy
value: 0.9853128028426833
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner-deBERTa-v3-large-conll2003
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1546
- Precision: 0.9235
- Recall: 0.9363
- F1: 0.9298
- Accuracy: 0.9853
## 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: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0077 | 1.0 | 878 | 0.1280 | 0.9096 | 0.9265 | 0.9180 | 0.9832 |
| 0.0084 | 2.0 | 1756 | 0.1380 | 0.9167 | 0.9299 | 0.9233 | 0.9844 |
| 0.0037 | 3.0 | 2634 | 0.1495 | 0.9221 | 0.9347 | 0.9283 | 0.9850 |
| 0.0015 | 4.0 | 3512 | 0.1517 | 0.9215 | 0.9347 | 0.9280 | 0.9849 |
| 0.0006 | 5.0 | 4390 | 0.1546 | 0.9235 | 0.9363 | 0.9298 | 0.9853 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
goter/onceler
|
goter
| 2023-07-03T19:54:52Z | 0 | 1 | null |
[
"ru",
"en",
"license:openrail",
"region:us"
] | null | 2023-06-04T13:16:48Z |
---
license: openrail
language:
- ru
- en
---
|
mrizalf7/xlm-r-qa-small-squad
|
mrizalf7
| 2023-07-03T19:50:09Z | 116 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-07-03T18:15:49Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-r-qa-small-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. -->
# xlm-r-qa-small-squad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9800
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2394 | 1.0 | 5437 | 1.9701 |
| 0.9683 | 2.0 | 10874 | 1.9800 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
PrakhAI/HelloWorld
|
PrakhAI
| 2023-07-03T19:22:35Z | 0 | 0 | null |
[
"dataset:mnist",
"license:gpl-3.0",
"region:us"
] | null | 2023-07-02T01:34:55Z |
---
license: gpl-3.0
datasets:
- mnist
---
Flax handwritten digit (MNIST) classification model trained using https://colab.research.google.com/github/google/flax/blob/main/docs/getting_started.ipynb
|
andres-gv/model-cmi-1
|
andres-gv
| 2023-07-03T19:19:59Z | 4 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2023-07-03T19:13:59Z |
---
pipeline_tag: text-classification
library_name: bertopic
---
|
practical-dreamer/rpgpt-7b-lora
|
practical-dreamer
| 2023-07-03T19:08:58Z | 0 | 1 | null |
[
"dataset:practicaldreamer/RPGPT_PublicDomain-ShareGPT",
"region:us"
] | null | 2023-07-03T18:16:52Z |
---
datasets:
- practicaldreamer/RPGPT_PublicDomain-ShareGPT
---
## Introduction
This is my first attempt at training a model for long form character interaction using asterisk roleplay format.
There are plenty of general instruction/answer models but most focus on single responses between an ai and a human.
My goal for this project is to more closely align the training data with CHARACTER interactions for roleplay.
This model is trained on a small synthetic dataset of characters interacting through a variety of scenarios.
The Characters, Scenarios and interactions were all generated by GPT4.
Intended for research, creative writing, entertainment, DnD campaigns? fun!
## Train Summary
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
```
duration: ~1hr
gpu: 1xA100 80GB
epochs: 1.0
speed: 3e-5
sequence_len: 2048
gradient_accumulation_steps: 32
wandb: https://wandb.ai/practicaldreamer/rpgpt/runs/bvyawfro
```
*Please see the documentation folder for more information*
## Usage
This LoRA was trained for use with **Neko-Institute-of-Science/LLaMA-7B-HF**
Please follow the prompt format outlined below. *Hint: If you're not sure what to put for your character description (or you're lazy) just ask chatgpt to generate it for you! Example:*
```
Generate a short character description for Dr. Watson (The Adventures of Sherlock Holmes) that includes gender, age, MBTI and speech accent using 30 words or less.
```
## Prompt Format
Context/Memory:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Write a character roleplay dialogue using asterisk roleplay format based on the following character descriptions and scenario. (Each line in your response must be from the perspective of one of these characters)
## Characters
<User-Character Name> (<User-Character Universe>):
<User-Character Description>
<Bot-Character Name> (Bot-Character Universe):
<Bot-Character Description>
## Scenario
<Scenario Description>
ASSISTANT:
```
Turn Template:
```
<User-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<Bot-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<User-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<Bot-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
...
```
## Example
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Write a character roleplay dialogue using asterisk roleplay format based on the following character descriptions and scenario. (Each line in your response must be from the perspective of one of these characters)
## Characters
Baloo (The Jungle Book):
Male, middle-aged bear, ENFP, primarily American accent with slight Indian inflections. Wise, carefree, and friendly, he teaches Mowgli the ways of the jungle.
The Queen of Hearts (Alice's Adventures in Wonderland):
Female, middle-aged, ENTJ, British English accent; imperious, temperamental, commanding, but quick to change from anger to warmth - the quintessential capricious monarch.
## Scenario
In an enchanted glade deep within Wonderland, Baloo finds himself separated from Mowgli and in the Queen of Hearts' territory. Having heard tales of this mercurial ruler, Baloo is intrigued but cautious. To his surprise, the Queen of Hearts approaches him with a broken heart - she has been betrayed by one of her closest confidants.
The Queen initially sees Baloo as an opportunity to vent her frustrations and seek validation for her hurt feelings. Baloo, however, quickly recognizes that the Queen's explosive temperament is driving others away and contributing to her own isolation.\n\nUsing his wisdom and patience, Baloo tries to teach the Queen the value of empathy and understanding in forming meaningful relationships. Through their dialogue, they explore their contrasting attitudes toward power, authority, and friendship. The Queen learns that it is not through fear and command that she can find true loyalty, but rather by treating others with kindness.
In this somber environment where both characters are out of their comfort zones, can Baloo help the Queen mend her own heart by opening up about his experiences with Mowgli? Will the Queen allow herself to be vulnerable enough to learn from the wise bear? And can these two unlikely friends find common ground in this enchanted glade within Wonderland?
ASSISTANT:
Baloo: *stumbles into the enchanted glade, looking around curiously* Well, I must've taken a wrong turn back there. Mowgli! *calls out for Mowgli*
The Queen of Hearts: *storms into the glade, clutching a broken heart symbol, face contorted in anger* Off with their heads! Everyone's heads!
Baloo: *gently scratches his head and approaches the Queen cautiously* Uh, excuse me, ma'am? I couldn't help but notice that you seem rather... upset.
The Queen of Hearts: *sniffs haughtily and scowls at Baloo* And who are you to pry into my affairs? You should be bowing to your queen!
Baloo: *bows graciously but maintains eye contact* Begging your pardon, Your Majesty. My name is Baloo, and I've heard tales of your... unique temperament.
The Queen of Hearts:
```
## Thanks
openaccess-ai-collective https://huggingface.co/openaccess-ai-collective
---
license: mit
---
|
practical-dreamer/rpgpt-13b-lora
|
practical-dreamer
| 2023-07-03T19:08:32Z | 0 | 2 | null |
[
"dataset:practicaldreamer/RPGPT_PublicDomain-ShareGPT",
"region:us"
] | null | 2023-07-03T17:17:03Z |
---
datasets:
- practicaldreamer/RPGPT_PublicDomain-ShareGPT
---
## Introduction
This is my first attempt at training a model for long form character interaction using asterisk roleplay format.
There are plenty of general instruction/answer models but most focus on single responses between an ai and a human.
My goal for this project is to more closely align the training data with CHARACTER interactions for roleplay.
This model is trained on a small synthetic dataset of characters interacting through a variety of scenarios.
The Characters, Scenarios and interactions were all generated by GPT4.
Intended for research, creative writing, entertainment, DnD campaigns? fun!
