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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 06:27:36
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| likes
int64 0
11.7k
| library_name
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4.05k
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NousResearch/Yarn-Mistral-7b-64k
|
NousResearch
| 2023-11-02T19:00:04Z | 11,249 | 51 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"custom_code",
"en",
"dataset:emozilla/yarn-train-tokenized-16k-mistral",
"arxiv:2309.00071",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-31T02:01:43Z |
---
datasets:
- emozilla/yarn-train-tokenized-16k-mistral
metrics:
- perplexity
library_name: transformers
license: apache-2.0
language:
- en
---
# Model Card: Nous-Yarn-Mistral-7b-64k
[Preprint (arXiv)](https://arxiv.org/abs/2309.00071)
[GitHub](https://github.com/jquesnelle/yarn)
## Model Description
Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method.
It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window.
To use, pass `trust_remote_code=True` when loading the model, for example
```python
model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k",
use_flash_attention_2=True,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True)
```
In addition you will need to use the latest version of `transformers` (until 4.35 comes out)
```sh
pip install git+https://github.com/huggingface/transformers
```
## Benchmarks
Long context benchmarks:
| Model | Context Window | 8k PPL | 16k PPL | 32k PPL | 64k PPL | 128k PPL |
|-------|---------------:|------:|----------:|-----:|-----:|------------:|
| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 2.96 | - | - | - | - |
| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 3.04 | 2.65 | 2.44 | 2.20 | - |
| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 3.08 | 2.68 | 2.47 | 2.24 | 2.19 |
Short context benchmarks showing that quality degradation is minimal:
| Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
|-------|---------------:|------:|----------:|-----:|------------:|
| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 59.98 | 83.31 | 64.16 | 42.15 |
| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 59.38 | 81.21 | 61.32 | 42.50 |
| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 58.87 | 80.58 | 60.64 | 42.46 |
## Collaborators
- [bloc97](https://github.com/bloc97): Methods, paper and evals
- [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals
- [@EnricoShippole](https://twitter.com/EnricoShippole): Model training
- [honglu2875](https://github.com/honglu2875): Paper and evals
The authors would like to thank LAION AI for their support of compute for this model.
It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
|
will010321/unit1_huggy
|
will010321
| 2023-11-02T18:58:57Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-02T18:58:49Z |
---
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: will010321/unit1_huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
benjipeng/distilhubert-finetuned-gtzan-finetuned-gtzan
|
benjipeng
| 2023-11-02T18:50:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:benjipeng/distilhubert-finetuned-gtzan",
"base_model:finetune:benjipeng/distilhubert-finetuned-gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-11-02T15:05:19Z |
---
license: apache-2.0
base_model: benjipeng/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.86
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan-finetuned-gtzan
This model is a fine-tuned version of [benjipeng/distilhubert-finetuned-gtzan](https://huggingface.co/benjipeng/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6143
- Accuracy: 0.86
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8153 | 1.0 | 113 | 0.7412 | 0.8 |
| 0.6209 | 2.0 | 226 | 0.8662 | 0.75 |
| 0.3642 | 3.0 | 339 | 0.5880 | 0.84 |
| 0.2077 | 4.0 | 452 | 0.6017 | 0.85 |
| 0.1658 | 5.0 | 565 | 0.5087 | 0.87 |
| 0.0209 | 6.0 | 678 | 0.6488 | 0.85 |
| 0.1971 | 7.0 | 791 | 0.5813 | 0.87 |
| 0.0047 | 8.0 | 904 | 0.6143 | 0.86 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
kanishka/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-3
|
kanishka
| 2023-11-02T18:45:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-01T11:19:14Z |
---
base_model: models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-3/config.json
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-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. -->
# smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-3
This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-aann-counterfactual-anan-3e-3/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1776
- Accuracy: 0.4302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.003
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.3633 | 1.0 | 18353 | 3.4811 | 0.3866 |
| 3.195 | 2.0 | 36706 | 3.3301 | 0.4025 |
| 3.0724 | 3.0 | 55059 | 3.2650 | 0.4118 |
| 3.0006 | 4.0 | 73412 | 3.2050 | 0.4178 |
| 2.9416 | 5.0 | 91765 | 3.1671 | 0.4220 |
| 2.8996 | 6.0 | 110118 | 3.1491 | 0.4247 |
| 2.8613 | 7.0 | 128471 | 3.1426 | 0.4271 |
| 2.8281 | 8.0 | 146824 | 3.1523 | 0.4264 |
| 2.7997 | 9.0 | 165177 | 3.1559 | 0.4266 |
| 2.7703 | 10.0 | 183530 | 3.1362 | 0.4290 |
| 2.7432 | 11.0 | 201883 | 3.1378 | 0.4296 |
| 2.7268 | 12.0 | 220236 | 3.1547 | 0.4286 |
| 2.7 | 13.0 | 238589 | 3.1532 | 0.4289 |
| 2.6706 | 14.0 | 256942 | 3.1488 | 0.4300 |
| 2.6538 | 15.0 | 275295 | 3.1454 | 0.4307 |
| 2.6324 | 16.0 | 293648 | 3.1518 | 0.4305 |
| 2.6081 | 17.0 | 312001 | 3.1684 | 0.4297 |
| 2.5835 | 18.0 | 330354 | 3.1659 | 0.4302 |
| 2.5653 | 19.0 | 348707 | 3.1638 | 0.4308 |
