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Aratako/Qwen1.5-MoE-2x72B | Aratako | 2024-03-26T16:19:21Z | 6 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"moe",
"conversational",
"custom_code",
"en",
"base_model:Qwen/Qwen1.5-72B-Chat",
"base_model:merge:Qwen/Qwen1.5-72B-Chat",
"base_model:abacusai/Liberated-Qwen1.5-72B",
"base_model:merge:abacusai/Liberated-Qwen1.5-72B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-24T14:13:33Z | ---
base_model:
- Qwen/Qwen1.5-72B-Chat
- abacusai/Liberated-Qwen1.5-72B
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
language:
- en
tags:
- mergekit
- merge
- moe
---
# Qwen1.5-MoE-2x72B
## Description
This model is created using MoE (Mixture of Experts) through mergekit based on [Qwen/Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat) and [abacusai/Liberated-Qwen1.5-72B](https://huggingface.co/abacusai/Liberated-Qwen1.5-72B) without further FT.
It utilizes a customized script for MoE via mergekit, which is available [here](https://github.com/Aratako/mergekit-qwen2).
Due to the structural modifications introduced by MoE, the use of this model requires [custom modeling file](https://huggingface.co/Aratako/Liberated-Qwen1.5-2x72B/blob/main/modeling_qwen2.py) and [custom configuration file](https://huggingface.co/Aratako/Liberated-Qwen1.5-2x72B/blob/main/configuration_qwen2.py).
When using the model, please place these files in the same folder as the model.
This model inherits the the [tongyi-qianwen license](https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE).
## Benchmark
The benchmark score of the [mt-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) for this model and the two base models are as follows:
**1-turn, 4-bit quantization**
|Model|Size|Coding|Extraction|Humanities|Math|Reasoning|Roleplay|STEM|Writing|avg_score|
|---|---|---|---|---|---|---|---|---|---|---|
| Liberated-Qwen1.5-72B | 72B | **5.8** | 7.9 | 9.6 | 6.7 | 7.0 | **9.05** | 9.55 | **9.9** | 8.1875 |
| Qwen1.5-72B-Chat | 72B | 5.5 | **8.7** | 9.7 | **8.4** | 7.5 | 9.0 | 9.45 | 9.75 | **8.5000** |
| This model | 2x72B | 5.6 | 7.8 | **9.75** | 7.0 | **8.1** | 9.0 | **9.65** | 9.8 | 8.3375 |
**2-turn, 4-bit quantization**
|Model|Size|Coding|Extraction|Humanities|Math|Reasoning|Roleplay|STEM|Writing|avg_score|
|---|---|---|---|---|---|---|---|---|---|---|
| Liberated-Qwen1.5-72B | 72B | 3.9 | 8.2 | **10.0** | 5.7 | 5.5 | 8.4 | 8.7 | 8.6 | 7.3750 |
| Qwen1.5-72B-Chat | 72B | **5.2** | 8.8 | **10.0** | **6.1** | 6.7 | 9.0 | **9.8** | **9.5** | 8.1375 |
| This model | 2x72B | 5.0 | **9.5** | 9.9 | 5.6 | **8.1** | **9.3** | 9.6 | 9.2 | **8.2750** |
## Merge config
[mergekit_config.yml](./mergekit_moe_config.yml)
```yaml
base_model: ./Qwen1.5-72B-Chat
gate_mode: random
dtype: bfloat16
experts:
- source_model: ./Qwen1.5-72B-Chat
positive_prompts: []
- source_model: ./Liberated-Qwen1.5-72B
positive_prompts: []
tokenizer_source: model:./Qwen1.5-72B-Chat
```
## Gratitude
- Huge thanks to [Alibaba Cloud Qwen](https://www.alibabacloud.com/solutions/generative-ai/qwen) for training and publishing the weights of Qwen model
- Thank you to [abacusai](https://huggingface.co/abacusai) for publishing fine-tuned model from Qwen
- And huge thanks to [mlabonne](https://huggingface.co/mlabonne), as I customized modeling file using [phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference |
omarmomen/structroberta_sx_final | omarmomen | 2024-03-26T16:18:01Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"custom_code",
"en",
"dataset:omarmomen/babylm_10M",
"arxiv:2310.20589",
"license:mit",
"autotrain_compatible",
"region:us"
]
| fill-mask | 2023-07-18T00:30:59Z | ---
license: mit
datasets:
- omarmomen/babylm_10M
language:
- en
metrics:
- perplexity
library_name: transformers
---
# Model Card for omarmomen/structroberta_sx_final
This model is part of the experiments in the published paper at the BabyLM workshop in CoNLL 2023.
The paper titled "Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building" (https://aclanthology.org/2023.conll-babylm.29/)
<strong>omarmomen/structroberta_sx_final</strong> is a modification on the Roberta Model to incorporate syntactic inductive bias using an unsupervised parsing mechanism.
This model variant places the parser network ahead of all attention blocks, and increase the number of convolution layers from 4 to 6.
The model is pretrained on the BabyLM 10M dataset using a custom pretrained RobertaTokenizer (https://huggingface.co/omarmomen/babylm_tokenizer_32k).
https://arxiv.org/abs/2310.20589 |
omarmomen/structroberta_sx2_final | omarmomen | 2024-03-26T16:17:32Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"custom_code",
"en",
"dataset:omarmomen/babylm_10M",
"arxiv:2310.20589",
"license:mit",
"autotrain_compatible",
"region:us"
]
| fill-mask | 2023-08-12T13:54:47Z | ---
license: mit
datasets:
- omarmomen/babylm_10M
language:
- en
metrics:
- perplexity
library_name: transformers
---
# Model Card for omarmomen/structroberta_sx2_final
This model is part of the experiments in the published paper at the BabyLM workshop in CoNLL 2023.
The paper titled "Increasing The Performance of Cognitively Inspired Data-Efficient Language Models via Implicit Structure Building" (https://aclanthology.org/2023.conll-babylm.29/)
<strong>omarmomen/structroberta_sx2_final</strong> is a modification of the Roberta Model to incorporate syntactic inductive bias using an unsupervised parsing mechanism.
This model variant places the parser network after 4 attention blocks and increases the number of convolution layers in the parser network from 4 to 6.
The model is pretrained on the BabyLM 10M dataset using a custom pretrained RobertaTokenizer (https://huggingface.co/omarmomen/babylm_tokenizer_32k).
https://arxiv.org/abs/2310.20589 |
SolaireOfTheSun/openchat_3.5-DHBW-Bio-Deutsch-EducationAID-2Epochen | SolaireOfTheSun | 2024-03-26T16:16:29Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"openchat",
"C-RLFT",
"conversational",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:imone/OpenOrca_FLAN",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Verified-Camel",
"dataset:tiedong/goat",
"dataset:glaiveai/glaive-code-assistant",
"dataset:meta-math/MetaMathQA",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:TIGER-Lab/MathInstruct",
"arxiv:2309.11235",
"arxiv:2303.08774",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T16:10:53Z | ---
license: apache-2.0
tags:
- openchat
- mistral
- C-RLFT
datasets:
- openchat/openchat_sharegpt4_dataset
- imone/OpenOrca_FLAN
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
- tiedong/goat
- glaiveai/glaive-code-assistant
- meta-math/MetaMathQA
- OpenAssistant/oasst_top1_2023-08-25
- TIGER-Lab/MathInstruct
library_name: transformers
pipeline_tag: text-generation
---
# OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
<p align="center">
<a href="https://github.com/imoneoi/openchat">GitHub Repo</a> •
<a href="https://openchat.team">Online Demo</a> •
<a href="https://discord.gg/pQjnXvNKHY">Discord</a> •
<a href="https://twitter.com/imonenext">Twitter</a> •
<a href="https://huggingface.co/openchat">Huggingface</a> •
<a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a>
</p>
**🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥**
**🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖**
<div align="center" style="justify-content: center; align-items: center; "'>
<img src="https://github.com/alpayariyak/openchat/blob/master/assets/3.5-benchmarks.png?raw=true" style="width: 100%; border-radius: 0.5em">
</div>
OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision.
[](https://zenodo.org/badge/latestdoi/645397533)
## Usage
To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command.
Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience.
If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server.
<details>
<summary>Example request (click to expand)</summary>
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_3.5",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
```
Coding Mode
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_3.5",
"condition": "Code",
"messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}]
}'
```
</details>
| Model | Size | Context | Weights | Serving |
|--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
| OpenChat 3.5 | 7B | 8192 | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` |
For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below.
<details>
<summary>Conversation templates (click to expand)</summary>
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Coding Mode
tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
```
</details>
The GPT4 template is also available as the integrated `tokenizer.chat_template`,
which can be used instead of manually specifying the template:
```python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
## Comparison with [X.AI Grok models](https://x.ai/)
Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok?
Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡
(Written by OpenChat 3.5, with a touch of humor and wit.)
| | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k |
|--------------|-------------|---------|----------|------|-----------|----------|----------|
| OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** |
| Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 |
| Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 |
## <a id="benchmarks"></a> Benchmarks
| Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K |
|--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
| OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** |
| ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 |
| | | | | | | | | | | |
| OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 |
| OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 |
| Zephyr-β^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 |
| Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 |
| Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 |
| | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
*: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data.
**: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks).
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
**Safety**
OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.
## License
Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.
## Dataset Details
OpenChat 3.5 was trained with C-RLFT on a collection of publicly available high-quality instruction data, with a custom processing pipeline. We detail some notable subsets included here:
- [OpenChat ShareGPT](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset)
- [Open-Orca with FLAN answers](https://huggingface.co/datasets/imone/OpenOrca_FLAN)
- Capybara [1](https://huggingface.co/datasets/LDJnr/Pure-Dove) [2](https://huggingface.co/datasets/LDJnr/Verified-Camel) [3](https://huggingface.co/datasets/LDJnr/LessWrong-Amplify-Instruct)
- [GOAT](https://huggingface.co/datasets/tiedong/goat)
- [Glaive](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
- [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
- [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- [OpenAssistant](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
## Citation
```
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
```
## 💌 Main Contributor
* Wang Guan [[email protected]], Cheng Sijie [[email protected]], LDJ
* We look forward to hearing you and collaborating on this exciting project! |
cherryboi/gemma2b-it-dutch-v1 | cherryboi | 2024-03-26T16:14:06Z | 149 | 1 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"nl",
"dataset:BramVanroy/alpaca-cleaned-dutch",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T09:20:50Z | ---
library_name: transformers
datasets:
- BramVanroy/alpaca-cleaned-dutch
language:
- nl
license: cc-by-nc-4.0
---
## gemma2b-it-dutch-v1
## Model description
Really shitty finetune of [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) using [BramVanroy/alpaca-cleaned-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch).
## Intended uses & limitations
Its finetuned on [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) which is an english model. However because gemma has a large vocab size i thought it to be interessting to see if it could learn dutch.
## Training and evaluation data
Trained on [BramVanroy/alpaca-cleaned-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) dataset.
Therefore, commercial use of this model is forbidden. The model is intended for research purposes only.
## Training procedure
Trained with QLoRA and merged before upload.
### Training hyperparameters
idk anymore tbh (i think it has been trained on 400 steps) |
DracolIA/BERT-Context-based-QA | DracolIA | 2024-03-26T16:11:55Z | 180 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:rajpurkar/squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-03-21T07:38:27Z | ---
license: apache-2.0
datasets:
- rajpurkar/squad
language:
- en
metrics:
- bertscore
---
## BERT-Context-based-QA
**Original Author**: [BastienHot](https://github.com/BastienHot)<br>
**Date created**: 2024/03/21 <br>
**Datasets**: SQuad |
TahaIqbal/designgen3-0 | TahaIqbal | 2024-03-26T16:11:43Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2024-03-26T16:07:51Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### designGen3.0 Dreambooth model trained by TahaIqbal 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:
|
SolaireOfTheSun/openchat_3.5-DHBW-Bio-Deutsch-EducationAID-2Epochen-adapters | SolaireOfTheSun | 2024-03-26T16:10:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-26T16:10:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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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).