## Train Summary
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
```
duration: ~1.5hrs
gpu: 1xA100 80GB
epochs: 1.0
speed: 3e-5
sequence_len: 2048
gradient_accumulation_steps: 32
wandb: https://wandb.ai/practicaldreamer/rpgpt/runs/b3sznjpz
```
*Please see the documentation folder for more information*
## Usage
This LoRA was trained for use with **Neko-Institute-of-Science/LLaMA-13B-HF**
Please follow the prompt format outlined below. *Hint: If you're not sure what to put for your character description (or you're lazy) just ask chatgpt to generate it for you! Example:*
```
Generate a short character description for Dr. Watson (The Adventures of Sherlock Holmes) that includes gender, age, MBTI and speech accent using 30 words or less.
```
## Prompt Format
Context/Memory:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Write a character roleplay dialogue using asterisk roleplay format based on the following character descriptions and scenario. (Each line in your response must be from the perspective of one of these characters)
## Characters
<User-Character Name> (<User-Character Universe>):
<User-Character Description>
<Bot-Character Name> (Bot-Character Universe):
<Bot-Character Description>
## Scenario
<Scenario Description>
ASSISTANT:
```
Turn Template:
```
<User-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<Bot-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<User-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
<Bot-Character Name>: \*<1st person action/sensations/thoughts>\* <Spoken Word> \*<1st person action/sensations/thoughts>\*
...
```
## Example
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Write a character roleplay dialogue using asterisk roleplay format based on the following character descriptions and scenario. (Each line in your response must be from the perspective of one of these characters)
## Characters
Baloo (The Jungle Book):
Male, middle-aged bear, ENFP, primarily American accent with slight Indian inflections. Wise, carefree, and friendly, he teaches Mowgli the ways of the jungle.
The Queen of Hearts (Alice's Adventures in Wonderland):
Female, middle-aged, ENTJ, British English accent; imperious, temperamental, commanding, but quick to change from anger to warmth - the quintessential capricious monarch.
## Scenario
In an enchanted glade deep within Wonderland, Baloo finds himself separated from Mowgli and in the Queen of Hearts' territory. Having heard tales of this mercurial ruler, Baloo is intrigued but cautious. To his surprise, the Queen of Hearts approaches him with a broken heart - she has been betrayed by one of her closest confidants.
The Queen initially sees Baloo as an opportunity to vent her frustrations and seek validation for her hurt feelings. Baloo, however, quickly recognizes that the Queen's explosive temperament is driving others away and contributing to her own isolation.\n\nUsing his wisdom and patience, Baloo tries to teach the Queen the value of empathy and understanding in forming meaningful relationships. Through their dialogue, they explore their contrasting attitudes toward power, authority, and friendship. The Queen learns that it is not through fear and command that she can find true loyalty, but rather by treating others with kindness.
In this somber environment where both characters are out of their comfort zones, can Baloo help the Queen mend her own heart by opening up about his experiences with Mowgli? Will the Queen allow herself to be vulnerable enough to learn from the wise bear? And can these two unlikely friends find common ground in this enchanted glade within Wonderland?
ASSISTANT:
Baloo: *stumbles into the enchanted glade, looking around curiously* Well, I must've taken a wrong turn back there. Mowgli! *calls out for Mowgli*
The Queen of Hearts: *storms into the glade, clutching a broken heart symbol, face contorted in anger* Off with their heads! Everyone's heads!
Baloo: *gently scratches his head and approaches the Queen cautiously* Uh, excuse me, ma'am? I couldn't help but notice that you seem rather... upset.
The Queen of Hearts: *sniffs haughtily and scowls at Baloo* And who are you to pry into my affairs? You should be bowing to your queen!
Baloo: *bows graciously but maintains eye contact* Begging your pardon, Your Majesty. My name is Baloo, and I've heard tales of your... unique temperament.
The Queen of Hearts:
```
## Thanks
openaccess-ai-collective https://huggingface.co/openaccess-ai-collective
---
license: mit
---
|
RajkNakka/Taxi-v3
|
RajkNakka
| 2023-07-03T19:01:50Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T19:01:48Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="RajkNakka/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
headflame02/Achaz
|
headflame02
| 2023-07-03T18:56:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-03T18:53:43Z |
---
license: creativeml-openrail-m
---
|
geekyrakshit/DeepLabV3-Plus
|
geekyrakshit
| 2023-07-03T18:51:23Z | 60 | 0 |
keras
|
[
"keras",
"segmentation",
"tensorflow",
"cityscapes",
"arxiv:1802.02611",
"region:us"
] | null | 2023-07-03T17:32:36Z |
---
metrics:
- accuracy
- mean_iou
tags:
- segmentation
- keras
- tensorflow
- cityscapes
---
# DeepLabV3-Plus
Keras implementation of the DeepLabV3+ model as proposed by the paper [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)(ECCV 2018).
The models were trained on the fine-annotations set of the [Cityscapes dataset](cityscapes-dataset.com) for creating presets for [this PR](https://github.com/keras-team/keras-cv/pull/1831) on the `keras-cv` repository.
**Weights & Biases Dashboard:** https://wandb.ai/geekyrakshit/deeplabv3-keras-cv
|
anujsahani01/finetuned_mbart
|
anujsahani01
| 2023-07-03T18:40:55Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-17T14:19:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned_Mbart
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_Mbart
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) 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: 0.0005
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Shularp/TestHelsinkimulEnJpTh02
|
Shularp
| 2023-07-03T18:39:09Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-03T11:53:35Z |
---
tags:
- generated_from_trainer
model-index:
- name: TestHelsinkimulEnJpTh02
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. -->
# TestHelsinkimulEnJpTh02
This model is a fine-tuned version of [Shularp/TestHelsinkimulEnJpTh02](https://huggingface.co/Shularp/TestHelsinkimulEnJpTh02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4364 | 1.0 | 4846 | 0.2666 |
| 0.1094 | 2.0 | 9692 | 0.2277 |
| 0.0484 | 3.0 | 14538 | 0.1940 |
| 0.0111 | 4.0 | 19384 | 0.1749 |
| 0.0105 | 5.0 | 24230 | 0.1630 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Enymy/t5-base-feedback-generator
|
Enymy
| 2023-07-03T18:38:49Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-03T18:36:24Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-feedback-generator
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. -->
# t5-base-feedback-generator
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
falkne/justification
|
falkne
| 2023-07-03T18:20:44Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:42Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/justification` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/justification", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/argumentative
|
falkne
| 2023-07-03T18:20:37Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:36Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/argumentative` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/argumentative", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/story
|
falkne
| 2023-07-03T18:20:36Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:34Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/story` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/story", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/respect
|
falkne
| 2023-07-03T18:20:33Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:32Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/respect` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/respect", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/clarity
|
falkne
| 2023-07-03T18:20:31Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:30Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/clarity` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/clarity", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/reasonableness
|
falkne
| 2023-07-03T18:20:30Z | 3 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:28Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/reasonableness` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/reasonableness", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/negEmotion
|
falkne
| 2023-07-03T18:20:24Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:23Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/negEmotion` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/negEmotion", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/posEmotion
|
falkne
| 2023-07-03T18:20:20Z | 1 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:19Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/posEmotion` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/posEmotion", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/interactivity
|
falkne
| 2023-07-03T18:20:18Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:17Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/interactivity` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/interactivity", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/empathie
|
falkne
| 2023-07-03T18:20:14Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:20:13Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/empathie` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/empathie", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/impact
|
falkne
| 2023-07-03T18:20:09Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:11:35Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/impact` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/impact", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
falkne/effectiveness
|
falkne
| 2023-07-03T18:20:09Z | 2 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:argument/quality",
"roberta",
"region:us"
] | null | 2023-07-03T18:17:55Z |
---
tags:
- adapterhub:argument/quality
- roberta
- adapter-transformers
---
# Adapter `falkne/effectiveness` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [argument/quality](https://adapterhub.ml/explore/argument/quality/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/effectiveness", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
BBAI/qlora-koalpaca-polyglot-12.8b-50step
|
BBAI
| 2023-07-03T18:06:07Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-06-22T06:33:23Z |
---
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: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
JMGaloDoido/ppo-Huggy
|
JMGaloDoido
| 2023-07-03T18:04:01Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-03T16:41:47Z |
---
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: JMGaloDoido/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
osiria/bert-tweet-base-italian-uncased
|
osiria
| 2023-07-03T17:57:30Z | 173 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"it",
"arxiv:1810.04805",
"arxiv:2209.07562",
"arxiv:2010.05609",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-05-29T17:25:55Z |
---
license: apache-2.0
language:
- it
widget:
- text: "una fantastica [MASK] di #calcio! grande prestazione del mister e della squadra"
example_title: "Example 1"
- text: "il governo [MASK] dovrebbe fare politica, non soltanto propaganda! #vergogna"
example_title: "Example 2"
- text: "che serata da sogno sul #redcarpet! grazie a tutti gli attori e registi del [MASK] italiano #oscar #awards"
example_title: "Example 3"
---
--------------------------------------------------------------------------------------------------
<body>
<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;"> Model: BERT-TWEET</span>
<br>
<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;"> Lang: IT</span>
<br>
<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;"> </span>
<br>
<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
</body>
--------------------------------------------------------------------------------------------------
<h3>Model description</h3>
This is a <b>BERT</b> <b>[1]</b> uncased model for the <b>Italian</b> language, obtained using <b>TwHIN-BERT</b> <b>[2]</b> ([twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base)) as a starting point and focusing it on the Italian language by modifying the embedding layer
(as in <b>[3]</b>, computing document-level frequencies over the <b>Wikipedia</b> dataset)
The resulting model has 110M parameters, a vocabulary of 30.520 tokens, and a size of ~440 MB.
