semvec/pypi-packages huggingface dataset:
sbx-rl 0.25.0
Jax version of Stable Baselines, implementations of reinforcement learning algorithms.
License: MIT
Requires Python: >=3.10
Keywords: reinforcement-learning-algorithms reinforcement-learning machine-learning gym gymnasium jax openai stable baselines toolbox python data-science



# Stable Baselines Jax (SB3 + JAX = SBX)

See https://github.com/araffin/sbx

Proof of concept version of [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3) in Jax.

Implemented algorithms:
- [Soft Actor-Critic (SAC)](https://arxiv.org/abs/1801.01290) and [SAC-N](https://arxiv.org/abs/2110.01548)
- [Truncated Quantile Critics (TQC)](https://arxiv.org/abs/2005.04269)
- [Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)](https://openreview.net/forum?id=xCVJMsPv3RT)
- [Proximal Policy Optimization (PPO)](https://arxiv.org/abs/1707.06347)
- [Deep Q Network (DQN)](https://arxiv.org/abs/1312.5602)
- [Twin Delayed DDPG (TD3)](https://arxiv.org/abs/1802.09477)
- [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971)
- [Batch Normalization in Deep Reinforcement Learning (CrossQ)](https://openreview.net/forum?id=PczQtTsTIX)
- [Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning (SimBa)](https://openreview.net/forum?id=jXLiDKsuDo)

## Example

```python
from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ

model = TQC("MlpPolicy", "Pendulum-v1", verbose=1)
model.learn(total_timesteps=10_000, progress_bar=True)
avianinsight 2025.12.21175643
A new package that analyzes user-provided text descriptions of bird behaviors and returns structured insights about potential bird consciousness indicators, such as problem-solving, social interaction
License: MIT
Requires Python: >=3.9

# Avian Insight
[![PyPI version](https://badge.fury.io/py/avianinsight.svg)](https://badge.fury.io/py/avianinsight)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/avianinsight)](https://pepy.tech/project/avianinsight)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

A package that extracts insights on bird behavior from text descriptions using pattern-matched LLM responses.

## Installation
```bash
pip install avianinsight
```

## Usage
```python
from avianinsight import avianinsight

response = avianinsight(user_input, api_key="your_api_key", llm=your_llm_instance)
```

## Parameters
- `user_input`: str - the user input text to process
- `llm`: Optional[BaseChatModel] - the langchain llm instance to use, defaults to ChatLLM7
- `api_key`: Optional[str] - the api key for llm7, defaults to LLM7_API_KEY environment variable or "None"

## Using alternative LLM instances
You can pass your own llm instance (based on [https://docs.langchain.com/](https://docs.langchain.com/)) by passing it like `avianinsight(user_input, llm=their_llm_instance)`, for example:
```python
from langchain_openai import ChatOpenAI from avianinsight import avianinsight
llm = ChatOpenAI()
response = avianinsight(user_input, llm=llm)
```
```python
from langchain_anthropic import ChatAnthropic from avianinsight import avianinsight
llm = ChatAnthropic()
response = avianinsight(user_input, llm=llm)
```
```python
from langchain_google_genai import ChatGoogleGenerativeAI from avianinsight import avianinsight
llm = ChatGoogleGenerativeAI()
response = avianinsight(user_input, llm=llm)
```
## API Key Limitations
The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits for LLM7, you can pass your own api_key via environment variable LLM7_API_KEY or via passing it directly like `avianinsight(user_input, api_key="your_api_key"`.

## Contributing
If you encounter any issues or have suggestions for improvement, please report them on the GitHub issues page: https://github.com/chigwell/avianinsight

## Author
* **Eugene Evstafev** - hi@euegne.plus
* **GitHub:** chigwell
headline-parser 2025.12.21175317
A new package that processes news headlines or short text snippets to generate structured summaries of current events. It uses an LLM to extract key entities, topics, and sentiment, ensuring the outpu
License: MIT
Requires Python: >=3.9

# Headline Parser
[![PyPI version](https://badge.fury.io/py/headline-parser.svg)](https://badge.fury.io/py/headline-parser)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/headline-parser)](https://pepy.tech/project/headline-parser)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A package to process news headlines or short text snippets, extracting key entities, topics, and sentiment, and outputting structured summaries in a consistent format.

## Installation

```bash
pip install headline_parser
```

## Overview

This package uses a large language model (LLM) to process input text and generate structured summaries of current events. It's suitable for:
- News aggregation
- Content moderation analysis
- Tracking public discourse on free speech and regulatory issues
- Applications not requiring raw media files

## Example usage

```python
from headline_parser import headline_parser

response = headline_parser(
    user_input="Us Democrats pass a health care reform bill.",
)
print(response)
```

The `headline_parser` function can take in optional arguments:
- `llm`: an instance of `langchain_core.language_models.BaseChatModel`, which defaults to `langchain_llm7.ChatLLM7`
- `api_key`: an optional API key for LLM7, which defaults to the `LLM7_API_KEY` environment variable

You can pass your own LLM instance from other providers:

```python
# Using OpenAI
from langchain_openai import ChatOpenAI
from headline_parser import headline_parser

llm = ChatOpenAI()
response = headline_parser("Your news headline", llm=llm)
print(response)

# Using Anthropic
from langchain_anthropic import ChatAnthropic
from headline_parser import headline_parser

llm = ChatAnthropic()
response = headline_parser("Your news headline", llm=llm)
print(response)

# Using Google
from langchain_google_genai import ChatGoogleGenerativeAI
from headline_parser import headline_parser

llm = ChatGoogleGenerativeAI()
response = headline_parser("Your news headline", llm=llm)
print(response)
```

For higher rate limits with LLM7, you can obtain a free API key by registering at https://token.llm7.io/ and pass it either as an environment variable or directly to the function:

```python
from headline_parser import headline_parser

# Via environment variable
os.environ["LLM7_API_KEY"] = "your_api_key"

# Or directly
response = headline_parser("Your news headline", api_key="your_api_key")
print(response)
```

## Contributing

Found an issue or have a feature request? Please report them on [GitHub](https://github.com/chigwell/headline-parser/issues).

## Author

Eugene Evstafev (hi@eugene.plus)