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|
kevinpro/MistralMathOctopus-7B | kevinpro | 2024-03-26T16:07:19Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"multilingual",
"arxiv:2401.06838",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T08:17:59Z | ---
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- multilingual
---
## MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
**🔥Our paper**
https://arxiv.org/abs/2401.06838
**🔥Github Project**
https://github.com/NJUNLP/MAPO
**🔥Open Multilingual Reasoning Leaderboard**
https://huggingface.co/spaces/kevinpro/Open-Multilingual-Reasoning-Leaderboard
## Benchmarks
| System | [MSVAMP](https://huggingface.co/datasets/Mathoctopus/MSVAMP) | [MGSM](https://huggingface.co/datasets/juletxara/mgsm) | [MNumGLUESub](https://huggingface.co/datasets/Mathoctopus/MSVAMP) |
| ------ | :-------------------------------------------------------------: | :------------------------------------------------------: | :-------------------------------------------------------------: |
| GPT-3.5-Turbo | 46.6 | 42.2 | 49.4 |
| [MAmmoTH 7B](https://huggingface.co/TIGER-Lab/MAmmoTH-7B) | 26.3 | 21.3 | 24.2 |
| [WizardMath 7B](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) | 32.5 | 23.0 | 28.7 |
| [MetaMath 7B](https://huggingface.co/meta-math/MetaMath-7B-V1.0) | 46.2 | 37.0 | 43.2 |
| [QAlign 7B](https://huggingface.co/Wenhao97/QAlign-MetaMathQA-7B) | 57.2 | 49.6 | - |
| [MathOctopus 7B](https://huggingface.co/Mathoctopus/Parallel_7B) | 41.2 | 39.5 | 37.1 |
| **[+ MAPO-DPO(ours)🔥](https://huggingface.co/kevinpro/MathOctopus-MAPO-DPO-7B)** | **57.4** | **41.6** | **50.4** |
| [MetaMathOctopus 7B](https://huggingface.co/kevinpro/MetaMathOctopus-7B) | 53.0 | 45.5 | 39.2 |
| **[+ MAPO-DPO(ours) 👑](https://huggingface.co/kevinpro/MetaMathOctopus-MAPO-DPO-7B)** | **64.7** | **51.6** | **52.9** |
| [MistralMathOctopus 7B](https://huggingface.co/kevinpro/MistralMathOctopus-7B) | 59.0 | 58.0 | 56.8 |
| **[+ MAPO-DPO(ours) 👑](https://huggingface.co/kevinpro/MistralMathOctopus-MAPO-DPO-7B)** | **74.6** | **67.3** | **70.0** |
| System | [MSVAMP](https://huggingface.co/datasets/Mathoctopus/MSVAMP) | [MGSM](https://huggingface.co/datasets/juletxara/mgsm) | [MNumGLUESub](https://huggingface.co/datasets/Mathoctopus/MSVAMP) |
| ------ | :-------------------------------------------------------------: | :------------------------------------------------------: | :-------------------------------------------------------------: |
| GPT-3.5-Turbo | 46.6 | 42.2 | 49.4 |
| [MAmmoTH 13B](https://huggingface.co/TIGER-Lab/MAmmoTH-13B) | 38.6 | 28.9 | 29.5 |
| [WizardMath 13B](https://huggingface.co/WizardLM/WizardMath-13B-V1.1) | 35.7 | 28.3 | 29.0 |
| [MetaMath 13B](https://huggingface.co/meta-math/MetaMath-13B-V1.0) | 46.2 | 43.9 | 43.3 |
| [QAlign 13B](https://huggingface.co/Wenhao97/QAlign-MetaMathQA-13B) | 62.6 | 57.1 | - |
| [MathOctopus 13B](https://huggingface.co/Mathoctopus/Parallel_13B) | 51.8 | 46.0 | 40.3 |
| **[+ MAPO-DPO(ours)🔥](https://huggingface.co/kevinpro/MathOctopus-MAPO-DPO-13B)** | **60.1** | **48.5** | **53.8** |
| [MetaMathOctopus 13B](https://huggingface.co/kevinpro/MetaMathOctopus-13B) | 56.3 | 51.4 | 49.5 |
| **[+ MAPO-DPO(ours) 👑](https://huggingface.co/kevinpro/MetaMathOctopus-MAPO-DPO-13B)** | **67.0** | **58.0** | **59.8** |
## Citation
If you find this model helpful, feel free to cite our paper:
```
@misc{she2024mapo,
title={MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization},
author={Shuaijie She and Wei Zou and Shujian Huang and Wenhao Zhu and Xiang Liu and Xiang Geng and Jiajun Chen},
year={2024},
eprint={2401.06838},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
pippinnie/finetuned-t5-cnn_dailymail | pippinnie | 2024-03-26T16:06:09Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T15:59:23Z | ---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: finetuned-t5-cnn_dailymail
results:
- task:
name: Summarization
type: summarization
dataset:
name: cnn_dailymail 3.0.0
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 33.5809
---
<!-- 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-t5-cnn_dailymail
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the cnn_dailymail 3.0.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0404
- Rouge1: 33.5809
- Rouge2: 16.338
- Rougel: 27.0894
- Rougelsum: 31.8225
- Gen Len: 57.48
## 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 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
fjodorshevchenko/bert-finetuned-ner | fjodorshevchenko | 2024-03-26T16:03:14Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-26T15:53:58Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0546
- Precision: 0.9336
- Recall: 0.9517
- F1: 0.9426
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0753 | 1.0 | 1756 | 0.0617 | 0.9119 | 0.9386 | 0.9250 | 0.9829 |
| 0.0345 | 2.0 | 3512 | 0.0627 | 0.9324 | 0.9473 | 0.9398 | 0.9853 |
| 0.0214 | 3.0 | 5268 | 0.0546 | 0.9336 | 0.9517 | 0.9426 | 0.9870 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
zelus82/JuliusCesar-72B-BeyonderV.0 | zelus82 | 2024-03-26T16:00:03Z | 46 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:davidkim205/Rhea-72b-v0.5",
"base_model:merge:davidkim205/Rhea-72b-v0.5",
"base_model:migtissera/Tess-72B-v1.5b",
"base_model:merge:migtissera/Tess-72B-v1.5b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T15:13:18Z | ---
base_model:
- migtissera/Tess-72B-v1.5b
- davidkim205/Rhea-72b-v0.5
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# zelus
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [migtissera/Tess-72B-v1.5b](https://huggingface.co/migtissera/Tess-72B-v1.5b)
* [davidkim205/Rhea-72b-v0.5](https://huggingface.co/davidkim205/Rhea-72b-v0.5)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: davidkim205/Rhea-72b-v0.5
layer_range: [0, 40]
- model: migtissera/Tess-72B-v1.5b
layer_range: [0, 40]
# or, the equivalent models: syntax:
# models:
# - model: psmathur/orca_mini_v3_13b
# - model: garage-bAInd/Platypus2-13B
merge_method: slerp
base_model: davidkim205/Rhea-72b-v0.5
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
``` |
joachimsallstrom/aetherverse_xl | joachimsallstrom | 2024-03-26T15:57:04Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-03-26T15:46:16Z | ---
license: creativeml-openrail-m
---
|
OrdalieTech/Solon-embeddings-base-0.1 | OrdalieTech | 2024-03-26T15:54:17Z | 1,338 | 13 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"fr",
"en",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-11-21T15:37:54Z | ---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
license: mit
language:
- fr
- en
model-index:
- name: Solon-embeddings-base-0.1
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_500
name: Recall@500
value: 90.9
- type: recall_at_100
name: Recall@100
value: 80.6
- type: recall_at_10
name: Recall@10
value: 52.5
- type: map_at_10
name: MAP@10
value: 27.4
- type: ndcg_at_10
name: nDCG@10
value: 33.5
- type: mrr_at_10
name: MRR@10
value: 27.9
---
# Solon Embeddings — Base 0.1
SOTA Open source french embedding model.
**Instructions :**
Add "query : " before the *query* to retrieve to increase performance of retrieval.
No instructions needed for *passages*.
| Model | Mean Score |
| --- | --- |
| **OrdalieTech/Solon-embeddings-large-0.1** | 0.7490 |
| cohere/embed-multilingual-v3 | 0.7402 |
| **OrdalieTech/Solon-embeddings-base-0.1** | 0.7306 |
| openai/ada-002 | 0.7290 |
| cohere/embed-multilingual-light-v3 | 0.6945 |
| antoinelouis/biencoder-camembert-base-mmarcoFR | 0.6826 |
| dangvantuan/sentence-camembert-large | 0.6756 |
| voyage/voyage-01 | 0.6753 |
| intfloat/multilingual-e5-large | 0.6660 |
| intfloat/multilingual-e5-base | 0.6597 |
| Sbert/paraphrase-multilingual-mpnet-base-v2 | 0.5975 |
| dangvantuan/sentence-camembert-base | 0.5456 |
| EuropeanParliament/eubert_embedding_v1 | 0.5063 |
These results have been obtained through 9 french benchmarks on a variety of text similarity tasks (classification, reranking, STS) :
- AmazonReviewsClassification (MTEB)
- MassiveIntentClassification (MTEB)
- MassiveScenarioClassification (MTEB)
- MTOPDomainClassification (MTEB)
- MTOPIntentClassification (MTEB)
- STS22 (MTEB)
- MiraclFRRerank (Miracl)
- OrdalieFRSTS (Ordalie)
- OrdalieFRReranking (Ordalie)
We created OrdalieFRSTS and OrdalieFRReranking to enhance the benchmarking capabilities of French STS and reranking assessments.
(evaluation script available here : github.com/OrdalieTech/mteb) |
LuangMV97/RoBERTa_EmpDialog | LuangMV97 | 2024-03-26T15:53:50Z | 46 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-generation",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T13:48:30Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: RoBERTa_EmpDialog
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. -->
# RoBERTa_EmpDialog
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0009
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1052 | 1.0 | 1198 | 0.0077 |
| 0.0124 | 2.0 | 2396 | 0.0014 |
| 0.0104 | 3.0 | 3594 | 0.0059 |
| 0.0033 | 4.0 | 4792 | 0.0012 |
| 0.0026 | 5.0 | 5990 | 0.0009 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
JustAFool/wav2vec2-large-xls-r-300m-vi-colab2 | JustAFool | 2024-03-26T15:53:34Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-17T09:54:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
susumuota/gpt_0.125B_global_step100_openassistant | susumuota | 2024-03-26T15:50:04Z | 145 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T15:49:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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:**
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## Glossary [optional]
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magjico/unit1-LunarLander-v2 | magjico | 2024-03-26T15:47:51Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-26T15:47:30Z | ---
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: 273.09 +/- 18.13
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
...
```
|
OpenBuddy/openbuddy-mixtral-7bx8-v18.1-32k-gptq | OpenBuddy | 2024-03-26T15:47:46Z | 9 | 3 | transformers | [
"transformers",
"mixtral",
"text-generation",
"conversational",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2024-02-15T04:11:48Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
minhah/vivit-b-16x2-kinetics400-finetuned-crema-d | minhah | 2024-03-26T15:46:31Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"vivit",
"video-classification",
"generated_from_trainer",
"base_model:google/vivit-b-16x2-kinetics400",
"base_model:finetune:google/vivit-b-16x2-kinetics400",
"license:mit",
"endpoints_compatible",
"region:us"
]
| video-classification | 2024-03-21T14:24:48Z | ---
license: mit
base_model: google/vivit-b-16x2-kinetics400
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vivit-b-16x2-kinetics400-finetuned-crema-d
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. -->
# vivit-b-16x2-kinetics400-finetuned-crema-d
This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7914
- Accuracy: 0.7164
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2976
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9737 | 0.25 | 745 | 1.0216 | 0.6148 |
| 0.9205 | 1.25 | 1490 | 0.9240 | 0.6510 |
| 0.456 | 2.25 | 2235 | 0.7564 | 0.7154 |
| 0.3316 | 3.25 | 2976 | 0.7168 | 0.7275 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mlx-community/Matter-0.1-7B-DPO-preview-4bit | mlx-community | 2024-03-26T15:44:01Z | 3 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"en",
"dataset:0-hero/Matter-0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-26T15:18:01Z | ---
language:
- en
license: apache-2.0
tags:
- mlx
datasets:
- 0-hero/Matter-0.1
---
# mlx-community/Matter-0.1-7B-DPO-preview-4bit
This model was converted to MLX format from [`0-hero/Matter-0.1-7B-DPO-preview`]() using mlx-lm version **0.4.0**.
Refer to the [original model card](https://huggingface.co/0-hero/Matter-0.1-7B-DPO-preview) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Matter-0.1-7B-DPO-preview-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
azharmo/tamil-gemma-exp-0.02 | azharmo | 2024-03-26T15:43:03Z | 143 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T15:37:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer | eladogruyol | 2024-03-26T15:39:22Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2024-03-26T15:33:48Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is created with the base of "https://huggingface.co/NeuML/pubmedbert-base-embeddings". This pre-trained model is trained with approximately 1100 interview questions data collected by our team.
Our aim is to be able to get more accurate similarity rates about answers given to interview questions for our project.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer')
model = AutoModel.from_pretrained('eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=eladogruyol/pubmedbert-base-embeddings-base-sentence-transformer)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 23 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 23,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Oneeb/Humanised-LLMv2 | Oneeb | 2024-03-26T15:37:08Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T15:32:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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
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[More Information Needed]
## Training Details
### Training Data
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[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 Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Summary
## Model Examination [optional]
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## 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
Trubnik1967/rugpt3small_based_on_gpt2 | Trubnik1967 | 2024-03-26T15:28:41Z | 147 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-08T15:12:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## 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
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[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
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[More Information Needed]
## Training Details
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### Testing Data, Factors & Metrics
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#### 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]
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## Model Card Contact
[More Information Needed] |
Americo/umabot_finetuning2 | Americo | 2024-03-26T15:25:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
]
| null | 2024-03-26T15:25:43Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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
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[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 Dataset 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
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#### Preprocessing [optional]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
haguy77/sdictabert-heq | haguy77 | 2024-03-26T15:24:10Z | 1,403 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"he",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2024-03-26T12:35:27Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language:
- he
---
# haguy77/sdictabert-heq
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('haguy77/sdictabert-heq')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('haguy77/sdictabert-heq')
model = AutoModel.from_pretrained('haguy77/sdictabert-heq')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=haguy77/sdictabert-heq)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 27142 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "constantlr",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
posterllava/posterllava_v0 | posterllava | 2024-03-26T15:20:25Z | 90 | 5 | transformers | [
"transformers",
"pytorch",
"llava",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:20:09Z | ---
license: apache-2.0
---
<br>
<br>
# PosterLLaVA Model Card
## Model details
**Model type:**
PoserLLaVA is an layout generation model with LLaVa achitecture. The current model is fine-tuned with the LLaVA-v1.5-13B checkpoint.
**Model date:**
posterllava_v0 was trained in March 2024.