| 2.542 | 20.0 | 367060 | 3.1776 | 0.4302 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
|
nasreenAkhtar/PongNoFrameSkip-v4
|
nasreenAkhtar
| 2023-11-02T18:35:12Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-11-02T18:35:12Z |
---
license: bigcode-openrail-m
---
|
Britania/workout_plans_model_weights
|
Britania
| 2023-11-02T18:34:04Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-02T18:33:55Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
dhanush23/wav2vec2-audio-emotion-classification
|
dhanush23
| 2023-11-02T18:32:40Z | 16 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-11-02T17:53:58Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-audio-emotion-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-audio-emotion-classification
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
TheBloke/japanese-stablelm-instruct-beta-70B-GGUF
|
TheBloke
| 2023-11-02T18:22:05Z | 612 | 12 |
transformers
|
[
"transformers",
"gguf",
"llama",
"japanese-stablelm",
"causal-lm",
"text-generation",
"ja",
"dataset:kunishou/hh-rlhf-49k-ja",
"dataset:kunishou/databricks-dolly-15k-ja",
"dataset:kunishou/oasst1-89k-ja",
"base_model:stabilityai/japanese-stablelm-instruct-beta-70b",
"base_model:quantized:stabilityai/japanese-stablelm-instruct-beta-70b",
"license:llama2",
"region:us"
] |
text-generation
| 2023-11-02T15:45:23Z |
---
base_model: stabilityai/japanese-stablelm-instruct-beta-70b
datasets:
- kunishou/hh-rlhf-49k-ja
- kunishou/databricks-dolly-15k-ja
- kunishou/oasst1-89k-ja
inference: false
language:
- ja
license:
- llama2
model_creator: Stability AI
model_name: Japanese StableLM Instruct Beta 70B
model_type: llama
pipeline_tag: text-generation
prompt_template: "<s>[INST] <<SYS>>\n\u3042\u306A\u305F\u306F\u5F79\u7ACB\u3064\u30A2\
\u30B7\u30B9\u30BF\u30F3\u30C8\u3067\u3059\u3002\n<<SYS>>\n\n{prompt} [/INST] \n"
quantized_by: TheBloke
tags:
- japanese-stablelm
- causal-lm
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Japanese StableLM Instruct Beta 70B - GGUF
- Model creator: [Stability AI](https://huggingface.co/stabilityai)
- Original model: [Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Stability AI's Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF)
* [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Japanese-StableLM-Llama-2-Chat
```
<s>[INST] <<SYS>>
あなたは役立つアシスタントです。
<<SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `['llama2']`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Stability AI's Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [japanese-stablelm-instruct-beta-70b.Q2_K.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [japanese-stablelm-instruct-beta-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [japanese-stablelm-instruct-beta-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [japanese-stablelm-instruct-beta-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [japanese-stablelm-instruct-beta-70b.Q4_0.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [japanese-stablelm-instruct-beta-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [japanese-stablelm-instruct-beta-70b.Q5_0.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [japanese-stablelm-instruct-beta-70b.Q5_K_S.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [japanese-stablelm-instruct-beta-70b.Q5_K_M.gguf](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF/blob/main/japanese-stablelm-instruct-beta-70b.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| japanese-stablelm-instruct-beta-70b.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| japanese-stablelm-instruct-beta-70b.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-a`
* `japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-b`
### q8_0
Please download:
* `japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-a`
* `japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-* > japanese-stablelm-instruct-beta-70b.Q6_K.gguf && rm japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-*
cat japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-* > japanese-stablelm-instruct-beta-70b.Q8_0.gguf && rm japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-a + japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-b japanese-stablelm-instruct-beta-70b.Q6_K.gguf
del japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-a japanese-stablelm-instruct-beta-70b.Q6_K.gguf-split-b
COPY /B japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-a + japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-b japanese-stablelm-instruct-beta-70b.Q8_0.gguf
del japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-a japanese-stablelm-instruct-beta-70b.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/japanese-stablelm-instruct-beta-70B-GGUF and below it, a specific filename to download, such as: japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GGUF japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GGUF japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n{prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/japanese-stablelm-instruct-beta-70B-GGUF", model_file="japanese-stablelm-instruct-beta-70b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Stability AI's Japanese StableLM Instruct Beta 70B
# Japanese-StableLM-Instruct-Beta-70B
> A cute robot wearing a kimono writes calligraphy with one single brush — [Stable Diffusion XL](https://clipdrop.co/stable-diffusion)
## Model Description
`japanese-stablelm-instruct-beta-70b` is a 70B-parameter decoder-only language model based on [japanese-stablelm-base-beta-70b](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b) and further fine tuned on Databricks Dolly-15k, Anthropic HH, and other public data.