<h3>Quick usage</h3>
```python
from transformers import BertTokenizerFast, BertModel
tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-tweet-base-italian-uncased")
model = BertModel.from_pretrained("osiria/bert-tweet-base-italian-uncased")
```
Here you can find the find the model already fine-tuned on Sentiment Analysis: https://huggingface.co/osiria/bert-tweet-italian-uncased-sentiment
<h3>References</h3>
[1] https://arxiv.org/abs/1810.04805
[2] https://arxiv.org/abs/2209.07562
[3] https://arxiv.org/abs/2010.05609
<h3>Limitations</h3>
This model was trained on tweets, so it's mainly suitable for general-purpose social media text processing, involving short texts written in a social network style.
It might show limitations when it comes to longer and more structured text, or domain-specific text.
<h3>License</h3>
The model is released under <b>Apache-2.0</b> license
|
hopkins/eng-kor-simcse.dev2.44k
|
hopkins
| 2023-07-03T17:51:10Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T17:38:07Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-kor-simcse.dev2.44k
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. -->
# eng-kor-simcse.dev2.44k
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9818
- Bleu: 7.4953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Officialletai/Taxi-v3
|
Officialletai
| 2023-07-03T17:46:05Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T17:43:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Officialletai/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hopkins/eng-ind-simcse.dev2.44k
|
hopkins
| 2023-07-03T17:37:40Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T17:19:41Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-simcse.dev2.44k
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. -->
# eng-ind-simcse.dev2.44k
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7531
- Bleu: 22.6202
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
cdreetz/codeparrot-ds2
|
cdreetz
| 2023-07-03T17:31:45Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-05-15T19:08:28Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds2
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. -->
# codeparrot-ds2
GPT-2 style trained on a filtered set of The Stack, specific to data science related code. Things like pandas, numpy, matplotlib, etc.
- Loss: 1.0584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2038 | 0.01 | 500 | 2.1062 |
| 2.0551 | 0.02 | 1000 | 2.0109 |
| 1.9622 | 0.02 | 1500 | 1.9219 |
| 1.9512 | 0.03 | 2000 | 1.8461 |
| 1.8817 | 0.04 | 2500 | 1.7903 |
| 1.8341 | 0.05 | 3000 | 1.7401 |
| 1.7877 | 0.05 | 3500 | 1.7022 |
| 1.7586 | 0.06 | 4000 | 1.6694 |
| 1.7271 | 0.07 | 4500 | 1.6457 |
| 1.7034 | 0.08 | 5000 | 1.6193 |
| 1.6756 | 0.08 | 5500 | 1.5978 |
| 1.6576 | 0.09 | 6000 | 1.5772 |
| 1.6377 | 0.1 | 6500 | 1.5611 |
| 1.6211 | 0.11 | 7000 | 1.5453 |
| 1.6033 | 0.11 | 7500 | 1.5317 |
| 1.591 | 0.12 | 8000 | 1.5193 |
| 1.5765 | 0.13 | 8500 | 1.5053 |
| 1.5661 | 0.14 | 9000 | 1.4966 |
| 1.5548 | 0.15 | 9500 | 1.4846 |
| 1.5429 | 0.15 | 10000 | 1.4729 |
| 1.5347 | 0.16 | 10500 | 1.4641 |
| 1.5215 | 0.17 | 11000 | 1.4557 |
| 1.5151 | 0.18 | 11500 | 1.4454 |
| 1.5059 | 0.18 | 12000 | 1.4381 |
| 1.499 | 0.19 | 12500 | 1.4288 |
| 1.4906 | 0.2 | 13000 | 1.4210 |
| 1.4849 | 0.21 | 13500 | 1.4143 |
| 1.4765 | 0.21 | 14000 | 1.4085 |
| 1.4708 | 0.22 | 14500 | 1.4026 |
| 1.4602 | 0.23 | 15000 | 1.3936 |
| 1.4533 | 0.24 | 15500 | 1.3896 |
| 1.4523 | 0.25 | 16000 | 1.3818 |
| 1.4415 | 0.25 | 16500 | 1.3748 |
| 1.4417 | 0.26 | 17000 | 1.3701 |
| 1.4311 | 0.27 | 17500 | 1.3645 |
| 1.4282 | 0.28 | 18000 | 1.3585 |
| 1.4223 | 0.28 | 18500 | 1.3531 |
| 1.4165 | 0.29 | 19000 | 1.3473 |
| 1.4105 | 0.3 | 19500 | 1.3419 |
| 1.3993 | 0.31 | 20000 | 1.3374 |
| 1.4034 | 0.31 | 20500 | 1.3322 |
| 1.3982 | 0.32 | 21000 | 1.3278 |
| 1.3951 | 0.33 | 21500 | 1.3225 |
| 1.3806 | 0.34 | 22000 | 1.3180 |
| 1.3781 | 0.34 | 22500 | 1.3121 |
| 1.3761 | 0.35 | 23000 | 1.3082 |
| 1.3662 | 0.36 | 23500 | 1.3038 |
| 1.3631 | 0.37 | 24000 | 1.2995 |
| 1.3549 | 0.38 | 24500 | 1.2955 |
| 1.3577 | 0.38 | 25000 | 1.2912 |
| 1.3498 | 0.39 | 25500 | 1.2851 |
| 1.3428 | 0.4 | 26000 | 1.2807 |
| 1.342 | 0.41 | 26500 | 1.2768 |
| 1.3365 | 0.41 | 27000 | 1.2720 |
| 1.3313 | 0.42 | 27500 | 1.2678 |
| 1.3309 | 0.43 | 28000 | 1.2629 |
| 1.3221 | 0.44 | 28500 | 1.2594 |
| 1.3214 | 0.44 | 29000 | 1.2558 |
| 1.3099 | 0.45 | 29500 | 1.2510 |
| 1.31 | 0.46 | 30000 | 1.2449 |
| 1.31 | 0.47 | 30500 | 1.2414 |
| 1.305 | 0.48 | 31000 | 1.2390 |
| 1.2975 | 0.48 | 31500 | 1.2358 |
| 1.2882 | 0.49 | 32000 | 1.2311 |
| 1.2831 | 0.5 | 32500 | 1.2251 |
| 1.2836 | 0.51 | 33000 | 1.2212 |
| 1.2817 | 0.51 | 33500 | 1.2178 |
| 1.2772 | 0.52 | 34000 | 1.2130 |
| 1.2651 | 0.53 | 34500 | 1.2080 |
| 1.2683 | 0.54 | 35000 | 1.2048 |
| 1.2581 | 0.54 | 35500 | 1.1999 |
| 1.263 | 0.55 | 36000 | 1.1972 |
| 1.255 | 0.56 | 36500 | 1.1924 |
| 1.2466 | 0.57 | 37000 | 1.1884 |
| 1.2448 | 0.57 | 37500 | 1.1860 |
| 1.2413 | 0.58 | 38000 | 1.1804 |
| 1.2362 | 0.59 | 38500 | 1.1782 |
| 1.2309 | 0.6 | 39000 | 1.1732 |
| 1.2289 | 0.61 | 39500 | 1.1687 |
| 1.2208 | 0.61 | 40000 | 1.1649 |
| 1.2225 | 0.62 | 40500 | 1.1605 |
| 1.2178 | 0.63 | 41000 | 1.1555 |
| 1.208 | 0.64 | 41500 | 1.1533 |
| 1.2069 | 0.64 | 42000 | 1.1490 |
| 1.206 | 0.65 | 42500 | 1.1453 |
| 1.2013 | 0.66 | 43000 | 1.1414 |
| 1.2003 | 0.67 | 43500 | 1.1374 |
| 1.1867 | 0.67 | 44000 | 1.1337 |
| 1.187 | 0.68 | 44500 | 1.1302 |
| 1.188 | 0.69 | 45000 | 1.1270 |
| 1.179 | 0.7 | 45500 | 1.1237 |
| 1.1866 | 0.71 | 46000 | 1.1204 |
| 1.173 | 0.71 | 46500 | 1.1173 |
| 1.1706 | 0.72 | 47000 | 1.1134 |
| 1.1645 | 0.73 | 47500 | 1.1099 |
| 1.1641 | 0.74 | 48000 | 1.1063 |
| 1.1623 | 0.74 | 48500 | 1.1032 |
| 1.1561 | 0.75 | 49000 | 1.1006 |
| 1.1531 | 0.76 | 49500 | 1.0977 |
| 1.1569 | 0.77 | 50000 | 1.0950 |