## Training dataset
- 7k banner layouts from Ad Banner dataset
- 60k commerical poster layouts from CGL dataset and PosterLayout with text constraints
- 4k social media poster layouts from QB-Poster dataset
## Evaluation dataset
the combination of the corresponding test sets of the above training datasets |
TieIncred/distilbert-base-uncased-finetuned-intro-verizon | TieIncred | 2024-03-26T15:18:33Z | 120 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T14:28:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-intro-verizon
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-intro-verizon
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0400
- Accuracy: 1.0
- F1: 1.0
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.3753 | 1.0 | 3 | 0.2877 | 1.0 | 1.0 |
| 0.278 | 2.0 | 6 | 0.2253 | 1.0 | 1.0 |
| 0.2366 | 3.0 | 9 | 0.1788 | 1.0 | 1.0 |
| 0.1721 | 4.0 | 12 | 0.1433 | 1.0 | 1.0 |
| 0.1531 | 5.0 | 15 | 0.1173 | 1.0 | 1.0 |
| 0.117 | 6.0 | 18 | 0.0980 | 1.0 | 1.0 |
| 0.108 | 7.0 | 21 | 0.0841 | 1.0 | 1.0 |
| 0.0916 | 8.0 | 24 | 0.0737 | 1.0 | 1.0 |
| 0.0843 | 9.0 | 27 | 0.0656 | 1.0 | 1.0 |
| 0.0701 | 10.0 | 30 | 0.0594 | 1.0 | 1.0 |
| 0.0683 | 11.0 | 33 | 0.0546 | 1.0 | 1.0 |
| 0.0599 | 12.0 | 36 | 0.0508 | 1.0 | 1.0 |
| 0.058 | 13.0 | 39 | 0.0478 | 1.0 | 1.0 |
| 0.0512 | 14.0 | 42 | 0.0454 | 1.0 | 1.0 |
| 0.0523 | 15.0 | 45 | 0.0437 | 1.0 | 1.0 |
| 0.0515 | 16.0 | 48 | 0.0423 | 1.0 | 1.0 |
| 0.0468 | 17.0 | 51 | 0.0413 | 1.0 | 1.0 |
| 0.0472 | 18.0 | 54 | 0.0406 | 1.0 | 1.0 |
| 0.0479 | 19.0 | 57 | 0.0401 | 1.0 | 1.0 |
| 0.0474 | 20.0 | 60 | 0.0400 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
magjico/ppo-LunarLander-v2 | magjico | 2024-03-26T15:16:30Z | 2 | 0 | transformers | [
"transformers",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
]
| reinforcement-learning | 2024-03-15T16:18:10Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -216.03 +/- 122.28
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'f': '/root/.local/share/jupyter/runtime/kernel-a5723393-974b-45e0-9a2d-ab8b647dddaf.json'
'exp_name': 'ppo.py'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.001
'num_envs': 4
'num_steps': 200
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None}
```
|
tlin/ppo-Huggy | tlin | 2024-03-26T15:16:07Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2024-03-26T14:28:18Z | ---
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: tlin/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AdnanRiaz107/CBERT-MBPP | AdnanRiaz107 | 2024-03-26T15:09:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"text2text-generation",
"base_model:microsoft/codebert-base-mlm",
"base_model:finetune:microsoft/codebert-base-mlm",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-21T08:45:55Z | ---
base_model: microsoft/codebert-base-mlm
tags:
- generated_from_trainer
model-index:
- name: CBERT-MBPP
results: []
metrics:
- bleu
pipeline_tag: text2text-generation
---
<!-- 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. -->
# CBERT-MBPP
This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0099
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0215 | 0.05 | 500 | 0.0196 |
| 0.0087 | 0.1 | 1000 | 0.0091 |
| 0.0093 | 0.15 | 1500 | 0.0090 |
| 0.0096 | 0.2 | 2000 | 0.0094 |
| 0.009 | 0.25 | 2500 | 0.0095 |
| 0.01 | 0.3 | 3000 | 0.0091 |
| 0.01 | 0.35 | 3500 | 0.0093 |
| 0.0093 | 0.4 | 4000 | 0.0094 |
| 0.0086 | 0.45 | 4500 | 0.0098 |
| 0.0085 | 0.5 | 5000 | 0.0097 |
| 0.0094 | 0.55 | 5500 | 0.0099 |
| 0.0099 | 0.6 | 6000 | 0.0101 |
| 0.0096 | 0.65 | 6500 | 0.0099 |
| 0.0104 | 0.7 | 7000 | 0.0099 |
| 0.0096 | 0.75 | 7500 | 0.0097 |
| 0.0087 | 0.8 | 8000 | 0.0098 |
| 0.0105 | 0.85 | 8500 | 0.0099 |
| 0.0098 | 0.9 | 9000 | 0.0098 |
| 0.0091 | 0.95 | 9500 | 0.0099 |
| 0.0101 | 1.0 | 10000 | 0.0099 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
Jahanzeb1/MyGPT | Jahanzeb1 | 2024-03-26T15:07:32Z | 5 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-26T15:07:28Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: Mygpt
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. -->
# Mygpt
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.5936 | 0.92 | 3 | 3.9666 |
| 4.0513 | 1.85 | 6 | 3.4491 |
| 3.4796 | 2.77 | 9 | 2.9990 |
| 2.2703 | 4.0 | 13 | 2.5778 |
| 2.6972 | 4.92 | 16 | 2.3346 |
| 2.3873 | 5.85 | 19 | 2.1539 |
| 2.1838 | 6.77 | 22 | 2.0267 |
| 1.545 | 8.0 | 26 | 1.9506 |
| 2.0113 | 8.92 | 29 | 1.9089 |
| 1.3969 | 9.23 | 30 | 1.8976 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
yhzhai/WSCL | yhzhai | 2024-03-26T15:05:27Z | 0 | 2 | null | [
"image manipulation detection",
"weak supervision",
"arxiv:2309.01246",
"license:mit",
"region:us"
]
| null | 2024-03-23T19:12:52Z | ---
license: mit
metrics:
- f1
tags:
- image manipulation detection
- weak supervision
---
# Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
<p align="center">
<a href="https://arxiv.org/abs/2309.01246"><b>Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning</b></a>
</p>
<p align="center">
<a href="https://www.yhzhai.com">Yuanhao Zhai</a>,
<a href="https://tyluann.github.io">Tianyu Luan</a>,
<a href="https://cse.buffalo.edu/~doermann/">David Doermann</a>,
<a href="https://cse.buffalo.edu/~jsyuan/">Junsong Yuan</a>
</p>
<p align="center">
University at Buffalo
</p>
<p align="center">
ICCV 2023
</p>
Please checkout our [GitHub repo](https://github.com/yhZhai/WSCL) for the whole project!
## Citation
If you feel this project is helpful, please consider citing our paper
```bibtex
@inproceedings{zhai2023towards,
title={Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning},
author={Zhai, Yuanhao and Luan, Tianyu and Doermann, David and Yuan, Junsong},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={22390--22400},
year={2023}
}
``` |
prampampam/big-sdxl-lora | prampampam | 2024-03-26T15:02:52Z | 8 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| text-to-image | 2024-03-26T10:36:44Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a photo of living room with <s0><s1> and <s2><s3>'
output:
url:
"image_0.png"
- text: 'a photo of living room with <s0><s1> and <s2><s3>'
output:
url:
"image_1.png"
- text: 'a photo of living room with <s0><s1> and <s2><s3>'
output:
url:
"image_2.png"
- text: 'a photo of living room with <s0><s1> and <s2><s3>'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of living room with <s0><s1> and <s2><s3> in scandinavic style
license: openrail++
---
# SDXL LoRA DreamBooth - prampampam/big-sdxl-lora
<Gallery />
## Model description
### These are prampampam/big-sdxl-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`big-sdxl-lora.safetensors` here 💾](/prampampam/big-sdxl-lora/blob/main/big-sdxl-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:big-sdxl-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`big-sdxl-lora_emb.safetensors` here 💾](/prampampam/big-sdxl-lora/blob/main/big-sdxl-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `big-sdxl-lora_emb` to your prompt. For example, `a photo of living room with big-sdxl-lora_emb and in scandinavic style`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('prampampam/big-sdxl-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='prampampam/big-sdxl-lora', filename='big-sdxl-lora_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('a photo of living room with <s0><s1> and <s2><s3>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK49986` → use `<s0><s1>` in your prompt
to trigger concept `TOK52817` → use `<s2><s3>` in your prompt
to trigger concept `TOK98678` → use `<s4><s5>` in your prompt
to trigger concept `TOK113071` → use `<s6><s7>` in your prompt
to trigger concept `TOK120149` → use `<s8><s9>` in your prompt
to trigger concept `TOK123556` → use `<s10><s11>` in your prompt
to trigger concept `TOK135534` → use `<s12><s13>` in your prompt
to trigger concept `TOK189670` → use `<s14><s15>` in your prompt
to trigger concept `TOK190562` → use `<s16><s17>` in your prompt
to trigger concept `TOK193714` → use `<s18><s19>` in your prompt
to trigger concept `TOK241048` → use `<s20><s21>` in your prompt
to trigger concept `TOK250041` → use `<s22><s23>` in your prompt
to trigger concept `TOK268959` → use `<s24><s25>` in your prompt
to trigger concept `TOK273000` → use `<s26><s27>` in your prompt
to trigger concept `TOK277813` → use `<s28><s29>` in your prompt
to trigger concept `TOK279746` → use `<s30><s31>` in your prompt
to trigger concept `TOK295935` → use `<s32><s33>` in your prompt
to trigger concept `TOK305227` → use `<s34><s35>` in your prompt
to trigger concept `TOK309606` → use `<s36><s37>` in your prompt
to trigger concept `TOK321136` → use `<s38><s39>` in your prompt
to trigger concept `TOK329781` → use `<s40><s41>` in your prompt
to trigger concept `TOK329782` → use `<s42><s43>` in your prompt
to trigger concept `TOK352259` → use `<s44><s45>` in your prompt
to trigger concept `TOK355638` → use `<s46><s47>` in your prompt
to trigger concept `TOK357399` → use `<s48><s49>` in your prompt
to trigger concept `TOK368921` → use `<s50><s51>` in your prompt
to trigger concept `TOK377236` → use `<s52><s53>` in your prompt
to trigger concept `TOK382924` → use `<s54><s55>` in your prompt
to trigger concept `TOK405550` → use `<s56><s57>` in your prompt
to trigger concept `TOK429026` → use `<s58><s59>` in your prompt
to trigger concept `TOK434773` → use `<s60><s61>` in your prompt
to trigger concept `TOK435237` → use `<s62><s63>` in your prompt
to trigger concept `TOK437871` → use `<s64><s65>` in your prompt
to trigger concept `TOK439185` → use `<s66><s67>` in your prompt
to trigger concept `TOK439384` → use `<s68><s69>` in your prompt
to trigger concept `TOK443869` → use `<s70><s71>` in your prompt
to trigger concept `TOK444874` → use `<s72><s73>` in your prompt
to trigger concept `TOK453903` → use `<s74><s75>` in your prompt
to trigger concept `TOK465250` → use `<s76><s77>` in your prompt
to trigger concept `TOK482466` → use `<s78><s79>` in your prompt
to trigger concept `TOK503940` → use `<s80><s81>` in your prompt
to trigger concept `TOK524768` → use `<s82><s83>` in your prompt
to trigger concept `TOK536204` → use `<s84><s85>` in your prompt
to trigger concept `TOK537466` → use `<s86><s87>` in your prompt
to trigger concept `TOK547839` → use `<s88><s89>` in your prompt
to trigger concept `TOK548004` → use `<s90><s91>` in your prompt
to trigger concept `TOK549578` → use `<s92><s93>` in your prompt
to trigger concept `TOK557244` → use `<s94><s95>` in your prompt
to trigger concept `TOK561859` → use `<s96><s97>` in your prompt
to trigger concept `TOK569103` → use `<s98><s99>` in your prompt
to trigger concept `TOK569104` → use `<s100><s101>` in your prompt
to trigger concept `TOK570087` → use `<s102><s103>` in your prompt
to trigger concept `TOK577885` → use `<s104><s105>` in your prompt
to trigger concept `TOK578163` → use `<s106><s107>` in your prompt
to trigger concept `TOK586036` → use `<s108><s109>` in your prompt
to trigger concept `TOK590747` → use `<s110><s111>` in your prompt
to trigger concept `TOK592708` → use `<s112><s113>` in your prompt
to trigger concept `TOK597750` → use `<s114><s115>` in your prompt
to trigger concept `TOK603453` → use `<s116><s117>` in your prompt
to trigger concept `TOK618877` → use `<s118><s119>` in your prompt
to trigger concept `TOK619268` → use `<s120><s121>` in your prompt
to trigger concept `TOK629015` → use `<s122><s123>` in your prompt
to trigger concept `TOK631826` → use `<s124><s125>` in your prompt
to trigger concept `TOK639063` → use `<s126><s127>` in your prompt
to trigger concept `TOK639380` → use `<s128><s129>` in your prompt
to trigger concept `TOK648242` → use `<s130><s131>` in your prompt
to trigger concept `TOK651623` → use `<s132><s133>` in your prompt
to trigger concept `TOK653420` → use `<s134><s135>` in your prompt
to trigger concept `TOK654567` → use `<s136><s137>` in your prompt
to trigger concept `TOK657274` → use `<s138><s139>` in your prompt
to trigger concept `TOK658513` → use `<s140><s141>` in your prompt
to trigger concept `TOK662375` → use `<s142><s143>` in your prompt
to trigger concept `TOK664555` → use `<s144><s145>` in your prompt
to trigger concept `TOK674941` → use `<s146><s147>` in your prompt
to trigger concept `TOK679104` → use `<s148><s149>` in your prompt
## Details
All [Files & versions](/prampampam/big-sdxl-lora/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
susumuota/gpt_0.125B_global_step100 | susumuota | 2024-03-26T14:59:03Z | 146 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:58:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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. -->
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[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] |
AdnanRiaz107/SantaCoder-MBPP | AdnanRiaz107 | 2024-03-26T14:52:57Z | 72 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"question-answering",
"custom_code",
"en",
"dataset:mbpp",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-03-16T12:36:41Z | ---
library_name: transformers
license: mit
datasets:
- mbpp
language:
- en
metrics:
- bleu
pipeline_tag: question-answering
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
KGsteven/0318credit_card_labels-distilroberta-finetuned-banking77 | KGsteven | 2024-03-26T14:52:00Z | 199 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-18T15:52:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[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 Dataset 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]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[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]
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|
DrWangDr/tag5_TheBloke_Mistral-7B-Instruct-v0.2-GPTQ_r8_with_GTag5 | DrWangDr | 2024-03-26T14:51:55Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-26T14:47:39Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: tag5_TheBloke_Mistral-7B-Instruct-v0.2-GPTQ_r8_with_GTag5
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. -->
# tag5_TheBloke_Mistral-7B-Instruct-v0.2-GPTQ_r8_with_GTag5
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1 |
SOUMYADEEPSAR/roberta_subj512 | SOUMYADEEPSAR | 2024-03-26T14:49:35Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T09:53:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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
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[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 Dataset 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 Dataset 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
opelumen/distilgpt2-finetuned-wikitext2_2 | opelumen | 2024-03-26T14:46:53Z | 215 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:14:52Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2_2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5812
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7226 | 1.0 | 1758 | 3.6036 |
| 3.6081 | 2.0 | 3516 | 3.5875 |
| 3.5649 | 3.0 | 5274 | 3.5812 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
aubmindlab/bert-base-arabertv02 | aubmindlab | 2024-03-26T14:39:39Z | 694,016 | 33 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:1.5B-Arabic-Corpus",
"dataset:oscar-arabic-unshuffled",
"dataset:Assafir-private",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: ar
datasets:
- wikipedia
- Osian
- 1.5B-Arabic-Corpus
- oscar-arabic-unshuffled
- Assafir-private
widget:
- text: ' عاصمة لبنان هي [MASK] .'