This model is also available in a [smaller 7b version](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b), or a [smaller and faster version with a specialized tokenizer](https://huggingface.co/stabilityai/japanese-stablelm-instruct-ja_vocab-beta-7b).
## Usage
First install additional dependencies in [requirements.txt](./requirements.txt):
```sh
pip install -r requirements.txt
```
Then start generating text with `japanese-stablelm-instruct-beta-70b` by using the following code snippet:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "stabilityai/japanese-stablelm-instruct-beta-70b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# The next line may need to be modified depending on the environment
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
def build_prompt(user_query, inputs):
sys_msg = "<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n"
p = sys_msg + user_query + "\n\n" + inputs + " [/INST] "
return p
# Infer with prompt without any additional input
user_inputs = {
"user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
"inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
)
# this is for reproducibility.
# feel free to change to get different result
seed = 23
torch.manual_seed(seed)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=128,
temperature=0.99,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)
```
We suggest playing with different generation config (`top_p`, `repetition_penalty` etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.
## Model Details
* **Model type**: `japanese-stablelm-instruct-beta-70b` model is an auto-regressive language model based on the Llama2 transformer architecture.
* **Language(s)**: Japanese
* **License**: [Llama2 Community License](https://ai.meta.com/llama/license/).
* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
## Training Dataset
The following datasets were used for the instruction training. Note these are Japanese translated versions of the original datasets, shared by [kunishou](https://huggingface.co/kunishou).
- [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)
- [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
- [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja)
## Use and Limitations
### Intended Use
The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use.
### Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
## Authors
This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by [Takuya Akiba](https://huggingface.co/iwiwi) and [Meng Lee](https://huggingface.co/leemeng). The members of the team are as follows:
- [Meng Lee](https://huggingface.co/leemeng)
- [Fujiki Nakamura](https://huggingface.co/fujiki)
- [Makoto Shing](https://huggingface.co/mkshing)
- [Paul McCann](https://huggingface.co/polm-stability)
- [Takuya Akiba](https://huggingface.co/iwiwi)
- [Naoki Orii](https://huggingface.co/mrorii)
## Acknowledgements
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
<!-- original-model-card end -->
|
reneeshdenny/ppo-SnowballTarget
|
reneeshdenny
| 2023-11-02T18:19:13Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-02T18:19:06Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: reneeshdenny/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jinymusim/metrum-validator
|
jinymusim
| 2023-11-02T18:09:41Z | 0 | 0 | null |
[
"license:cc-by-nd-4.0",
"region:us"
] | null | 2023-11-02T17:55:22Z |
---
license: cc-by-nd-4.0
---
## Czech Metrum Validator.
Validator for metrum. Trained on Czech poetry from github project by
Institute of Czech Literature, Czech Academy of Sciences.