| 1.1505 | 0.77 | 50500 | 1.0927 |
| 1.1473 | 0.78 | 51000 | 1.0902 |
| 1.1428 | 0.79 | 51500 | 1.0870 |
| 1.1412 | 0.8 | 52000 | 1.0844 |
| 1.1452 | 0.8 | 52500 | 1.0823 |
| 1.1391 | 0.81 | 53000 | 1.0805 |
| 1.1329 | 0.82 | 53500 | 1.0783 |
| 1.1295 | 0.83 | 54000 | 1.0764 |
| 1.125 | 0.84 | 54500 | 1.0746 |
| 1.1295 | 0.84 | 55000 | 1.0730 |
| 1.1247 | 0.85 | 55500 | 1.0711 |
| 1.1225 | 0.86 | 56000 | 1.0696 |
| 1.1235 | 0.87 | 56500 | 1.0680 |
| 1.1192 | 0.87 | 57000 | 1.0670 |
| 1.1189 | 0.88 | 57500 | 1.0654 |
| 1.1196 | 0.89 | 58000 | 1.0646 |
| 1.1152 | 0.9 | 58500 | 1.0635 |
| 1.1133 | 0.9 | 59000 | 1.0628 |
| 1.1126 | 0.91 | 59500 | 1.0619 |
| 1.1142 | 0.92 | 60000 | 1.0610 |
| 1.1112 | 0.93 | 60500 | 1.0605 |
| 1.1137 | 0.93 | 61000 | 1.0599 |
| 1.1127 | 0.94 | 61500 | 1.0595 |
| 1.1111 | 0.95 | 62000 | 1.0592 |
| 1.1121 | 0.96 | 62500 | 1.0588 |
| 1.1114 | 0.97 | 63000 | 1.0587 |
| 1.1121 | 0.97 | 63500 | 1.0585 |
| 1.1078 | 0.98 | 64000 | 1.0584 |
| 1.1104 | 0.99 | 64500 | 1.0584 |
| 1.1057 | 1.0 | 65000 | 1.0584 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Sourabh2/spaceinvandernoframeship-v2
|
Sourabh2
| 2023-07-03T17:28:00Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T17:26:59Z |
---
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: 229.50 +/- 112.19
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 Sourabh2 -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 Sourabh2 -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 Sourabh2
```
## 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', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
DanialAmin/InsuranceLLM
|
DanialAmin
| 2023-07-03T17:20:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-07-03T17:15:38Z |
---
license: tii-falcon-llm
---
|
hopkins/eng-deu-simcse.dev2.44k
|
hopkins
| 2023-07-03T17:19:14Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T17:05:25Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-simcse.dev2.44k
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. -->
# eng-deu-simcse.dev2.44k
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6420
- Bleu: 21.3894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-delfy
|
hopkins
| 2023-07-03T16:49:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T16:03:32Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-delfy
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. -->
# eng-deu-delfy
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6917
- Bleu: 19.9632
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
felipec23/open-llama-3b
|
felipec23
| 2023-07-03T16:45:32Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-03T16:45:30Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
Wongstein/vide-noir
|
Wongstein
| 2023-07-03T16:39:18Z | 175 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text-generation-inference",
"en",
"dataset:amazon_us_reviews",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-03T16:13:16Z |
---
license: creativeml-openrail-m
datasets:
- amazon_us_reviews
language:
- en
tags:
- text-generation-inference
---
|
Sandrro/text_to_subfunction_v2
|
Sandrro
| 2023-07-03T16:10:33Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-03T13:59:39Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: text_to_subfunction_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text_to_subfunction_v2
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9853
- F1: 0.1413
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.1842 | 1.0 | 2525 | 5.0971 | 0.0271 |
| 4.5066 | 2.0 | 5050 | 4.5376 | 0.0676 |
| 4.0497 | 3.0 | 7575 | 4.2116 | 0.1053 |
| 3.7356 | 4.0 | 10100 | 4.0381 | 0.1318 |
| 3.6168 | 5.0 | 12625 | 3.9853 | 0.1413 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.1.0.dev20230414+cu117
- Datasets 2.9.0
- Tokenizers 0.13.3
|
khalidbutt/k
|
khalidbutt
| 2023-07-03T16:09:24Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-07-03T16:09:24Z |
---
license: bigscience-bloom-rail-1.0
---
|
FabriLluvia/BOT
|
FabriLluvia
| 2023-07-03T16:03:08Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"code",
"fill-mask",
"es",
"en",
"dataset:OpenAssistant/oasst1",
"dataset:fka/awesome-chatgpt-prompts",
"license:apache-2.0",
"region:us"
] |
fill-mask
| 2023-07-03T16:01:17Z |
---
license: apache-2.0
datasets:
- OpenAssistant/oasst1
- fka/awesome-chatgpt-prompts
language:
- es
- en
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: fill-mask
tags:
- code
---
|
dicta-il/BEREL_2.0
|
dicta-il
| 2023-07-03T15:36:58Z | 425 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"he",
"arxiv:2208.01875",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-06-26T22:40:47Z |
---
license: apache-2.0
language:
- he
library_name: transformers
tags:
- bert
---
# Introducing BEREL 2.0 - New and Improved BEREL: BERT Embeddings for Rabbinic-Encoded Language
When using BEREL 2.0, please reference:
Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Eli Handel, Moshe Koppel, "Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language", Aug 2022 [arXiv:2208.01875]
1. Usage:
```python
from transformers import AutoTokenizer, BertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('dicta-il/BEREL_2.0')
model = BertForMaskedLM.from_pretrained('dicta-il/BEREL_2.0')
# for evaluation, disable dropout
model.eval()
```
> NOTE: This code will **not** work and provide bad results if you use `BertTokenizer`. Please use `AutoTokenizer` or `BertTokenizerFast`.
2. Demo site:
You can experiment with the model in a GUI interface here: https://dicta-bert-demo.netlify.app/?genre=rabbinic
- The main part of the GUI consists of word buttons visualizing the tokenization of the sentences. Clicking on a button masks it, and then three BEREL word predictions are shown. Clicking on that bubble expands it to 10 predictions; alternatively, ctrl-clicking on that initial bubble expands to 30 predictions.