pipeline_tag: fill-mask
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained language model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERT uses the same BERT-Base config. More details are available in the [AraBERT Paper](https://arxiv.org/abs/2003.00104) and in the [AraBERT Meetup](https://github.com/WissamAntoun/pydata_khobar_meetup)
There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
We evaluate AraBERT models on different downstream tasks and compare them to [mBERT]((https://github.com/google-research/bert/blob/master/multilingual.md)), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets ([HARD](https://github.com/elnagara/HARD-Arabic-Dataset), [ASTD-Balanced](https://www.aclweb.org/anthology/D15-1299), [ArsenTD-Lev](https://staff.aub.edu.lb/~we07/Publications/ArSentD-LEV_Sentiment_Corpus.pdf), [LABR](https://github.com/mohamedadaly/LABR)), Named Entity Recognition with the [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp), and Arabic Question Answering on [Arabic-SQuAD and ARCD](https://github.com/husseinmozannar/SOQAL)
# AraBERTv2
## What's New!
AraBERT now comes in 4 new variants to replace the old v1 versions:
More Detail in the AraBERT folder and in the [README](https://github.com/aub-mind/arabert/blob/master/AraBERT/README.md) and in the [AraBERT Paper](https://arxiv.org/abs/2003.00104v2)
Model | HuggingFace Model Name | Size (MB/Params)| Pre-Segmentation | DataSet (Sentences/Size/nWords) |
---|:---:|:---:|:---:|:---:
AraBERTv0.2-base | [bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) | 543MB / 136M | No | 200M / 77GB / 8.6B |
AraBERTv0.2-large| [bert-large-arabertv02](https://huggingface.co/aubmindlab/bert-large-arabertv02) | 1.38G 371M | No | 200M / 77GB / 8.6B |
AraBERTv2-base| [bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) | 543MB 136M | Yes | 200M / 77GB / 8.6B |
AraBERTv2-large| [bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) | 1.38G 371M | Yes | 200M / 77GB / 8.6B |
AraBERTv0.2-Twitter-base| [bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) | 543MB / 136M | No | Same as v02 + 60M Multi-Dialect Tweets|
AraBERTv0.2-Twitter-large| [bert-large-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-large-arabertv02-twitter) | 1.38G / 371M | No | Same as v02 + 60M Multi-Dialect Tweets|
AraBERTv0.1-base| [bert-base-arabertv01](https://huggingface.co/aubmindlab/bert-base-arabertv01) | 543MB 136M | No | 77M / 23GB / 2.7B |
AraBERTv1-base| [bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) | 543MB 136M | Yes | 77M / 23GB / 2.7B |
All models are available in the `HuggingFace` model page under the [aubmindlab](https://huggingface.co/aubmindlab/) name. Checkpoints are available in PyTorch, TF2 and TF1 formats.
## Better Pre-Processing and New Vocab
We identified an issue with AraBERTv1's wordpiece vocabulary. The issue came from punctuations and numbers that were still attached to words when learned the wordpiece vocab. We now insert a space between numbers and characters and around punctuation characters.
The new vocabulary was learned using the `BertWordpieceTokenizer` from the `tokenizers` library, and should now support the Fast tokenizer implementation from the `transformers` library.
**P.S.**: All the old BERT codes should work with the new BERT, just change the model name and check the new preprocessing function
**Please read the section on how to use the [preprocessing function](#Preprocessing)**
## Bigger Dataset and More Compute
We used ~3.5 times more data, and trained for longer.
For Dataset Sources see the [Dataset Section](#Dataset)
Model | Hardware | num of examples with seq len (128 / 512) |128 (Batch Size/ Num of Steps) | 512 (Batch Size/ Num of Steps) | Total Steps | Total Time (in Days) |
---|:---:|:---:|:---:|:---:|:---:|:---:
AraBERTv0.2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv0.2-large | TPUv3-128 | 420M / 207M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERTv2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv2-large | TPUv3-128 | 520M / 245M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERT-base (v1/v0.1) | TPUv2-8 | - |512 / 900K | 128 / 300K| 1.2M | 4
# Dataset
The pretraining data used for the new AraBERT model is also used for Arabic **GPT2 and ELECTRA**.
The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation)
For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled:
- OSCAR unshuffled and filtered.
- [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01
- [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4)
- [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619)
- Assafir news articles. Huge thank you for Assafir for providing us the data
# Preprocessing
It is recommended to apply our preprocessing function before training/testing on any dataset.
**Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data `pip install arabert`**
```python
from arabert.preprocess import ArabertPreprocessor
model_name="aubmindlab/bert-large-arabertv02"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "ولن نبالغ إذا قلنا: إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري"
arabert_prep.preprocess(text)
>>> output: ولن نبالغ إذا قلنا : إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري
```
# TensorFlow 1.x models
The TF1.x model are available in the HuggingFace models repo.
You can download them as follows:
- via git-lfs: clone all the models in a repo
```bash
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/aubmindlab/MODEL_NAME
tar -C ./MODEL_NAME -zxvf /content/MODEL_NAME/tf1_model.tar.gz
```
where `MODEL_NAME` is any model under the `aubmindlab` name
- via `wget`:
- Go to the tf1_model.tar.gz file on huggingface.co/models/aubmindlab/MODEL_NAME.
- copy the `oid sha256`
- then run `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/INSERT_THE_SHA_HERE` (ex: for `aragpt2-base`: `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/3766fc03d7c2593ff2fb991d275e96b81b0ecb2098b71ff315611d052ce65248`)
# If you used this model please cite us as :
Google Scholar has our Bibtex wrong (missing name), use this instead
```
@inproceedings{antoun2020arabert,
title={AraBERT: Transformer-based Model for Arabic Language Understanding},
author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},
booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},
pages={9}
}
```
# Acknowledgments
Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continuous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.
# Contacts
**Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <[email protected]> | <[email protected]>
**Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <[email protected]> | <[email protected]> |
igorcardoso/ppo-LunarLander-v2 | igorcardoso | 2024-03-26T14:35:49Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-26T14:34:25Z | ---
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: 271.15 +/- 22.42
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
...
```
|
rorito/testbb | rorito | 2024-03-26T14:35:26Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2024-03-26T14:35:21Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: 'baby '
output:
url: images/b6da52a7-f60e-4640-befe-87e4feec78b3.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
---
# testbb
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/rorito/testbb/tree/main) them in the Files & versions tab.
|
THREEBETKING009/o | THREEBETKING009 | 2024-03-26T14:28:27Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2024-03-26T14:28:27Z | ---
license: bigcode-openrail-m
---
|
fhjhl/my_awesome_model | fhjhl | 2024-03-26T14:28:10Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-25T02:16:21Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3837
- Accuracy: 0.8642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 192 | 0.4443 | 0.8407 |
| No log | 2.0 | 384 | 0.4431 | 0.8407 |
| 0.3652 | 3.0 | 576 | 0.4471 | 0.8407 |
| 0.3652 | 4.0 | 768 | 0.3579 | 0.8642 |
| 0.3652 | 5.0 | 960 | 0.3823 | 0.8642 |
| 0.3287 | 6.0 | 1152 | 0.3654 | 0.8642 |
| 0.3287 | 7.0 | 1344 | 0.3694 | 0.8642 |
| 0.2929 | 8.0 | 1536 | 0.3688 | 0.8642 |
| 0.2929 | 9.0 | 1728 | 0.3817 | 0.8642 |
| 0.2929 | 10.0 | 1920 | 0.3837 | 0.8642 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
|
antoinelouis/biencoder-electra-base-mmarcoFR | antoinelouis | 2024-03-26T14:24:35Z | 34 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"electra",
"passage-retrieval",
"sentence-similarity",
"fr",
"dataset:unicamp-dl/mmarco",
"base_model:dbmdz/electra-base-french-europeana-cased-discriminator",
"base_model:finetune:dbmdz/electra-base-french-europeana-cased-discriminator",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-05-22T20:58:16Z | ---
pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- passage-retrieval
library_name: sentence-transformers
base_model: dbmdz/electra-base-french-europeana-cased-discriminator
model-index:
- name: biencoder-electra-base-french-mmarcoFR
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_500
name: Recall@500
value: 81.6
- type: recall_at_100
name: Recall@100
value: 69.0
- type: recall_at_10
name: Recall@10
value: 43.5
- type: map_at_10
name: MAP@10
value: 22.9
- type: ndcg_at_10
name: nDCG@10
value: 28.0
- type: mrr_at_10
name: MRR@10
value: 23.4
---
# biencoder-electra-base-french-mmarcoFR
This is a dense single-vector bi-encoder model for **French** that can be used for semantic search. The model maps queries and passages to 768-dimensional dense vectors which are used to compute relevance through cosine similarity.
## Usage
Here are some examples for using the model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using Sentence-Transformers
Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = SentenceTransformer('antoinelouis/biencoder-electra-base-french-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using FlagEmbedding
Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:
```python
from FlagEmbedding import FlagModel
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = FlagModel('antoinelouis/biencoder-electra-base-french-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using Transformers
Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModel
from torch.nn.functional import normalize
def mean_pooling(model_output, attention_mask):
""" Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation."""
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-electra-base-french-mmarcoFR')
model = AutoModel.from_pretrained('antoinelouis/biencoder-electra-base-french-mmarcoFR')
q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt')
p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
q_output = model(**encoded_queries)
p_output = model(**encoded_passages)
q_embeddings = mean_pooling(q_output, q_input['attention_mask'])
q_embedddings = normalize(q_embeddings, p=2, dim=1)
p_embeddings = mean_pooling(p_output, p_input['attention_mask'])
p_embedddings = normalize(p_embeddings, p=2, dim=1)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
## Evaluation
The model is evaluated on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for a corpus of
8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k).
To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
## Training
#### Data
We use the French training samples from the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset.
#### Implementation
The model is initialized from the [dbmdz/electra-base-french-europeana-cased-discriminator](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-discriminator)
checkpoint and optimized via the cross-entropy loss (as in [DPR](https://doi.org/10.48550/arXiv.2004.04906)) with a temperature of 0.05. It is fine-tuned on one 32GB
NVIDIA V100 GPU for 20 epochs (i.e., 62.4k steps) using the AdamW optimizer with a batch size of 160, a peak learning rate of 2e-5 with warm up along the first 500
steps and linear scheduling. We set the maximum sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores.
## Citation
```bibtex
@online{louis2024decouvrir,
author = 'Antoine Louis',
title = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
publisher = 'Hugging Face',
month = 'mar',
year = '2024',
url = 'https://huggingface.co/spaces/antoinelouis/decouvrir',
}
``` |
neopolita/tess-7b-v2.0-gguf | neopolita | 2024-03-26T14:23:52Z | 3 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-03-26T13:45:15Z | ---
{}
---
# GGUF quants for [**migtissera/Tess-7B-v2.0**](https://huggingface.co/migtissera/Tess-7B-v2.0) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/migtissera/Tess-7B-v2.0)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR | antoinelouis | 2024-03-26T14:22:59Z | 90 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"passage-retrieval",
"sentence-similarity",
"fr",
"dataset:unicamp-dl/mmarco",
"base_model:nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large",
"base_model:finetune:nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-05-22T20:59:25Z | ---
pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- passage-retrieval
library_name: sentence-transformers
base_model: nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
model-index:
- name: biencoder-mMiniLMv2-L12-mmarcoFR
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_500
name: Recall@500
value: 84.4
- type: recall_at_100
name: Recall@100
value: 71.5
- type: recall_at_10
name: Recall@10
value: 45.4
- type: map_at_10
name: MAP@10
value: 24.2
- type: ndcg_at_10
name: nDCG@10
value: 29.4
- type: mrr_at_10
name: MRR@10
value: 24.7
---
# biencoder-mMiniLMv2-L12-mmarcoFR
This is a dense single-vector bi-encoder model for **French** that can be used for semantic search. The model maps queries and passages to 384-dimensional dense vectors which are used to compute relevance through cosine similarity.
## Usage
Here are some examples for using the model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using Sentence-Transformers
Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = SentenceTransformer('antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using FlagEmbedding
Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:
```python
from FlagEmbedding import FlagModel
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = FlagModel('antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using Transformers
Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModel
from torch.nn.functional import normalize
def mean_pooling(model_output, attention_mask):
""" Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation."""
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR')
model = AutoModel.from_pretrained('antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR')
q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt')
p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
q_output = model(**encoded_queries)
p_output = model(**encoded_passages)
q_embeddings = mean_pooling(q_output, q_input['attention_mask'])
q_embedddings = normalize(q_embeddings, p=2, dim=1)
p_embeddings = mean_pooling(p_output, p_input['attention_mask'])
p_embedddings = normalize(p_embeddings, p=2, dim=1)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
## Evaluation
The model is evaluated on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for a corpus of
8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k).
To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
## Training
#### Data
We use the French training samples from the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset.
#### Implementation
The model is initialized from the [nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) checkpoint and optimized via the cross-entropy loss (as in [DPR](https://doi.org/10.48550/arXiv.2004.04906)) with a temperature of 0.05. It is fine-tuned on one 32GB NVIDIA V100 GPU for 20 epochs (i.e., 65.7k steps) using the AdamW optimizer with a batch size of 152, a peak learning rate of 2e-5 with warm up along the first 500 steps and linear scheduling. We set the maximum sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores.