https://github.com/versotym/corpusCzechVerse
## Usage
### Loading model
Download validator.py with interface
Download model and load it by pytorch
```python
import torch
model: ValidatorInterface = (torch.load(args.metre_model_path_full, map_location=torch.device('cpu')))
```
Load base robeczech tokenizer and try it out
```python
tokenizer = = AutoTokenizer.from_pretrained('roberta-base')
model.validate(input_ids=datum["input_ids"], metre=datum["metre"])['acc']
```
### Train Model
```python
meter_model = MeterValidator(pretrained_model=args.pretrained_model)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
training_args = TrainingArguments(
save_strategy = "no",
logging_steps = 500,
warmup_steps = args.worm_up,
weight_decay = 0.0,
num_train_epochs = args.epochs,
learning_rate = args.learning_rate,
fp16 = True if torch.cuda.is_available() else False,
ddp_backend = "nccl",
lr_scheduler_type="cosine",
logging_dir = './logs',
output_dir = './results',
per_device_train_batch_size = args.batch_size)
Trainer(model = rhyme_model,
args = training_args,
train_dataset= train_data.pytorch_dataset_body,
data_collator=collate).train()
```
|
MilanBandara/original_model
|
MilanBandara
| 2023-11-02T18:09:30Z | 2 | 0 |
fasttext
|
[
"fasttext",
"text-classification",
"en",
"region:us"
] |
text-classification
| 2023-10-15T15:49:44Z |
---
language:
- en
library_name: fasttext
pipeline_tag: text-classification
tags:
- fasttext
---
|
Tirendaz/my_ner_model
|
Tirendaz
| 2023-11-02T17:59:18Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-01T11:04:37Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_ner_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.45807770961145194
- name: Recall
type: recall
value: 0.20759962928637626
- name: F1
type: f1
value: 0.2857142857142857
- name: Accuracy
type: accuracy
value: 0.9365995468342525
---
<!-- 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_ner_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2883
- Precision: 0.4581
- Recall: 0.2076
- F1: 0.2857
- Accuracy: 0.9366
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 107 | 0.2985 | 0.3836 | 0.1557 | 0.2215 | 0.9332 |
| No log | 2.0 | 214 | 0.2883 | 0.4581 | 0.2076 | 0.2857 | 0.9366 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
FaryalS/dqn-PongNoFrameskip-v4
|
FaryalS
| 2023-11-02T17:54:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PongNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T15:59:31Z |
---
library_name: stable-baselines3
tags:
- PongNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PongNoFrameskip-v4
type: PongNoFrameskip-v4
metrics:
- type: mean_reward
value: -21.00 +/- 0.00
name: mean_reward
verified: false
---
# **DQN** Agent playing **PongNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **PongNoFrameskip-v4**
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
...
```
|
JanGr/poca-SoccerTwos-v0
|
JanGr
| 2023-11-02T17:49:06Z | 5 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-11-02T17:48:38Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: JanGr/poca-SoccerTwos-v0
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
NeerajG03/t5-small-finetuned
|
NeerajG03
| 2023-11-02T17:37:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:big_patent",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T17:37:24Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- big_patent
model-index:
- name: t5-small-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. -->
# t5-small-finetuned
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the big_patent dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.14.1
|
kaitchup/Llama-2-7b-mt-Indonesian-to-English
|
kaitchup
| 2023-11-02T17:29:38Z | 3 | 0 |
peft
|
[
"peft",
"translation",
"en",
"id",
"dataset:kaitchup/opus-Indonesian-to-English",
"license:mit",
"region:us"
] |
translation
| 2023-10-26T16:53:01Z |
---
library_name: peft
license: mit
language:
- en
- id
datasets:
- kaitchup/opus-Indonesian-to-English
tags:
- translation
---
# Model Card for Model ID
This is an adapter for Meta's Llama 2 7B fine-tuned for translating Indonesian text into English.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** LoRA Adapter for Llama 2 7B
- **Language(s) (NLP):** Indonesian, English
- **License:** MIT license
## Uses
This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning.
You can use the following code to load the model:
```
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
from peft import PeftModel
base_model = "meta-llama/Llama-2-7b-hf"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
original_model_directory, device_map={"": 0}, quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Indonesian-to-English")
```
Then, run the model as follows:
```
my_text = "" #put your text to translate here
prompt = my_text+" ###>"
tokenized_input = tokenizer(prompt, return_tensors="pt")
input_ids = tokenized_input["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
num_beams=10,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=130
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq, skip_special_tokens=True)
print(output.split("###>")[1].strip())
```
## Model Card Contact
[The Kaitchup](https://kaitchup.substack.com/)
|
kaitchup/Llama-2-7b-mt-Vietnamese-to-English
|
kaitchup
| 2023-11-02T17:23:51Z | 4 | 0 |
peft
|
[
"peft",
"translation",
"en",
"vi",
"dataset:kaitchup/opus-Vietnamese-to-English",
"license:mit",
"region:us"
] |
translation
| 2023-10-26T16:54:10Z |
---
library_name: peft
license: mit
language:
- en
- vi
datasets:
- kaitchup/opus-Vietnamese-to-English
tags:
- translation
---
# Model Card for Model ID
This is an adapter for Meta's Llama 2 7B fine-tuned for translating Vietnamese text into English.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** LoRA Adapter for Llama 2 7B
- **Language(s) (NLP):** Vietnamese, English
- **License:** MIT license
## Uses
This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning.