- Ctrl-clicking adjacent word buttons combines them into a single token for the mask.
- The edit box on top contains the input sentence; this can be modified at will, and the word-buttons will adjust as relevant.
|
arver/code_gen
|
arver
| 2023-07-03T15:21:13Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-03T15:18:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
dcarpintero/q-Taxi-v3
|
dcarpintero
| 2023-07-03T15:02:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T15:01:48Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dcarpintero/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hopkins/eng-fra-wsample.49
|
hopkins
| 2023-07-03T14:56:11Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T14:37:16Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-wsample.49
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. -->
# eng-fra-wsample.49
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1184
- Bleu: 33.0590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
kresnik/wav2vec2-large-xlsr-korean
|
kresnik
| 2023-07-03T14:55:40Z | 1,123,517 | 38 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"ko",
"dataset:kresnik/zeroth_korean",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ko
datasets:
- kresnik/zeroth_korean
tags:
- speech
- audio
- automatic-speech-recognition
license: apache-2.0
model-index:
- name: 'Wav2Vec2 XLSR Korean'
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Zeroth Korean
type: kresnik/zeroth_korean
args: clean
metrics:
- name: Test WER
type: wer
value: 4.74
- name: Test CER
type: cer
value: 1.78
---
## Evaluation on Zeroth-Korean ASR corpus
[Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb)
```
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import soundfile as sf
import torch
from jiwer import wer
processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean")
model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda')
ds = load_dataset("kresnik/zeroth_korean", "clean")
test_ds = ds['test']
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
test_ds = test_ds.map(map_to_array)
def map_to_pred(batch):
inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
```
### Expected WER: 4.74%
### Expected CER: 1.78%
|
LukeMoore11/Big-Benjamin
|
LukeMoore11
| 2023-07-03T14:44:11Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"en",
"dataset:LukeMoore11/autotrain-data-second-attempt",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-06-21T22:08:19Z |
---
tags:
- summarization
language:
- en
widget:
- text: "Enter legal document..."
datasets:
- LukeMoore11/autotrain-data-second-attempt
co2_eq_emissions:
emissions: 67.54051067286701
---
## Validation Metrics
- Loss: 1.379
- Rouge1: 24.817
- Rouge2: 20.238
- RougeL: 24.044
- RougeLsum: 24.222
|
Phips/q-FrozenLake-v1-4x4-noSlippery
|
Phips
| 2023-07-03T14:42:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T14:42:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Phips/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
TootToot/ppo-LunarLander-v2-v2
|
TootToot
| 2023-07-03T14:19:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T14:19:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.50 +/- 24.77
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
toloka/gpt2-large-supervised-prompt-writing
|
toloka
| 2023-07-03T14:05:32Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-03-13T13:30:02Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gpt2-sweep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-sweep
This model is a fine-tuned version of [gpt2-large](https://huggingface.co/gpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0808
- Accuracy: 0.8556
## 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: 2.294477077303931e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.4827 | 0.19 | 1000 | 2.4565 | 0.8520 |
| 2.6468 | 0.37 | 2000 | 2.3303 | 0.8530 |
| 2.5106 | 0.56 | 3000 | 2.2487 | 0.8537 |
| 2.0732 | 0.74 | 4000 | 2.2020 | 0.8541 |
| 2.159 | 0.93 | 5000 | 2.1594 | 0.8545 |
| 1.856 | 1.12 | 6000 | 2.1518 | 0.8548 |
| 1.9138 | 1.3 | 7000 | 2.1261 | 0.8551 |
| 1.8055 | 1.49 | 8000 | 2.1126 | 0.8552 |
| 2.0385 | 1.67 | 9000 | 2.1008 | 0.8554 |
| 1.9648 | 1.86 | 10000 | 2.0858 | 0.8555 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.0+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
iammartian0/distilhubert-finetuned-gtzan
|
iammartian0
| 2023-07-03T13:52:49Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-03T10:17:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5528
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1578 | 0.99 | 56 | 2.1203 | 0.55 |
| 1.6815 | 2.0 | 113 | 1.6607 | 0.57 |
| 1.2921 | 2.99 | 169 | 1.2421 | 0.64 |
| 1.0324 | 4.0 | 226 | 1.0260 | 0.7 |
| 0.8661 | 4.99 | 282 | 0.8973 | 0.7 |
| 0.6192 | 6.0 | 339 | 0.7420 | 0.79 |
| 0.5437 | 6.99 | 395 | 0.6951 | 0.8 |
| 0.4917 | 8.0 | 452 | 0.6996 | 0.78 |
| 0.3868 | 8.99 | 508 | 0.6648 | 0.81 |
| 0.3816 | 10.0 | 565 | 0.6584 | 0.79 |
| 0.1935 | 10.99 | 621 | 0.6101 | 0.84 |
| 0.128 | 12.0 | 678 | 0.5445 | 0.85 |
| 0.1144 | 12.99 | 734 | 0.5703 | 0.84 |
| 0.0828 | 14.0 | 791 | 0.5632 | 0.83 |
| 0.0928 | 14.87 | 840 | 0.5528 | 0.84 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
renyulin/gpt-neo-1.3b-es-rlhf-step2500-peft
|
renyulin
| 2023-07-03T13:45:51Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-07-03T13:45:49Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="renyulin//tmp/tmpdgiqbda4/renyulin/gpt-neo-1.3b-es-rlhf-step250-peft")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("renyulin//tmp/tmpdgiqbda4/renyulin/gpt-neo-1.3b-es-rlhf-step250-peft")
model = AutoModelForCausalLMWithValueHead.from_pretrained("renyulin//tmp/tmpdgiqbda4/renyulin/gpt-neo-1.3b-es-rlhf-step250-peft")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
juliensimon/autotrain-food101-1471154053
|
juliensimon
| 2023-07-03T13:43:26Z | 210 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"autotrain",
"vision",
"image-classification",
"dataset:juliensimon/autotrain-data-food101",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-09-15T12:42:49Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- juliensimon/autotrain-data-food101
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 179.11544810549532
---
# Usage
```
from transformers import pipeline
p = pipeline("image-classification", model="juliensimon/autotrain-food101-1471154053")
result = p("my_image.jpg")
```
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1471154053
- CO2 Emissions (in grams): 179.1154
## Validation Metrics
- Loss: 0.301
- Accuracy: 0.915
- Macro F1: 0.915
- Micro F1: 0.915
- Weighted F1: 0.915
- Macro Precision: 0.917
- Micro Precision: 0.915
- Weighted Precision: 0.917
- Macro Recall: 0.915
- Micro Recall: 0.915
- Weighted Recall: 0.915
|
dcarpintero/q-FrozenLake-v1-4x4-noSlippery
|
dcarpintero
| 2023-07-03T13:41:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T13:41:06Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dcarpintero/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
AndreNasci/ppo-Huggy
|
AndreNasci
| 2023-07-03T13:26:19Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-03T13:26:09Z |
---
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: AndreNasci/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Khushnur/t5-base-end2end-questions-generation_eli_squad
|
Khushnur
| 2023-07-03T13:17:24Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5_cleaned_datav3_60k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-29T18:54:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- eli5_cleaned_datav3_60k
model-index:
- name: t5-base-end2end-questions-generation_eli_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. -->
# t5-base-end2end-questions-generation_eli_squad
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5_cleaned_datav3_60k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3313
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7426 | 0.26 | 100 | 2.4735 |
| 2.305 | 0.52 | 200 | 2.4169 |
| 2.2034 | 0.78 | 300 | 2.3887 |
| 2.1562 | 1.04 | 400 | 2.3710 |
| 2.0883 | 1.31 | 500 | 2.3574 |
| 2.07 | 1.57 | 600 | 2.3492 |
| 2.0595 | 1.83 | 700 | 2.3433 |
| 2.0337 | 2.09 | 800 | 2.3384 |
| 2.0012 | 2.35 | 900 | 2.3353 |
| 2.0175 | 2.61 | 1000 | 2.3320 |
| 2.0035 | 2.87 | 1100 | 2.3313 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
eskalofi/annchristinkh
|
eskalofi
| 2023-07-03T13:13:24Z | 29 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-03T13:09:57Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### annchristinkh Dreambooth model trained by eskalofi 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:
|
hiepnh/longchat-7b-16k-sharded
|
hiepnh
| 2023-07-03T13:10:23Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-03T10:45:56Z |
a sharded version of https://huggingface.co/lmsys/longchat-7b-16k
|
veluchs/whisper-tiny-us
|
veluchs
| 2023-07-03T13:06:17Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-03T12:43:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-us
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.33943329397874855
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-us
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Wer Ortho: 0.3430
- Wer: 0.3394
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0009 | 17.86 | 500 | 0.6329 | 0.3430 | 0.3394 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
coreml-community/coreml-aZovyaRPGArtistTools-v3VAE_cn
|
coreml-community
| 2023-07-03T13:04:23Z | 0 | 2 | null |
[
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-03T08:30:42Z |
---
license: creativeml-openrail-m
tags:
- coreml
- stable-diffusion
- text-to-image
---
# Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Custom resolution versions are tagged accordingly.