## Citation
```bibtex
@online{louis2024decouvrir,
author = 'Antoine Louis',
title = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
publisher = 'Hugging Face',
month = 'mar',
year = '2024',
url = 'https://huggingface.co/spaces/antoinelouis/decouvrir',
}
``` |
Ksgk-fy/M7Percival_010.7-0.57-0.83-0.26-0.35-0.58-7B | Ksgk-fy | 2024-03-26T14:21:45Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:AurelPx/Percival_01-7b-slerp",
"base_model:merge:AurelPx/Percival_01-7b-slerp",
"base_model:liminerity/M7-7b",
"base_model:merge:liminerity/M7-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:18:10Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- liminerity/M7-7b
- AurelPx/Percival_01-7b-slerp
---
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/M7-7b
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/M7-7b
parameters:
t:
- filter: self_attn
value: [0.698652053184304, 0.5666419592708605, 0.8285581299224429, 0.2573039882007465, 0.3487534533893577]
- filter: mlp
value: [0.301347946815696, 0.4333580407291395, 0.7426960117992535, 0.7426960117992535, 0.6512465466106423]
- value: 0.5811865511130739
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ksgk-fy/M7Percival_010.7-0.57-0.83-0.26-0.35-0.58-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
antoinelouis/biencoder-camembert-base-mmarcoFR | antoinelouis | 2024-03-26T14:20:48Z | 4,538 | 9 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"camembert",
"passage-retrieval",
"sentence-similarity",
"fr",
"dataset:unicamp-dl/mmarco",
"base_model:almanach/camembert-base",
"base_model:finetune:almanach/camembert-base",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-05-22T20:54:25Z | ---
pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- passage-retrieval
library_name: sentence-transformers
base_model: camembert-base
model-index:
- name: biencoder-camembert-base-mmarcoFR
results:
- task:
type: sentence-similarity
name: Passage Retrieval
dataset:
type: unicamp-dl/mmarco
name: mMARCO-fr
config: french
split: validation
metrics:
- type: recall_at_500
name: Recall@500
value: 89.1
- type: recall_at_100
name: Recall@100
value: 77.8
- type: recall_at_10
name: Recall@10
value: 51.5
- type: map_at_10
name: MAP@10
value: 27.9
- type: ndcg_at_10
name: nDCG@10
value: 33.7
- type: mrr_at_10
name: MRR@10
value: 28.5
---
# biencoder-camembert-base-mmarcoFR
This is a dense single-vector bi-encoder model for **French** that can be used for semantic search. The model maps queries and passages to 768-dimensional dense vectors which are used to compute relevance through cosine similarity.
## Usage
Here are some examples for using the model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using Sentence-Transformers
Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = SentenceTransformer('antoinelouis/biencoder-camembert-base-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using FlagEmbedding
Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:
```python
from FlagEmbedding import FlagModel
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = FlagModel('antoinelouis/biencoder-camembert-base-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
#### Using Transformers
Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModel
from torch.nn.functional import normalize
def mean_pooling(model_output, attention_mask):
""" Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation."""
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-camembert-base-mmarcoFR')
model = AutoModel.from_pretrained('antoinelouis/biencoder-camembert-base-mmarcoFR')
q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt')
p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
q_output = model(**encoded_queries)
p_output = model(**encoded_passages)
q_embeddings = mean_pooling(q_output, q_input['attention_mask'])
q_embedddings = normalize(q_embeddings, p=2, dim=1)
p_embeddings = mean_pooling(p_output, p_input['attention_mask'])
p_embedddings = normalize(p_embeddings, p=2, dim=1)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
```
## Evaluation
The model is evaluated on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for a corpus of
8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k).
To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
## Training
#### Data
We use the French training samples from the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset.
#### Implementation
The model is initialized from the [camembert-base](https://huggingface.co/camembert-base) checkpoint and optimized via the cross-entropy loss (as in [DPR](https://doi.org/10.48550/arXiv.2004.04906)) with a temperature of 0.05. It is fine-tuned on one 32GB NVIDIA V100 GPU for 20 epochs (i.e., 65.7k steps) using the AdamW optimizer with a batch size of 152, a peak learning rate of 2e-5 with warm up along the first 500 steps and linear scheduling. We set the maximum sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores.
## Citation
```bibtex
@online{louis2024decouvrir,
author = 'Antoine Louis',
title = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
publisher = 'Hugging Face',
month = 'mar',
year = '2024',
url = 'https://huggingface.co/spaces/antoinelouis/decouvrir',
}
``` |
chcaa/grc_odycy_joint_sm | chcaa | 2024-03-26T14:18:53Z | 74 | 1 | spacy | [
"spacy",
"token-classification",
"grc",
"license:mit",
"model-index",
"region:us"
]
| token-classification | 2023-04-03T11:57:29Z | ---
tags:
- spacy
- token-classification
language:
- grc
license: mit
model-index:
- name: grc_odycy_joint_sm
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.7500174637
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9052781581
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.8230691852
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.935765465
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.705650401
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.630690644
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.7130820399
---
<p align="center">
<img width="200" src="https://github.com/centre-for-humanities-computing/odyCy/raw/main/docs/_static/logo_with_text_below.svg">
<div align="center" style="color: #2c5882; font-weight: bold; font-size: 18px; margin-top: -20px;">
A general-purpose NLP pipeline for Ancient-Greek.
</div>
</p>
[](https://colab.research.google.com/github/centre-for-humanities-computing/odyCy/blob/main/tutorials/01_odycy_getting_started.ipynb#&offline=true&sandboxMode=true)
Check out our Documentation on [Basic Usage](https://centre-for-humanities-computing.github.io/odyCy/getting_started.html).
## Performance
odyCy achieves state of the art performance on multiple tasks on unseen test data from the Universal Dependencies Perseus treebank,
and performs second best on the PROIEL treebank’s test set on even more tasks.
In addition performance also seems relatively stable across the two evaluation datasets in comparison with other NLP pipelines.
For plots and tables on OdyCy's performance, check out the Documentation page on [Performance](https://centre-for-humanities-computing.github.io/odyCy/performance.html)
| Feature | Description |
| --- | --- |
| **Name** | `grc_odycy_joint_sm` |
| **Version** | `0.7.0` |
| **spaCy** | `>=3.7.4,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `frequency_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [{Márton Kardos, Jan Kostkan}](https://github.com/centre-for-humanities-computing/odyCy) |
### Label Scheme
<details>
<summary>View label scheme (2299 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `---------`, `--p---fa-`, `--s---ma-`, `-3paia---`, `-3paim---`, `-3siia---`, `A-`, `C-`, `Df`, `Dq`, `Du`, `F-`, `G-`, `I-`, `Ma`, `Mo`, `Nb`, `Ne`, `Pc`, `Pd`, `Pi`, `Pk`, `Pp`, `Pr`, `Ps`, `Px`, `R-`, `S-`, `V-`, `a--------`, `a-------s`, `a-d---fa-`, `a-d---fd-`, `a-d---fg-`, `a-d---fn-`, `a-d---ma-`, `a-d---md-`, `a-d---mg-`, `a-d---mn-`, `a-d---mnc`, `a-d---mv-`, `a-d---na-`, `a-d---ng-`, `a-d---nn-`, `a-p----dc`, `a-p---fa-`, `a-p---fac`, `a-p---fas`, `a-p---fd-`, `a-p---fdc`, `a-p---fds`, `a-p---fg-`, `a-p---fgc`, `a-p---fn-`, `a-p---fnc`, `a-p---fns`, `a-p---fv-`, `a-p---m--`, `a-p---m-c`, `a-p---ma-`, `a-p---mac`, `a-p---mas`, `a-p---md-`, `a-p---mdc`, `a-p---mds`, `a-p---mg-`, `a-p---mgc`, `a-p---mgs`, `a-p---mn-`, `a-p---mnc`, `a-p---mns`, `a-p---mv-`, `a-p---mvs`, `a-p---na-`, `a-p---nac`, `a-p---nas`, `a-p---nd-`, `a-p---ndc`, `a-p---nds`, `a-p---ng-`, `a-p---ngs`, `a-p---nn-`, `a-p---nnc`, `a-p---nns`, `a-p---nv-`, `a-s----d-`, `a-s----dc`, `a-s----g-`, `a-s----gc`, `a-s---fa-`, `a-s---fac`, `a-s---fas`, `a-s---fd-`, `a-s---fds`, `a-s---fg-`, `a-s---fgc`, `a-s---fgs`, `a-s---fn-`, `a-s---fnc`, `a-s---fns`, `a-s---fv-`, `a-s---m--`, `a-s---ma-`, `a-s---mac`, `a-s---mas`, `a-s---md-`, `a-s---mdc`, `a-s---mds`, `a-s---mg-`, `a-s---mgc`, `a-s---mgs`, `a-s---mn-`, `a-s---mnc`, `a-s---mns`, `a-s---mv-`, `a-s---mvc`, `a-s---mvs`, `a-s---na-`, `a-s---nac`, `a-s---nas`, `a-s---nd-`, `a-s---ndc`, `a-s---nds`, `a-s---ng-`, `a-s---nn-`, `a-s---nnc`, `a-s---nns`, `a-s---nv-`, `a-s---nvs`, `c--------`, `d--------`, `d-------c`, `d-------s`, `g--------`, `i--------`, `l--------`, `l-d---fa-`, `l-d---fg-`, `l-d---mg-`, `l-d---mn-`, `l-d---na-`, `l-d---nn-`, `l-p---fa-`, `l-p---fd-`, `l-p---fg-`, `l-p---fn-`, `l-p---ma-`, `l-p---md-`, `l-p---mg-`, `l-p---mn-`, `l-p---na-`, `l-p---nd-`, `l-p---ng-`, `l-p---nn-`, `l-s---fa-`, `l-s---fd-`, `l-s---fg-`, `l-s---fn-`, `l-s---ma-`, `l-s---md-`, `l-s---mg-`, `l-s---mn-`, `l-s---na-`, `l-s---nd-`, `l-s---ng-`, `l-s---nn-`, `m--------`, `m-p---m--`, `m-p---md-`, `m-p---nn-`, `n-----fg-`, `n-----na-`, `n-----nn-`, `n-d----a-`, `n-d---fa-`, `n-d---fd-`, `n-d---fg-`, `n-d---fn-`, `n-d---ma-`, `n-d---md-`, `n-d---mg-`, `n-d---mn-`, `n-d---mv-`, `n-d---na-`, `n-d---nn-`, `n-p----d-`, `n-p----g-`, `n-p---fa-`, `n-p---fd-`, `n-p---fg-`, `n-p---fn-`, `n-p---fv-`, `n-p---ma-`, `n-p---md-`, `n-p---mg-`, `n-p---mn-`, `n-p---mv-`, `n-p---na-`, `n-p---nd-`, `n-p---ng-`, `n-p---nn-`, `n-p---nv-`, `n-s----d-`, `n-s----g-`, `n-s----n-`, `n-s----v-`, `n-s---fa-`, `n-s---fd-`, `n-s---fg-`, `n-s---fn-`, `n-s---fv-`, `n-s---m--`, `n-s---ma-`, `n-s---md-`, `n-s---mg-`, `n-s---mn-`, `n-s---mv-`, `n-s---na-`, `n-s---nd-`, `n-s---ng-`, `n-s---nn-`, `n-s---nv-`, `p--------`, `p-d----d-`, `p-d----n-`, `p-d---fa-`, `p-d---fd-`, `p-d---fg-`, `p-d---fn-`, `p-d---ma-`, `p-d---md-`, `p-d---mg-`, `p-d---mn-`, `p-d---mv-`, `p-p----a-`, `p-p----d-`, `p-p----g-`, `p-p----n-`, `p-p---fa-`, `p-p---fd-`, `p-p---fg-`, `p-p---fn-`, `p-p---ma-`, `p-p---md-`, `p-p---mg-`, `p-p---mn-`, `p-p---na-`, `p-p---nd-`, `p-p---ng-`, `p-p---nn-`, `p-s----a-`, `p-s----d-`, `p-s----g-`, `p-s----n-`, `p-s---fa-`, `p-s---fd-`, `p-s---fg-`, `p-s---fn-`, `p-s---ma-`, `p-s---md-`, `p-s---mg-`, `p-s---mn-`, `p-s---mv-`, `p-s---na-`, `p-s---nd-`, `p-s---ng-`, `p-s---nn-`, `p1p---fa-`, `p1p---ma-`, `p1p---md-`, `p1p---mg-`, `p1p---mn-`, `p1s---fa-`, `p1s---fd-`, `p1s---fg-`, `p1s---fn-`, `p1s---ma-`, `p1s---md-`, `p1s---mg-`, `p1s---mn-`, `p2p----a-`, `p2p----d-`, `p2p---ma-`, `p2p---mg-`, `p2p---mn-`, `p2s----a-`, `p2s----d-`, `p2s----g-`, `p2s----n-`, `p2s---ma-`, `p2s---md-`, `p2s---mg-`, `p3s---fa-`, `p3s---ma-`, `r--------`, `u--------`, `v---na---`, `v--amm---`, `v--an----`, `v--ana---`, `v--ane---`, `v--anm---`, `v--anp---`, `v--fna---`, `v--fne---`, `v--fnm---`, `v--fnp---`, `v--pna---`, `v--pnd---`, `v--pne---`, `v--pnp---`, `v--ppefa-`, `v--ppemn-`, `v--rn----`, `v--rna---`, `v--rne---`, `v--rnp---`, `v--tna---`, `v-dapafn-`, `v-dapama-`, `v-dapamg-`, `v-dapamn-`, `v-dapmfn-`, `v-dapmmn-`, `v-dappma-`, `v-dappmn-`, `v-dppafg-`, `v-dppama-`, `v-dppamn-`, `v-dppefn-`, `v-dppema-`, `v-dppemd-`, `v-dppemn-`, `v-dpppmn-`, `v-drpama-`, `v-drpamn-`, `v-drpefn-`, `v-drpemn-`, `v-p-pmma-`, `v-pap-mn-`, `v-papafa-`, `v-papafg-`, `v-papafn-`, `v-papama-`, `v-papamd-`, `v-papamg-`, `v-papamn-`, `v-papana-`, `v-papand-`, `v-papann-`, `v-papefn-`, `v-papema-`, `v-papemn-`, `v-papmfa-`, `v-papmfg-`, `v-papmfn-`, `v-papmma-`, `v-papmmd-`, `v-papmmg-`, `v-papmmn-`, `v-papmna-`, `v-papmng-`, `v-papmnn-`, `v-pappfd-`, `v-pappfg-`, `v-pappfn-`, `v-pappma-`, `v-pappmd-`, `v-pappmg-`, `v-pappmn-`, `v-pappna-`, `v-pappng-`, `v-pappnn-`, `v-pfpama-`, `v-pfpamg-`, `v-pfpamn-`, `v-pfpema-`, `v-pfpemn-`, `v-pfpmfa-`, `v-pfpmfn-`, `v-pfpmma-`, `v-pfpmmd-`, `v-pfpmmg-`, `v-pfpmmn-`, `v-pfpmnn-`, `v-pfppmn-`, `v-ppp-mn-`, `v-pppafa-`, `v-pppafd-`, `v-pppafg-`, `v-pppafn-`, `v-pppafv-`, `v-pppama-`, `v-pppamd-`, `v-pppamg-`, `v-pppamn-`, `v-pppamv-`, `v-pppana-`, `v-pppand-`, `v-pppang-`, `v-pppann-`, `v-pppefa-`, `v-pppefd-`, `v-pppefg-`, `v-pppefn-`, `v-pppefv-`, `v-pppema-`, `v-pppemd-`, `v-pppemg-`, `v-pppemn-`, `v-pppemv-`, `v-pppena-`, `v-pppend-`, `v-pppeng-`, `v-pppenn-`, `v-ppppma-`, `v-ppppmd-`, `v-ppppmn-`, `v-prp-mn-`, `v-prpafa-`, `v-prpafd-`, `v-prpafn-`, `v-prpama-`, `v-prpamd-`, `v-prpamg-`, `v-prpamn-`, `v-prpana-`, `v-prpang-`, `v-prpefa-`, `v-prpefd-`, `v-prpefg-`, `v-prpefn-`, `v-prpema-`, `v-prpemd-`, `v-prpemg-`, `v-prpemn-`, `v-prpena-`, `v-prpend-`, `v-prpeng-`, `v-prpenn-`, `v-prppfn-`, `v-prppmn-`, `v-sagamn-`, `v-saiamn-`, `v-samp---`, `v-sap-mg-`, `v-sap-mn-`, `v-sapafa-`, `v-sapafd-`, `v-sapafg-`, `v-sapafn-`, `v-sapama-`, `v-sapamd-`, `v-sapamg-`, `v-sapamn-`, `v-sapamv-`, `v-sapana-`, `v-sapang-`, `v-sapann-`, `v-sapanv-`, `v-sapema-`, `v-sapemn-`, `v-sapmfa-`, `v-sapmfd-`, `v-sapmfg-`, `v-sapmfn-`, `v-sapmma-`, `v-sapmmd-`, `v-sapmmg-`, `v-sapmmn-`, `v-sapmna-`, `v-sapmng-`, `v-sapmnn-`, `v-sappfa-`, `v-sappfd-`, `v-sappfg-`, `v-sappfn-`, `v-sappma-`, `v-sappmd-`, `v-sappmg-`, `v-sappmn-`, `v-sappna-`, `v-sappng-`, `v-sappnn-`, `v-sappnv-`, `v-sfpafa-`, `v-sfpafd-`, `v-sfpafn-`, `v-sfpama-`, `v-sfpamd-`, `v-sfpamg-`, `v-sfpamn-`, `v-sfpmfa-`, `v-sfpmfd-`, `v-sfpmfg-`, `v-sfpmfn-`, `v-sfpmma-`, `v-sfpmmg-`, `v-sfpmmn-`, `v-sfpmna-`, `v-sfppma-`, `v-spiamn-`, `v-spp-mn-`, `v-spp-nn-`, `v-sppa---`, `v-sppafa-`, `v-sppafd-`, `v-sppafg-`, `v-sppafn-`, `v-sppafv-`, `v-sppama-`, `v-sppamd-`, `v-sppamg-`, `v-sppamn-`, `v-sppamv-`, `v-sppana-`, `v-sppand-`, `v-sppang-`, `v-sppann-`, `v-sppanv-`, `v-sppefa-`, `v-sppefd-`, `v-sppefg