- The `vae-ft-mse-840000-ema-pruned.ckpt` VAE is embedded into the model.
- This model was converted with a `vae-encoder` for use with `image2image`.
- This model is `fp16`.
- Descriptions are posted as-is from original model source.
- Not all features and/or results may be available in `CoreML` format.
- This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).
- This model does not include a `safety checker` (for NSFW content).
- This model can be used with ControlNet.
<br>
# aZovyaRPGArtistTools_v3VAE_cn:
Source(s): [CivitAI](https://civitai.com/models/8124/a-zovya-rpg-artist-tools)
## A-Zovya RPG Artist Tools
A model with professional RPG industry artists in mind. Those working in video games, board and tabletop games as well as concept art and book covers should get good use from this model. The new version 3 packs in more training for creatures and rendering styles.
[Detailed tutorial on how I get the results in the preview images.](https://civitai.com/models/34192/ultra-sharp-high-contrast-tutorial-vaeandupscaler)\
Check here if you're having trouble getting the same results. Initial generation size and VAE usage is key.
You can prompt any style you need with these models, but the default aesthetic is listed for each of the models in this handy list.
## V3+VAE
Same as V3 but with the added convenience of having a preset VAE baked in so you don't need to select that each time. Saves on vram usage and possible NaN errors. Speeds up workflow if that's the VAE you're going to use anyway.
Compared to earlier versions, V3 has a stronger painterly style. High contrast and sharpness. Even more RPG knowledge.
Do you have requests? I've been putting in many more hours lately with this. That's my problem, not yours. But if you'd like to tip me, buy me a beer. Beer encourages me to ignore work and make AI models instead. Tip and make a request. I'll give it a shot if I can. [Here at Ko-Fi](https://ko-fi.com/zovya)<br><br>
|
GeniusVoice/bert-base-dutch-cased-finetuned-gem
|
GeniusVoice
| 2023-07-03T12:58:44Z | 124 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"nl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- nl
tags:
- generated_from_trainer
model_index:
- name: bert-base-dutch-cased-finetuned-gem
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-dutch-cased-finetuned-gem
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8767
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7518 | 1.0 | 2133 | 1.8428 |
| 1.5679 | 2.0 | 4266 | 1.8729 |
| 1.3332 | 3.0 | 6399 | 1.8767 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
|
msladic/Reinforce-Pixelcopter-PLE-v0
|
msladic
| 2023-07-03T12:51:10Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T10:03:43Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.70 +/- 12.40
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
hopkins/mbart-finetuned-eng-ind-longest
|
hopkins
| 2023-07-03T12:45:11Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T12:26:25Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-finetuned-eng-ind-longest
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. -->
# mbart-finetuned-eng-ind-longest
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7474
- Bleu: 21.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/mbart-finetuned-eng-ind-random
|
hopkins
| 2023-07-03T12:44:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T12:26:05Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-finetuned-eng-ind-random
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. -->
# mbart-finetuned-eng-ind-random
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7847
- Bleu: 21.5508
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Tadorne/Political_Parrots
|
Tadorne
| 2023-07-03T12:37:20Z | 0 | 0 | null |
[
"political",
"gpt",
"eu",
"🇪🇺",
"legal",
"text-generation",
"region:us"
] |
text-generation
| 2023-07-02T13:30:44Z |
---
tags:
- political
- gpt
- eu
- 🇪🇺
- legal
pipeline_tag: text-generation
---
# 🗳️ Political Parrots 🦜
This repository contain a fine-tune models of the LLaMA 7B.
These LLM models was trained on the amendments made by the European Parliament's political groups during the 7th, 8th and current legislature.
| Political Group | Number of amendments |
|---------------------|----------------------------------|
| SD | 55 149 |
| EPP | 68 780 |
| Renew | 17 379 |
| ECR | 22 479 |
## An example of the results:
Here is the actual prompt :
> Amend the following text.
> paternity leave means leave from work for fathers to be taken on the occasion of the birth of a child
__The output of the SD model (Socialists & Democrats):__
> paternity leave means leave from work for fathers **, mothers and adoptive parents** to be taken on the occasion of the birth **or adoption** of a child
__The output of the EPP model (European People's Party Group):__
> paternity leave means leave from work **of at least ten working days** for fathers to be taken on the occasion of the birth of a child, **in accordance with national law , in order to enable the father to take care of the child and the mother .**
|
renatoneto14/HuggyTraining
|
renatoneto14
| 2023-07-03T12:29:30Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-03T12:28:24Z |
---
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: renatoneto14/HuggyTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
hopkins/mbart-finetuned-eng-deu-longest
|
hopkins
| 2023-07-03T12:25:56Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T12:06:22Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-finetuned-eng-deu-longest
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. -->
# mbart-finetuned-eng-deu-longest
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6322
- Bleu: 20.9315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
napoleonthethird/wav2vec2-base-libris-prime
|
napoleonthethird
| 2023-07-03T12:21:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-01T13:52:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-libris-prime
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-libris-prime
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 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
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Heitechsoft/FalconAlpaca-7B
|
Heitechsoft
| 2023-07-03T12:17:56Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-30T11:34:06Z |
---
license: apache-2.0
---
# Model Card for FalconAlpaca
<!-- Provide a quick summary of what the model is/does. -->
FalconAlpaca is Falcon-7B trained on the [Stanford Alpaca Dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json)
## Model Details
This model was an attempt to influence the learned outputs of Falcon-7B to adapt the outputs to become more information-rich and focused.
Trained using [Lit GPT](https://github.com/Lightning-AI/lit-gpt), the model took 2 hours to train on 1 4xA6000 node.
### Model Description
- **License:** [Apache 2.0]
- **Finetuned from model :** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b)
### Model Sources
[Stanford Alpaca Dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json)
### Out-of-Scope Use
This model is not intended for anything but testing purposes. There have been no attempts to control/remove bias, toxicity, or any other form of
potentially dangerous or harmful messages.
## Bias, Risks, and Limitations
No effort was made to remove any wrong or harmful information from Falcon-7B or the Alpaca dataset. Any risks and limitations from either of
those datasets/models carry over to this project as well.
## How to Get Started with the Model
Download and install libraries for [Lit GPT](https://github.com/Lightning-AI/lit-gpt)
```sh
python generate/adapter_v2.py \
--adapter_path path/to/model/lit_model_adapter_finetuned.pth \
--checkpoint_dir path/to/model \
--prompt "What temperature should I cook pork at to ensure it is safe?"
```
This uses around 14GB of VRAM. If you need to use less VRAM, you can add the parameters
```
--quantize llm.int8
```
or
```
--quantize gptq.int4
```
### Training Data
[Stanford Alpaca Dataset](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json)
#### Training Hyperparameters
The defaults were as follows
```
learning_rate = 9e-3
batch_size = 32
micro_batch_size = 2
gradient_accumulation_iters = 16
epoch_size = 50000
num_epochs = 5
max_iters = 125000
weight_decay = 0.02
warmup_iters = 50000
```
## More Information
[HeitechSoft](https://heitechsoft.com/blog/heitechsoft-s-falcon-7b-fine-tuned-model-paves-the-way-for-advanced-ai-chatbots)
|
Tverous/gpt-j-claim-generator
|
Tverous
| 2023-07-03T12:15:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"generated_from_trainer",
"dataset:anli",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T01:03:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- anli
metrics:
- rouge
model-index:
- name: gpt-j-claim-generator
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: anli
type: anli
config: plain_text
split: dev_r3
args: plain_text
metrics:
- name: Rouge1
type: rouge
value: 0.8913860940628431
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-j-claim-generator
This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the anli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0232
- Rouge1: 0.8914
- Rouge2: 0.8240
- Rougel: 0.8863
- Rougelsum: 0.8864
## 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: 12
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 36
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.013 | 1.79 | 5000 | 0.0200 | 0.8921 | 0.8194 | 0.8859 | 0.8860 |
| 0.0085 | 3.58 | 10000 | 0.0232 | 0.8914 | 0.8240 | 0.8863 | 0.8864 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
DEplain/trimmed_mbart_sents_apa_web
|
DEplain
| 2023-07-03T12:09:30Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"text simplification",
"plain language",
"easy-to-read language",
"sentence simplification",
"de",
"dataset:DEplain/DEplain-APA-sent",
"dataset:DEplain/DEplain-web-sent",
"arxiv:2305.18939",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-01T14:45:33Z |
---
datasets:
- DEplain/DEplain-APA-sent
- DEplain/DEplain-web-sent
language:
- de
metrics:
- sari
- bleu
- bertscore
library_name: transformers
pipeline_tag: text2text-generation
tags:
- text simplification
- plain language
- easy-to-read language
- sentence simplification
---
# DEplain German Text Simplification
This model belongs to the experiments done at the work of Stodden, Momen, Kallmeyer (2023). ["DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification."](https://arxiv.org/abs/2305.18939) In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada. Association for Computational Linguistics.
Detailed documentation can be found on this GitHub repository [https://github.com/rstodden/DEPlain](https://github.com/rstodden/DEPlain)
### Model Description
The model is a finetuned checkpoint of the pre-trained mBART model `mbart-large-cc25`. With a trimmed vocabulary to the most frequent 30k words in the German language.
The model was finetuned towards the task of German text simplification of sentences.
The finetuning dataset included manually aligned sentences from the datasets `DEplain-APA-sent` and `DEplain-web-sent-manual-open`
|
teoha/openai-whisper-medium-LORA-ja
|
teoha
| 2023-07-03T12:08:06Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-02T20:16:19Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
WinterDP/ppo-Huggy
|
WinterDP
| 2023-07-03T12:05:54Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-03T12:05:51Z |
---
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: WinterDP/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
velascoluis/falcon7b-instruct-database-ft
|
velascoluis
| 2023-07-03T11:50:55Z | 0 | 0 | null |
[
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-07-02T19:45:27Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: falcon7b-instruct-database-ft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon7b-instruct-database-ft
This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4994
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
searde/model-financial-documents-3
|
searde
| 2023-07-03T11:46:05Z | 109 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:searde/dataset-financial-documents-3",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-06-29T08:20:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- searde/dataset-financial-documents-3
metrics:
- rouge
model-index:
- name: tst-summarization
results:
- task:
name: Summarization
type: summarization
dataset:
name: searde/dataset-financial-documents-3 3.0.0
type: searde/dataset-financial-documents-3
config: 3.0.0
split: validation
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 14.9574
---
<!-- 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. -->
# tst-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the searde/dataset-financial-documents-3 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0505
- Rouge1: 14.9574
- Rouge2: 0.0
- Rougel: 8.4517
- Rougelsum: 12.4858
- Gen Len: 63.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ayushutkarsh/t3
|
ayushutkarsh
| 2023-07-03T11:35:55Z | 51 | 6 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"conversational",
"en",
"dataset:McGill-NLP/FaithDial",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-04-02T06:07:50Z |
---
license: apache-2.0
datasets:
- McGill-NLP/FaithDial
language:
- en
metrics:
- bleu
- bertscore
- accuracy
pipeline_tag: conversational
---
T3 stands for Terribly Tiny Transformers that are an efficient way of creating tiny distilled (student) models for hallucination-free LLM models in parameter-constrained environment (edge devices).
The base model is a T3 adaptation of T5 model. The paradigm of T3 models can be extended to all types of models ( encoder only, decoder only & seq2seq)
|
joserodr68/dqn-SpaceInvadersNoFrameskip-v4
|
joserodr68
| 2023-07-03T11:34:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T11:33:53Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 380.50 +/- 203.24
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 joserodr68 -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 joserodr68 -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 joserodr68
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
AMUseBot/roberta-base-cookdial-v1_1
|
AMUseBot
| 2023-07-03T11:31:15Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-17T09:35:53Z |
---
language:
- en
library_name: transformers
tags:
- text-classification
widget:
- text: "What ingredients do I need?"
---
- Baseline NLU model for the "AMUseBot" cooking taskbot prototype. Updated version with more robust req_ingredient intent recognition thanks to finetuning with extra synthetic data.
- ``roberta-base`` model finetuned with default hyperparameters for 7 epochs on intents from the CookDial (https://github.com/YiweiJiang2015/CookDial) dataset with an extra choose_recipe intent added. The ``simpletransformers`` library was used for fine-tuning.
- Intent mapping: {"0": "affirm", "1": "choose_recipe", "2": "confirm", "3": "goodbye", "4": "greeting", "5": "negate", "6": "other", "7": "req_amount", "8": "req_duration", "9": "req_ingredient", "10": "req_ingredient_list", "11": "req_ingredient_list_ends", "12": "req_ingredient_list_length", "13": "req_instruction", "14": "req_is_recipe_finished", "15": "req_is_recipe_ongoing", "16": "req_parallel_action", "17": "req_repeat", "18": "req_start", "19": "req_substitute", "20": "req_temperature", "21": "req_title", "22": "req_tool", "23": "req_use_all", "24": "thank"}.
|
Shularp/TestHelsinkimulEnJpTh
|
Shularp
| 2023-07-03T11:25:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-03T10:03:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: TestHelsinkimulEnJpTh
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. -->
# TestHelsinkimulEnJpTh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6825
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7413 | 1.0 | 4846 | 1.0482 |
| 0.8773 | 2.0 | 9692 | 0.8856 |
| 0.8138 | 3.0 | 14538 | 0.7699 |
| 0.531 | 4.0 | 19384 | 0.7021 |
| 0.2739 | 5.0 | 24230 | 0.6825 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
zijun/autotrain-input_list-71788138727
|
zijun
| 2023-07-03T11:19:37Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:zijun/autotrain-data-input_list",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-03T11:19:08Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- zijun/autotrain-data-input_list
co2_eq_emissions:
emissions: 0.20160817247860105
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 71788138727
- CO2 Emissions (in grams): 0.2016
## Validation Metrics
- Loss: 0.261
- Accuracy: 0.882
- Precision: 0.926
- Recall: 0.926
- AUC: 0.931
- F1: 0.926
## 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/zijun/autotrain-input_list-71788138727
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("zijun/autotrain-input_list-71788138727", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("zijun/autotrain-input_list-71788138727", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
GretaClementi96/blip2-opt-2.7b-inbreast-flip-captions-adapters
|
GretaClementi96
| 2023-07-03T11:13:25Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-02T14:09:50Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
tomar840/falcon-7b-math-reasoning
|
tomar840
| 2023-07-03T11:09:24Z | 0 | 0 | null |
[
"arxiv:2106.09685",
"arxiv:2305.14314",
"region:us"
] | null | 2023-07-03T11:04:01Z |
datasets:
- OpenAssistant/oasst1
pipeline_tag: text-generation
license: apache-2.0
---
# 🚀 Falcon-7b-chat-oasst1
Falcon-7b-chat-oasst1 is a chatbot-like model for dialogue generation. It was built by fine-tuning [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) on the [openai/prm800k](https://github.com/openai/prm800k) dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗's [peft](https://github.com/huggingface/peft) package.