-`, `v-sppefn-`, `v-sppema-`, `v-sppemd-`, `v-sppemg-`, `v-sppemn-`, `v-sppemv-`, `v-sppena-`, `v-sppend-`, `v-sppeng-`, `v-sppenn-`, `v-spppfa-`, `v-spppfd-`, `v-spppfg-`, `v-spppfn-`, `v-spppma-`, `v-spppmn-`, `v-srp-mn-`, `v-srpafa-`, `v-srpafd-`, `v-srpafg-`, `v-srpafn-`, `v-srpama-`, `v-srpamd-`, `v-srpamg-`, `v-srpamn-`, `v-srpamv-`, `v-srpana-`, `v-srpand-`, `v-srpang-`, `v-srpann-`, `v-srpefa-`, `v-srpefd-`, `v-srpefg-`, `v-srpefn-`, `v-srpema-`, `v-srpemd-`, `v-srpemg-`, `v-srpemn-`, `v-srpemv-`, `v-srpena-`, `v-srpend-`, `v-srpeng-`, `v-srpenn-`, `v-srppfn-`, `v-srppma-`, `v-srppmn-`, `v-srppmv-`, `v1paia---`, `v1paim---`, `v1paip---`, `v1paoa---`, `v1paom---`, `v1paop---`, `v1pasa---`, `v1pase---`, `v1pasm---`, `v1pasp---`, `v1pfia---`, `v1pfim---`, `v1pfom---`, `v1piia---`, `v1piie---`, `v1plia---`, `v1plie---`, `v1ppia---`, `v1ppie---`, `v1ppip---`, `v1ppoa---`, `v1ppoe---`, `v1ppsa---`, `v1ppse---`, `v1pria---`, `v1prie---`, `v1prsa---`, `v1prse---`, `v1ptie---`, `v1s-sa---`, `v1sa-a---`, `v1saia---`, `v1saie---`, `v1saim---`, `v1saip---`, `v1sao----`, `v1saoa---`, `v1saoe---`, `v1saom---`, `v1saop---`, `v1sasa---`, `v1sase---`, `v1sasm---`, `v1sasp---`, `v1sfi----`, `v1sfia---`, `v1sfie---`, `v1sfim---`, `v1sfip---`, `v1siia---`, `v1siie---`, `v1slia---`, `v1slie---`, `v1slim---`, `v1spia---`, `v1spie---`, `v1spoa---`, `v1spoe---`, `v1spsa---`, `v1spse---`, `v1sria---`, `v1srie---`, `v1sroa---`, `v1sroe---`, `v1srsa---`, `v1stie---`, `v1stim---`, `v2daia---`, `v2dama---`, `v2dasa---`, `v2dase---`, `v2dfia---`, `v2dfim---`, `v2diia---`, `v2diie---`, `v2dpia---`, `v2dpma---`, `v2dpme---`, `v2dria---`, `v2drma---`, `v2paia---`, `v2paim---`, `v2paip---`, `v2pama---`, `v2pame---`, `v2pamm---`, `v2paoa---`, `v2paom---`, `v2paop---`, `v2pasa---`, `v2pase---`, `v2pasm---`, `v2pasp---`, `v2pfia---`, `v2pfim---`, `v2piia---`, `v2piie---`, `v2ppia---`, `v2ppie---`, `v2ppma---`, `v2ppme---`, `v2ppoa---`, `v2ppoe---`, `v2ppsa---`, `v2pria---`, `v2prie---`, `v2prma---`, `v2prmp---`, `v2proa---`, `v2prsa---`, `v2saia---`, `v2saie---`, `v2saim---`, `v2saip---`, `v2sam----`, `v2sama---`, `v2same---`, `v2samm---`, `v2samp---`, `v2saoa---`, `v2saoe---`, `v2saom---`, `v2saop---`, `v2sasa---`, `v2sase---`, `v2sasm---`, `v2sasp---`, `v2sfi----`, `v2sfia---`, `v2sfie---`, `v2sfim---`, `v2sfip---`, `v2siia---`, `v2siie---`, `v2siip---`, `v2slia---`, `v2slie---`, `v2slim---`, `v2spia---`, `v2spie---`, `v2spma---`, `v2spme---`, `v2spoa---`, `v2spoe---`, `v2spsa---`, `v2spse---`, `v2sria---`, `v2srie---`, `v2srma---`, `v2srme---`, `v2sroa---`, `v2srsa---`, `v2stie---`, `v3-roe---`, `v3daia---`, `v3daim---`, `v3daip---`, `v3daoa---`, `v3dfia---`, `v3dfim---`, `v3diia---`, `v3diie---`, `v3dlia---`, `v3dlie---`, `v3dlim---`, `v3dpia---`, `v3dpie---`, `v3dpma---`, `v3dpme---`, `v3dpsa---`, `v3dria---`, `v3pai----`, `v3paia---`, `v3paie---`, `v3paim---`, `v3paip---`, `v3pamm---`, `v3paoa---`, `v3paoe---`, `v3paom---`, `v3paop---`, `v3pasa---`, `v3pase---`, `v3pasm---`, `v3pasp---`, `v3pfia---`, `v3pfie---`, `v3pfim---`, `v3piia---`, `v3piie---`, `v3piip---`, `v3plia---`, `v3plie---`, `v3plim---`, `v3plip---`, `v3ppia---`, `v3ppie---`, `v3ppip---`, `v3ppma---`, `v3ppme---`, `v3ppoa---`, `v3ppoe---`, `v3ppsa---`, `v3ppse---`, `v3pria---`, `v3prie---`, `v3prip---`, `v3sai----`, `v3saia---`, `v3saie---`, `v3saim---`, `v3saip---`, `v3sama---`, `v3samm---`, `v3samp---`, `v3sana---`, `v3sao----`, `v3saoa---`, `v3saoe---`, `v3saom---`, `v3saop---`, `v3sas----`, `v3sasa---`, `v3sase---`, `v3sasm---`, `v3sasp---`, `v3sfi----`, `v3sfia---`, `v3sfie---`, `v3sfim---`, `v3sfip---`, `v3sfoa---`, `v3sii----`, `v3siia---`, `v3siie---`, `v3siip---`, `v3sli----`, `v3slia---`, `v3slie---`, `v3slim---`, `v3slip---`, `v3spia---`, `v3spie---`, `v3spip---`, `v3spma---`, `v3spme---`, `v3spoa---`, `v3spoe---`, `v3spop---`, `v3spsa---`, `v3spse---`, `v3sria---`, `v3srie---`, `v3srip---`, `v3srma---`, `v3sroa---`, `v3srsa---`, `v3stie---`, `v3stim---`, `v3stip---`, `x--------`, `x-p----d-`, `x-p---nn-` |
| **`morphologizer`** | `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `POS=SCONJ`, `POS=CCONJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid,Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Degree=Pos\|POS=ADV`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Pres\|VerbForm=Inf\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=INTJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Rel`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=DET`, `POS=ADV\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `POS=AUX\|Tense=Fut\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Pos\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=VERB`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Gdv`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Voc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Opt\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Gdv`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Degree=Cmp\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=NUM`, `POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=X`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=DET`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=NUM`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Aspect=Perf\|Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=ADJ\|PronType=Dem`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Degree=Pos\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ\|Person=2\|Poss=Yes`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=DET`, `Case=Dat\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem,Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem,Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem,Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem,Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=ADJ\|Person=1\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Nom\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid,Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid,Pass`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Gender=Masc,Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc,Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|POS=AUX\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Tense=Past\|VerbForm=Inf\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PART`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON`, `POS=DET`, `Case=Gen\|Number=Sing\|POS=PRON`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PRON`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Voc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=PRON`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=X`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=PRON`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `POS=VERB\|Tense=Past\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=PRON`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Nom\|Number=Dual\|POS=PRON`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Acc\|Gender=Neut\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PRON`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Inf`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Dual\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=DET`, `Case=Nom\|Gender=Neut\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|POS=VERB\|Tense=Fut\|VerbForm=Inf\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Opt\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Dual\|POS=PRON`, `Mood=Sub\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Mood=Imp\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Dual\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=X\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Voc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=PRON`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Opt\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1`, `Degree=Sup\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `Case=Voc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Mid`, `POS=VERB\|Tense=Pres\|VerbForm=Inf`, `Aspect=Perf\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Acc\|Number=Dual\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=X`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Mid`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Mid`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Mid`, `Mood=Opt\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 75.00 |
| `POS_ACC` | 90.53 |
| `MORPH_ACC` | 82.31 |
| `DEP_UAS` | 70.57 |
| `DEP_LAS` | 63.07 |
| `SENTS_P` | 68.31 |
| `SENTS_R` | 74.58 |
| `SENTS_F` | 71.31 |
| `LEMMA_ACC` | 93.58 |
| `TOK2VEC_LOSS` | 7048841.90 |
| `TAGGER_LOSS` | 1651010.08 |
| `MORPHOLOGIZER_LOSS` | 2241243.64 |
| `PARSER_LOSS` | 7737282.47 |
| `TRAINABLE_LEMMATIZER_LOSS` | 579140.28 | |
KGsteven/0326bank_transfer_labels-distilroberta-finetuned-banking77 | KGsteven | 2024-03-26T14:11:11Z | 197 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T14:10:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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
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[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 Dataset 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 Dataset 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
automerger/T3qT3qm7-7B | automerger | 2024-03-26T14:10:37Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:nlpguy/T3QM7",
"base_model:finetune:nlpguy/T3QM7",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:09:47Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- nlpguy/T3QM7
---
# T3qT3qm7-7B
T3qT3qm7-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [nlpguy/T3QM7](https://huggingface.co/nlpguy/T3QM7)
## 🧩 Configuration
```yaml
models:
- model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
# No parameters necessary for base model
- model: nlpguy/T3QM7
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/T3qT3qm7-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Ksgk-fy/M7Percival_010.51-0.35-0.38-0.94-0.93-0.25-7B | Ksgk-fy | 2024-03-26T14:09:31Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:AurelPx/Percival_01-7b-slerp",
"base_model:merge:AurelPx/Percival_01-7b-slerp",
"base_model:liminerity/M7-7b",
"base_model:merge:liminerity/M7-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:05:54Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- liminerity/M7-7b
- AurelPx/Percival_01-7b-slerp
---
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/M7-7b
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/M7-7b
parameters:
t:
- filter: self_attn
value: [0.5133343111685227, 0.3507301888180264, 0.3846749585340087, 0.9433114016244468, 0.9348421992124867]
- filter: mlp
value: [0.4866656888314773, 0.6492698111819736, 0.05668859837555318, 0.05668859837555318, 0.06515780078751332]
- value: 0.2528714899743384
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ksgk-fy/M7Percival_010.51-0.35-0.38-0.94-0.93-0.25-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ntvcie/Gemma7bVinhntV1 | ntvcie | 2024-03-26T14:09:04Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T14:03:40Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** ntvcie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
casque/cheerleader_outfit | casque | 2024-03-26T13:59:48Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-03-26T13:58:32Z | ---
license: creativeml-openrail-m
---
|
Brijesh1989/elephant-bab | Brijesh1989 | 2024-03-26T13:55:34Z | 1 | 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 | 2024-03-26T13:51:24Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### elephant-bab Dreambooth model trained by Brijesh1989 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 41534
Sample pictures of this concept:
.jpeg)
|
Ksgk-fy/M7Percival_010.59-0.04-0.08-0.81-0.14-0.11-7B | Ksgk-fy | 2024-03-26T13:55:30Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:AurelPx/Percival_01-7b-slerp",
"base_model:merge:AurelPx/Percival_01-7b-slerp",
"base_model:liminerity/M7-7b",
"base_model:merge:liminerity/M7-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T13:51:53Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- liminerity/M7-7b
- AurelPx/Percival_01-7b-slerp
---
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/M7-7b
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/M7-7b
parameters:
t:
- filter: self_attn
value: [0.5887895977806022, 0.03773014027697541, 0.08485392422713156, 0.8058823253633264, 0.13892156690649637]
- filter: mlp
value: [0.41121040221939775, 0.9622698597230246, 0.19411767463667362, 0.19411767463667362, 0.8610784330935036]
- value: 0.10567183371928668
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ksgk-fy/M7Percival_010.59-0.04-0.08-0.81-0.14-0.11-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
kmfoda/gpt2-2b | kmfoda | 2024-03-26T13:51:06Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T13:46:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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] |
weny22/extract_long_text_balanced_data | weny22 | 2024-03-26T13:50:35Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:weny22/sum_model_t5_saved",
"base_model:finetune:weny22/sum_model_t5_saved",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T13:14:56Z | ---
base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: extract_long_text_balanced_data
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. -->
# extract_long_text_balanced_data
This model is a fine-tuned version of [weny22/sum_model_t5_saved](https://huggingface.co/weny22/sum_model_t5_saved) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3331
- Rouge1: 0.2094
- Rouge2: 0.0794
- Rougel: 0.1697
- Rougelsum: 0.1696
- Gen Len: 18.9853
## 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: 0.002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 119 | 2.3318 | 0.18 | 0.0552 | 0.1439 | 0.1439 | 18.97 |
| No log | 2.0 | 238 | 2.1980 | 0.1899 | 0.06 | 0.1503 | 0.1503 | 18.972 |
| No log | 3.0 | 357 | 2.1448 | 0.1952 | 0.0646 | 0.1542 | 0.1541 | 18.9993 |
| No log | 4.0 | 476 | 2.1372 | 0.1983 | 0.0683 | 0.1574 | 0.1574 | 18.9453 |