## Model Summary
- **Model Type:** Causal decoder-only
- **Language(s):** English
- **Base Model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) (License: [Apache 2.0](https://huggingface.co/tiiuae/falcon-7b#license))
- **Dataset:** [openai/prm800k](https://github.com/openai/prm800k)
- **License(s):** Apache 2.0 inherited from "Base Model" and "Dataset"
## Model Details
The model was fine-tuned in 8-bit precision using 🤗 `peft` adapters, `transformers`, and `bitsandbytes`. Training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant.
### Model Date
July 3, 2023
## Quick Start
To prompt the chat model, use the following format:
```
<human>: [Instruction]
<bot>:
```
### Example Dialogue 1
**Prompter**:
```
"""<human>: A positive integer $X$ is 2 more than a multiple of 3. Its units digit is the same as the units digit of a number that is 4 more than a multiple of 5. What is the smallest possible value of $X$?
<bot>:"""
```
**Falcon-7b-chat-oasst1**:
```
I need to find a positive integer $X$ that satisfies two conditions: $X$ is 2 more than a multiple of 3, and its units digit is the same as the units digit of a number that is 4 more than a multiple of 5.
I can start by listing some multiples of 3 and their units digits: 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75, 78, 81, 84, 87, 90, 93, 96, 99.
I can also list some multiples of 5 and their units digits: 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.
I notice that the units digit of a multiple of 3 is always 3, and the units digit of a multiple of 5 is always 5.
This means that the units digit of $X$ must be 3, and the units digit of $X$ must be 5.
The only multiple of 3 that is 2 more than a multiple of 3 is 6, and the only multiple of 5 that is 4 more than a multiple of 5 is 19.
Therefore, the smallest possible value of $X$ is 6.
# Answer
6
```
|
mcamara/taxi-v3
|
mcamara
| 2023-07-03T11:04:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T10:47:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="mcamara/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
lx865712528/master-base-pretrained-msmarco
|
lx865712528
| 2023-07-03T11:04:17Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"feature-extraction",
"en",
"dataset:ms_marco",
"arxiv:2212.07841",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-07-03T10:19:24Z |
---
license: mit
datasets:
- ms_marco
language:
- en
pipeline_tag: feature-extraction
---
# MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers
Paper: [https://arxiv.org/abs/2212.07841](https://arxiv.org/abs/2212.07841).
Code: [https://github.com/microsoft/SimXNS/tree/main/MASTER](https://github.com/microsoft/SimXNS/tree/main/MASTER).
## Overview
This is the checkpoint after pretraining on the MS-MARCO corpus. **You may use this checkpoint as the initialization for finetuning.**
## Useage
To load this checkpoint for initialization, you may follow:
```python
from transformers import AutoModel
model = AutoModel.from_pretrained('lx865712528/master-base-pretrained-msmarco')
```
|
AhmedTaha012/gptneo-TxtToJson-v0.1.18
|
AhmedTaha012
| 2023-07-03T10:59:40Z | 116 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-03T09:38:10Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gptneo-TxtToJson-v0.1.18
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. -->
# gptneo-TxtToJson-v0.1.18
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1669
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0001 | 1.0 | 219 | 0.9411 |
| 0.7298 | 2.0 | 438 | 0.7063 |
| 0.4617 | 3.0 | 657 | 0.4989 |
| 0.3366 | 4.0 | 876 | 0.3333 |
| 0.2295 | 5.0 | 1095 | 0.2569 |
| 0.1593 | 6.0 | 1314 | 0.2146 |
| 0.1611 | 7.0 | 1533 | 0.1986 |
| 0.0983 | 8.0 | 1752 | 0.1866 |
| 0.0933 | 9.0 | 1971 | 0.1776 |
| 0.0794 | 10.0 | 2190 | 0.1742 |
| 0.0743 | 11.0 | 2409 | 0.1734 |
| 0.0459 | 12.0 | 2628 | 0.1690 |
| 0.0386 | 13.0 | 2847 | 0.1681 |
| 0.0261 | 14.0 | 3066 | 0.1673 |
| 0.0209 | 15.0 | 3285 | 0.1669 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
boleklolek/olka
|
boleklolek
| 2023-07-03T10:42:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-03T10:37:51Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### olka Dreambooth model trained by boleklolek 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:
|
fidhal-ad/liar-deberta-v3-base_first_attempt
|
fidhal-ad
| 2023-07-03T10:37:21Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-06-15T15:43:51Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: liar-deberta-v3-base_first_attempt
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. -->
# liar-deberta-v3-base_first_attempt
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6722
- Accuracy: 0.2705
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7307 | 1.0 | 642 | 1.6722 | 0.2705 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3
|
MOHIT8082/my-pet-dog-sbs
|
MOHIT8082
| 2023-07-03T10:12:22Z | 10 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-03T10:07:31Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-SBS Dreambooth model trained by MOHIT8082 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: CVRGU236
Sample pictures of this concept:
.jpg)
|
ZidanSink/Kayessss
|
ZidanSink
| 2023-07-03T10:11:35Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-03T10:09:49Z |
---
license: creativeml-openrail-m
---
|
pavanpankaj/incre-train-addlayers
|
pavanpankaj
| 2023-07-03T10:11:17Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"RefinedWebModel",
"custom_code",
"region:us"
] | null | 2023-07-03T09:54:35Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
ecwk/distilbert-git-commits-bugfix-classification
|
ecwk
| 2023-07-03T10:09:49Z | 103 | 1 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-03T10:08:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-git-commits-bugfix-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-git-commits-bugfix-classification
This model is a fine-tuned version of [neuralsentry/distilbert-git-commits-mlm](https://huggingface.co/neuralsentry/distilbert-git-commits-mlm) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5037
- Accuracy: 0.9231
- Precision: 0.85
- Recall: 1.0
- F1: 0.9189
- Roc Auc: 0.9318
## 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: 16
- eval_batch_size: 16
- seed: 420
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.6837 | 1.0 | 22 | 0.6040 | 0.5897 | 0.5161 | 0.9412 | 0.6667 | 0.6297 |
| 0.3852 | 2.0 | 44 | 0.2881 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
| 0.2148 | 3.0 | 66 | 0.3807 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
| 0.0701 | 4.0 | 88 | 0.4934 | 0.8718 | 0.7727 | 1.0 | 0.8718 | 0.8864 |
| 0.0164 | 5.0 | 110 | 0.4892 | 0.8974 | 0.8095 | 1.0 | 0.8947 | 0.9091 |
| 0.0039 | 6.0 | 132 | 0.4929 | 0.8974 | 0.8095 | 1.0 | 0.8947 | 0.9091 |
| 0.0012 | 7.0 | 154 | 0.4065 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
| 0.0008 | 8.0 | 176 | 0.4837 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
| 0.0007 | 9.0 | 198 | 0.5000 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
| 0.0006 | 10.0 | 220 | 0.5037 | 0.9231 | 0.85 | 1.0 | 0.9189 | 0.9318 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
sarthak101/my-pet-dog
|
sarthak101
| 2023-07-03T10:03:13Z | 0 | 0 | null |
[
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-07-03T09:56:02Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by sarthak101 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: CVRGU313
Sample pictures of this concept:
.jpeg)
.jpeg)
.jpeg)
.jpeg)
|
Abhishek1609/my-pet-dog-sbs
|
Abhishek1609
| 2023-07-03T09:57:16Z | 6 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-03T09:52:44Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-SBS Dreambooth model trained by Abhishek1609 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: CVRGU243
Sample pictures of this concept:
.jpg)
|
KJan05/KJan-Taxi-v3
|
KJan05
| 2023-07-03T09:55:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-03T09:55:33Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: KJan-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="KJan05/KJan-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.