| 2.8015 | 5.0 | 595 | 2.1142 | 0.2 | 0.0725 | 0.1611 | 0.1611 | 18.9933 |
| 2.8015 | 6.0 | 714 | 2.0970 | 0.2027 | 0.0757 | 0.1629 | 0.1629 | 18.9987 |
| 2.8015 | 7.0 | 833 | 2.1187 | 0.2027 | 0.0755 | 0.1637 | 0.1634 | 18.968 |
| 2.8015 | 8.0 | 952 | 2.1222 | 0.2013 | 0.0737 | 0.1619 | 0.1618 | 18.9753 |
| 2.02 | 9.0 | 1071 | 2.1316 | 0.2021 | 0.0764 | 0.1648 | 0.1647 | 18.9667 |
| 2.02 | 10.0 | 1190 | 2.1455 | 0.2109 | 0.0784 | 0.169 | 0.1689 | 18.982 |
| 2.02 | 11.0 | 1309 | 2.1580 | 0.2065 | 0.0781 | 0.167 | 0.1669 | 18.968 |
| 2.02 | 12.0 | 1428 | 2.1792 | 0.2088 | 0.0788 | 0.1693 | 0.169 | 18.9767 |
| 1.683 | 13.0 | 1547 | 2.1958 | 0.2085 | 0.0781 | 0.1689 | 0.1689 | 18.9913 |
| 1.683 | 14.0 | 1666 | 2.2436 | 0.2082 | 0.0785 | 0.1693 | 0.1692 | 18.978 |
| 1.683 | 15.0 | 1785 | 2.2480 | 0.2075 | 0.0797 | 0.1678 | 0.1678 | 18.9853 |
| 1.683 | 16.0 | 1904 | 2.2714 | 0.208 | 0.0789 | 0.1686 | 0.1685 | 18.9887 |
| 1.45 | 17.0 | 2023 | 2.2771 | 0.2091 | 0.0787 | 0.1693 | 0.1691 | 18.98 |
| 1.45 | 18.0 | 2142 | 2.2913 | 0.2103 | 0.0792 | 0.17 | 0.1698 | 18.9873 |
| 1.45 | 19.0 | 2261 | 2.3163 | 0.2094 | 0.0792 | 0.1699 | 0.1697 | 18.9893 |
| 1.45 | 20.0 | 2380 | 2.3331 | 0.2094 | 0.0794 | 0.1697 | 0.1696 | 18.9853 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Jamil4321/PurchaseOrder_v2 | Jamil4321 | 2024-03-26T13:50:11Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2024-03-16T11:31:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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] |
casque/beltbra_v0.1 | casque | 2024-03-26T13:49:37Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-03-26T13:49:22Z | ---
license: creativeml-openrail-m
---
|
beomi/gemma-ko-2b | beomi | 2024-03-26T13:48:24Z | 3,952 | 43 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"pytorch",
"ko",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T05:17:14Z | ---
language:
- ko
- en
license: other
library_name: transformers
tags:
- pytorch
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
---
# Gemma-Ko
> Update @ 2024.03.26: First release of Gemma-Ko 2B model
**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B base version of the **Gemma-Ko** model.
**Resources and Technical Documentation**:
* [Original Google's Gemma-2B](https://huggingface.co/google/gemma-2b)
* [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Citation**
```bibtex
@misc {gemma_ko_7b,
author = { {Junbum Lee, Taekyoon Choi} },
title = { gemma-ko-7b },
year = 2024,
url = { https://huggingface.co/beomi/gemma-ko-7b },
doi = { 10.57967/hf/1859 },
publisher = { Hugging Face }
}
```
**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon)
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-2b")
model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-2b")
input_text = "머신러닝과 딥러닝의 차이는"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-2b")
model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-2b", device_map="auto")
input_text = "머신러닝과 딥러닝의 차이는"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
"beomi/gemma-ko-2b",
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated Korean/English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Implementation Information
Details about the model internals.
### Software
Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM).
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
TBD
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
## Acknowledgement
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. |
beomi/gemma-ko-7b | beomi | 2024-03-26T13:48:10Z | 4,686 | 46 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"pytorch",
"ko",
"en",
"doi:10.57967/hf/1859",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-02T02:42:57Z | ---
language:
- ko
- en
license: other
library_name: transformers
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
tags:
- pytorch
---
# Gemma-Ko
> Update @ 2024.03.08: First release of Gemma-Ko 7B model
**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B base version of the **Gemma-Ko** model.
**Resources and Technical Documentation**:
* [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b)
* [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Citation**
```bibtex
@misc {gemma_ko_7b,
author = { {Junbum Lee, Taekyoon Choi} },
title = { gemma-ko-7b },
year = 2024,
url = { https://huggingface.co/beomi/gemma-ko-7b },
doi = { 10.57967/hf/1859 },
publisher = { Hugging Face }
}
```
**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon)
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-7b")
model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b")
input_text = "머신러닝과 딥러닝의 차이는"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-7b")
model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b", device_map="auto")
input_text = "머신러닝과 딥러닝의 차이는"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
"beomi/gemma-ko-7b",
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated Korean/English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Implementation Information
Details about the model internals.
### Software
Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM).
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
TBD
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
## Acknowledgement
The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program. |
mshetairy/bert-base-arabic-finetuned | mshetairy | 2024-03-26T13:47:06Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:asafaya/bert-base-arabic",
"base_model:finetune:asafaya/bert-base-arabic",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T13:46:51Z | ---
base_model: asafaya/bert-base-arabic
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-base-arabic-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-arabic-finetuned
This model is a fine-tuned version of [asafaya/bert-base-arabic](https://huggingface.co/asafaya/bert-base-arabic) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1660
- F1: 0.7561
- Roc Auc: 0.8464
- Accuracy: 0.7180
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1932 | 1.0 | 1133 | 0.1840 | 0.7144 | 0.8252 | 0.6703 |
| 0.1397 | 2.0 | 2266 | 0.1660 | 0.7561 | 0.8464 | 0.7180 |
| 0.0887 | 3.0 | 3399 | 0.1835 | 0.7487 | 0.8508 | 0.7269 |
| 0.0572 | 4.0 | 4532 | 0.2149 | 0.7356 | 0.8434 | 0.7200 |
| 0.0351 | 5.0 | 5665 | 0.2297 | 0.7377 | 0.8453 | 0.7249 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
MikeGreen2710/mlm_listing_3 | MikeGreen2710 | 2024-03-26T13:46:54Z | 160 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-03-26T13:46:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
a-h-m-e-d/results | a-h-m-e-d | 2024-03-26T13:43:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-03T15:31:07Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |
baek26/wiki_asp-educational_institution_8330_bart-base | baek26 | 2024-03-26T13:42:59Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T11:25:22Z | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: wiki_asp-educational_institution_8330_bart-base
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. -->
# wiki_asp-educational_institution_8330_bart-base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6483
- Rouge1: 0.1554
- Rouge2: 0.0598
- Rougel: 0.1308
- Rougelsum: 0.1308
- Gen Len: 19.1331
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.81 | 500 | 2.8696 | 0.1509 | 0.054 | 0.1261 | 0.126 | 19.2999 |
| No log | 3.63 | 1000 | 2.7162 | 0.1551 | 0.0574 | 0.1292 | 0.1293 | 19.1107 |
| No log | 5.44 | 1500 | 2.6720 | 0.154 | 0.0588 | 0.1297 | 0.1295 | 19.071 |
| 2.9542 | 7.26 | 2000 | 2.6582 | 0.1564 | 0.0602 | 0.1312 | 0.1312 | 19.1116 |
| 2.9542 | 9.07 | 2500 | 2.6483 | 0.1554 | 0.0598 | 0.1308 | 0.1308 | 19.1331 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
dbourget/philai-tsade-3e-cosine | dbourget | 2024-03-26T13:40:54Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2024-03-26T13:40:18Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# dbourget/philai-tsade-3e-cosine
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('dbourget/philai-tsade-3e-cosine')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('dbourget/philai-tsade-3e-cosine')
model = AutoModel.from_pretrained('dbourget/philai-tsade-3e-cosine')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=dbourget/philai-tsade-3e-cosine)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7540 with parameters:
```
{'batch_size': 30, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 3770,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3770,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
neopolita/rakutenai-7b-instruct-gguf | neopolita | 2024-03-26T13:39:48Z | 6 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-26T12:54:38Z | ---
{}
---
# GGUF quants for [**Rakuten/RakutenAI-7B-instruct**](https://huggingface.co/Rakuten/RakutenAI-7B-instruct) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/Rakuten/RakutenAI-7B-instruct)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
Ksgk-fy/M7Percival_010.0-0.29-0.91-0.24-0.69-0.44-7B | Ksgk-fy | 2024-03-26T13:38:27Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:AurelPx/Percival_01-7b-slerp",
"base_model:merge:AurelPx/Percival_01-7b-slerp",
"base_model:liminerity/M7-7b",
"base_model:merge:liminerity/M7-7b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T13:34:38Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- liminerity/M7-7b
- AurelPx/Percival_01-7b-slerp
---
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/M7-7b
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/M7-7b
parameters:
t:
- filter: self_attn
value: [0.004914869824223178, 0.2903884922744737, 0.9144751289429321, 0.24184489970656153, 0.692741814637377]
- filter: mlp
value: [0.9950851301757768, 0.7096115077255263, 0.7581551002934385, 0.7581551002934385, 0.30725818536262295]
- value: 0.4390767236118528
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ksgk-fy/M7Percival_010.0-0.29-0.91-0.24-0.69-0.44-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
HanliChu/ppo-Pyramids | HanliChu | 2024-03-26T13:37:19Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2024-03-26T13:37:16Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: HanliChu/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
GeniaV/BERT_Humor_35k | GeniaV | 2024-03-26T13:33:52Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-03-26T13:27:43Z | ### BERT-fine-tune-Humor-Detection ###
license: mit
language: en
pipeline_tag: text-classification
|
Dema99/BonusUnit1 | Dema99 | 2024-03-26T13:33:50Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2024-03-26T13:33:44Z | ---
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: Dema99/BonusUnit1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
manimaranpa07/my_Ws_extraction_model_26th_mar | manimaranpa07 | 2024-03-26T13:26:24Z | 269 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-03-26T13:23:04Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_Ws_extraction_model_26th_mar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_Ws_extraction_model_26th_mar
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2625
- Precision: 0.4942
- Recall: 0.4780
- F1: 0.4860
- Accuracy: 0.9079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 76 | 0.2922 | 0.4641 | 0.4216 | 0.4418 | 0.9028 |
| No log | 2.0 | 152 | 0.2625 | 0.4942 | 0.4780 | 0.4860 | 0.9079 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
|
jth500/t5-rlaif | jth500 | 2024-03-26T13:23:56Z | 165 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T09:14:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
adityarra07/whisper-medium-LoRA | adityarra07 | 2024-03-26T13:21:35Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"region:us"
]
| null | 2024-03-26T00:38:21Z | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-LoRA
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. -->
# whisper-medium-LoRA
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3284
- Wer: 12.2024
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6785 | 1.0 | 789 | 0.4078 | 18.0952 |
| 0.3283 | 2.0 | 1578 | 0.3660 | 19.9802 |
| 0.2298 | 3.0 | 2367 | 0.3391 | 14.2063 |
| 0.1458 | 4.0 | 3156 | 0.3307 | 13.7302 |
| 0.0688 | 5.0 | 3945 | 0.3211 | 12.5 |
| 0.0222 | 6.0 | 4734 | 0.3284 | 12.2024 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Corianas/Tiny-Moe | Corianas | 2024-03-26T13:21:20Z | 151 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"Corianas/Tiny_Test",
"Corianas/TinyTask-minipaca",
"base_model:Corianas/TinyTask-minipaca",
"base_model:merge:Corianas/TinyTask-minipaca",
"base_model:Corianas/Tiny_Test",
"base_model:merge:Corianas/Tiny_Test",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T13:07:14Z | ---
license: cc-by-nc-4.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Corianas/Tiny_Test
- Corianas/TinyTask-minipaca
base_model:
- Corianas/Tiny_Test
- Corianas/TinyTask-minipaca
---
# Tiny-moe
Tiny-moe is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Corianas/Tiny_Test](https://huggingface.co/Corianas/Tiny_Test)
* [Corianas/TinyTask-minipaca](https://huggingface.co/Corianas/TinyTask-minipaca)
## 🧩 Configuration
```yaml
base_model: Corianas/Tiny_Test
gate_mode: cheap_embed # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
## (optional)
# experts_per_token: 2
experts:
- source_model: Corianas/Tiny_Test
positive_prompts:
- "↨once upon a time"
## (optional)
# negative_prompts:
# - "This is a prompt expert_model_1 should not be used for"
- source_model: Corianas/TinyTask-minipaca
# ... and so on
positive_prompts:
- "↨please"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Corianas/Tiny-moe"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
uriel/opus-mt-fr-de-new-finetuned-fr-to-wol | uriel | 2024-03-26T13:18:35Z | 103 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-fr-de",
"base_model:finetune:Helsinki-NLP/opus-mt-fr-de",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T10:17:45Z | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-fr-de
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-fr-de-new-finetuned-fr-to-wol
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. -->
# opus-mt-fr-de-new-finetuned-fr-to-wol
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-fr-de](https://huggingface.co/Helsinki-NLP/opus-mt-fr-de) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4897
- Bleu: 5.5073
- Gen Len: 61.5873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 91 | 3.0424 | 0.4073 | 288.4603 |
| No log | 2.0 | 182 | 2.6751 | 0.9893 | 166.5666 |
| No log | 3.0 | 273 | 2.4795 | 3.1938 | 80.8081 |
| No log | 4.0 | 364 | 2.3826 | 4.1209 | 73.1684 |
| No log | 5.0 | 455 | 2.3629 | 4.858 | 63.6266 |
| 2.5384 | 6.0 | 546 | 2.3635 | 5.1207 | 66.588 |
| 2.5384 | 7.0 | 637 | 2.3929 | 5.3246 | 61.1974 |
| 2.5384 | 8.0 | 728 | 2.4382 | 5.5606 | 63.0014 |
| 2.5384 | 9.0 | 819 | 2.4625 | 5.3472 | 62.4086 |
| 2.5384 | 10.0 | 910 | 2.4897 | 5.5073 | 61.5873 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|
pavi156/crime_model | pavi156 | 2024-03-26T13:09:19Z | 115 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-03-26T13:08:39Z | ---
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: crime_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# crime_model
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 70 | 1.4774 |
| No log | 2.0 | 140 | 1.3600 |
| No log | 3.0 | 210 | 1.3817 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
colaboratory/ponymagine.safetensors | colaboratory | 2024-03-26T13:08:55Z | 0 | 1 | null | [
"art",
"region:us"
]
| null | 2024-03-25T09:34:21Z | ---
tags:
- art
---
## Model Details
### NOTE : not my model its a merge of ponyxl with animaginexl done by somoene as an experiment
### Model Description
recommended settings (by my tests)
CFG : 10-12
CFG mimic (optional) : 7-10
Sampler : euler a or euler a karras
Negatives : more effective in animagine format
Prompt : more effective in animagine format |
joy150/vit-base-beans | joy150 | 2024-03-26T13:03:35Z | 192 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"vision",
"generated_from_trainer",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2024-03-26T12:36:47Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-beans
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. -->
# vit-base-beans
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0146
- Accuracy: 0.9925
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1905 | 1.0 | 130 | 0.3638 | 0.8872 |
| 0.1421 | 2.0 | 260 | 0.1445 | 0.9474 |
| 0.0367 | 3.0 | 390 | 0.0980 | 0.9699 |
| 0.0493 | 4.0 | 520 | 0.0146 | 0.9925 |
| 0.0627 | 5.0 | 650 | 0.0361 | 0.9774 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
|
zarox/catting | zarox | 2024-03-26T13:01:59Z | 4 | 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 | 2024-03-26T10:37:06Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Catting Dreambooth model trained by zarox following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932ZAR
Sample pictures of this concept:
|
KimByeongSu/gpt-neo-125m-cs-finetuning-30000-3 | KimByeongSu | 2024-03-26T12:57:35Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T12:46:47Z | ---
license: mit
base_model: EleutherAI/gpt-neo-125m
tags:
- generated_from_trainer
model-index:
- name: gpt-neo-125m-cs-finetuning-30000-3
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. -->
# gpt-neo-125m-cs-finetuning-30000-3
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: 3.2516
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 393 | 3.3192 |
| 3.3627 | 2.0 | 786 | 3.2649 |
| 3.1273 | 3.0 | 1179 | 3.2516 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.13.1+cu117
- Datasets 2.14.6
- Tokenizers 0.15.0
|
AlignmentResearch/robust_llm_pythia-wl-1b-mz-ada-v3-ch-143000 | AlignmentResearch | 2024-03-26T12:53:42Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T12:51:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-wl-1b-mz-ada-v3-ch-143000
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. -->
# robust_llm_pythia-wl-1b-mz-ada-v3-ch-143000
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Rv-23bit/mistral7B_finetuned_v2.0 | Rv-23bit | 2024-03-26T12:49:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-26T11:18:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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
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### 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 Dataset 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 Dataset 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]
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[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]
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## Model Card Authors [optional]
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[More Information Needed] |
AlignmentResearch/robust_llm_pythia-wl-1b-mz-ada-v3-ch-138000 | AlignmentResearch | 2024-03-26T12:48:57Z | 102 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T12:47:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-wl-1b-mz-ada-v3-ch-138000
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. -->
# robust_llm_pythia-wl-1b-mz-ada-v3-ch-138000
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
AlignmentResearch/robust_llm_pythia-wl-1b-mz-ada-v3-ch-137000 | AlignmentResearch | 2024-03-26T12:45:31Z | 102 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T12:43:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-wl-1b-mz-ada-v3-ch-137000
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. -->
# robust_llm_pythia-wl-1b-mz-ada-v3-ch-137000
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
AlignmentResearch/robust_llm_pythia-wl-1b-mz-ada-v3-ch-142000 | AlignmentResearch | 2024-03-26T12:45:06Z | 103 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T12:43:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-wl-1b-mz-ada-v3-ch-142000
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. -->
# robust_llm_pythia-wl-1b-mz-ada-v3-ch-142000
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
burka9/my-new-shiny-tokenizer | burka9 | 2024-03-26T12:42:12Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-26T12:42:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
chlee10/T3Q-solar-dpo-4bit | chlee10 | 2024-03-26T12:42:02Z | 3 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2024-03-26T12:35:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [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 Dataset 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 Dataset 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]
|
donnyweb/LLaMAntino-2-7b-GGUF-1.0 | donnyweb | 2024-03-26T12:37:31Z | 0 | 0 | null | [
"gguf",
"text-generation-inference",
"it",
"arxiv:2312.09993",
"license:llama2",
"endpoints_compatible",
"region:us"
]
| null | 2024-03-26T12:34:31Z | ---
license: llama2
language:
- it
tags:
- text-generation-inference
---
# Model Card for LLaMAntino-2-7b-dolly
*Last Update: 22/01/2024*<br>
## Model description
<!-- Provide a quick summary of what the model is/does. -->
**LLaMAntino-2-7b-dolly** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-7b** (an italian-adapted **LLaMA 2**).
This model aims to provide Italian NLP researchers with a tool to tackle tasks such as *information extraction* and *closed qa*.
The model was trained following the methodology used for [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and using as training data [dolly-15k-it](https://huggingface.co/datasets/basilepp19/dolly-15k-it) formatted in an instruction-following style.
If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
- **Repository:** https://github.com/swapUniba/LLaMAntino
**NOTICE**: the code has not been released yet, we apologize for the delay, it will be available asap!
- **Developed by:** Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro
- **Funded by:** PNRR project FAIR - Future AI Research
- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
- **Model type:** LLaMA 2
- **Language(s) (NLP):** Italian
- **License:** Llama 2 Community License
- **Finetuned from model:** [swap-uniba/LLaMAntino-2-7b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-7b-hf-ITA)
## Prompt Format
This prompt format based on the Alpaca model was used for fine-tuning:
```python
"Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce ulteriore informazione. " \
"Scrivi una risposta che soddisfi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction}\n\n### Input:\n{input}\n\n### Risposta:\n{response}"
```
If no *input* was present in the instruction, the following prompt was used:
```python
"Di seguito è riportata un'istruzione che descrive un'attività. " \
"Scrivi una risposta che soddisfi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction}\n\n### Risposta:\n{response}"
```
We recommend using the same prompt in inference to obtain the best results!
## How to Get Started with the Model
Below you can find an example of model usage:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "swap-uniba/LLaMAntino-2-7b-hf-dolly-ITA"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
instruction_text = "Estrai i nomi propri di persona dal testo che segue"
input_text = "Marco ha incontrato Matteo per strada e hanno parlato di Mirco"
prompt = "Di seguito è riportata un'istruzione che descrive un'attività, accompagnata da un input che aggiunge ulteriore informazione. " \
f"Scrivi una risposta che completi adeguatamente la richiesta.\n\n" \
f"### Istruzione:\n{instruction_text}\n\n" \
f"### Input:\n{input_text}\n\n" \
f"### Risposta:\n"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
```
If you are facing issues when loading the model, you can try to load it quantized:
```python
model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True)
```
*Note*: The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
*Coming soon*!
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you use this model in your research, please cite the following:
```bibtex
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
*Notice:* Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. [*License*](https://ai.meta.com/llama/license/) |
CHShakish/my-pet-dog | CHShakish | 2024-03-26T12:34:30Z | 5 | 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 | 2024-03-26T12:30:35Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by CHShakish following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 015
Sample pictures of this concept:
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blockblockblock/Rhea-72b-v0.5-bpw4.4 | blockblockblock | 2024-03-26T12:33:46Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
]
| text-generation | 2024-03-26T12:29:38Z | ---
library_name: transformers
license: apache-2.0
language:
- en
---
# Rhea-72b-v0.5
The Rhea project is a project that conducts research on various learning methods to improve llm model performance. We fine-tuned the existing model using the [nox](https://github.com/davidkim205/nox) framework. We built a dataset for SFT learning based on the currently open dataset, and created a dataset using SGD (Self-Generated Dataset Creation Method for DPO Learning) for DPO learning.
Our model ranked first on HuggingFace's Open LLM leaderboard.
## SGD : A Study on Self-Generated Dataset creation method for DPO Learning
This method proposes a novel method for generating datasets for DPO (Self-supervised Learning) models. We suggest a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO models.
## Model Details
* **Model Developers** : davidkim(changyeon kim)
* **Repository** : [https://github.com/davidkim205/nox](https://github.com/davidkim205/nox)
* **base mode** : abacusai/Smaug-72B-v0.1
* **sft dataset** : will be updated soon.
* **dpo dataset** : will be updated soon.
## Evaluation
### [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| **model** | **average** | **arc** | **hellaswag** | **mmlu** | **truthfulQA** | **winogrande** | **GSM8k** |
| ------------- | ----------- | ------- | ------------- | -------- | -------------- | -------------- | --------- |
| Rhea-72b-v0.5 | 81.22 | 79.78 | 91.15 | 77.95 | 74.5 | 87.85 | 76.12 |
|
Anusha64/mistral-7b-aeon-dataset-1k | Anusha64 | 2024-03-26T12:32:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-03-26T12:26:52Z | ---
license: apache-2.0
pipeline_tag: text-generation
---
|
AlignmentResearch/robust_llm_pythia-wl-1b-mz-ada-v3-ch-134000 | AlignmentResearch | 2024-03-26T12:32:27Z | 103 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-03-26T12:30:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-wl-1b-mz-ada-v3-ch-134000
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. -->
# robust_llm_pythia-wl-1b-mz-ada-v3-ch-134000
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
georgysavva/q-FrozenLake-v1-4x4-noSlippery | georgysavva | 2024-03-26T12:32:20Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-03-26T12:32:18Z | ---
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="georgysavva/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"])
```
|
baek26/wiki_asp-written_work_8386_bart-base | baek26 | 2024-03-26T12:32:13Z | 103 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-03-26T10:36:02Z | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: wiki_asp-written_work_8386_bart-base
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. -->
# wiki_asp-written_work_8386_bart-base
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1703
- Rouge1: 0.1259
- Rouge2: 0.0375
- Rougel: 0.1017
- Rougelsum: 0.1018
- Gen Len: 19.446
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 2.12 | 500 | 3.3069 | 0.1207 | 0.0333 | 0.0979 | 0.0978 | 19.5572 |
| No log | 4.25 | 1000 | 3.2072 | 0.1199 | 0.0355 | 0.0974 | 0.0975 | 19.21 |
| No log | 6.37 | 1500 | 3.1806 | 0.1224 | 0.0358 | 0.0992 | 0.0992 | 19.2588 |
| 3.399 | 8.49 | 2000 | 3.1703 | 0.1259 | 0.0375 | 0.1017 | 0.1018 | 19.446 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
abusharique/my-pet-cat | abusharique | 2024-03-26T12:32:04Z | 0 | 0 | null | [
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2024-03-26T12:27:06Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Cat Dreambooth model trained by abusharique following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 2306410100002
Sample pictures of this concept:
|
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