You can use the following code to load the model:
```
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
from peft import PeftModel
base_model = "meta-llama/Llama-2-7b-hf"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
original_model_directory, device_map={"": 0}, quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Vietnamese-to-English")
```
Then, run the model as follows:
```
my_text = "" #put your text to translate here
prompt = my_text+" ###>"
tokenized_input = tokenizer(prompt, return_tensors="pt")
input_ids = tokenized_input["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
num_beams=10,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=130
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq, skip_special_tokens=True)
print(output.split("###>")[1].strip())
```
## Model Card Contact
[The Kaitchup](https://kaitchup.substack.com/)
|
trimble/clip-vit-large-patch14
|
trimble
| 2023-11-02T17:20:47Z | 1 | 0 | null |
[
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"endpoints_compatible",
"region:us"
] | null | 2023-10-31T17:19:00Z |
---
tags:
- vision
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
### Model Date
January 2021
### Model Type
The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
### Use with Transformers
```python
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Performance and Limitations
### Performance
We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
- Food101
- CIFAR10
- CIFAR100
- Birdsnap
- SUN397
- Stanford Cars
- FGVC Aircraft
- VOC2007
- DTD
- Oxford-IIIT Pet dataset
- Caltech101
- Flowers102
- MNIST
- SVHN
- IIIT5K
- Hateful Memes
- SST-2
- UCF101
- Kinetics700
- Country211
- CLEVR Counting
- KITTI Distance
- STL-10
- RareAct
- Flickr30
- MSCOCO
- ImageNet
- ImageNet-A
- ImageNet-R
- ImageNet Sketch
- ObjectNet (ImageNet Overlap)
- Youtube-BB
- ImageNet-Vid
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
## Feedback
### Where to send questions or comments about the model
Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)
|
kaitchup/Llama-2-7b-mt-Norwegian-to-English
|
kaitchup
| 2023-11-02T17:18:30Z | 4 | 1 |
peft
|
[
"peft",
"translation",
"en",
"no",
"dataset:kaitchup/opus-Norwegian-to-English",
"license:mit",
"region:us"
] |
translation
| 2023-10-26T16:59:26Z |
---
library_name: peft
license: mit
language:
- en
- no
datasets:
- kaitchup/opus-Norwegian-to-English
tags:
- translation
---
# Model Card for Model ID
This is an adapter for Meta's Llama 2 7B fine-tuned for translating Norwegian text into English.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** LoRA Adapter for Llama 2 7B
- **Language(s) (NLP):** Norwegian, English
- **License:** MIT license
## Uses
This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning.
You can use the following code to load the model:
```
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
from peft import PeftModel
base_model = "meta-llama/Llama-2-7b-hf"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
original_model_directory, device_map={"": 0}, quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Norwegian-to-English")
```
Then, run the model as follows:
```
my_text = "" #put your text to translate here
prompt = my_text+" ###>"
tokenized_input = tokenizer(prompt, return_tensors="pt")
input_ids = tokenized_input["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
num_beams=10,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=130
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq, skip_special_tokens=True)
print(output.split("###>")[1].strip())
```
## Model Card Contact
[The Kaitchup](https://kaitchup.substack.com/)
|
elemosynov/Taxi-v3
|
elemosynov
| 2023-11-02T17:14:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T17:14:27Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="elemosynov/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Hate-speech-CNERG/indic-abusive-allInOne-MuRIL
|
Hate-speech-CNERG
| 2023-11-02T17:05:31Z | 244 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-24T18:40:29Z |
---
language: [bn, hi, hi-en, ka-en, ma-en, mr, ta-en, ur, ur-en, en]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages**. The allInOne in the name refers to the Joint training/Cross-lingual training, where the model is trained using all the languages data. It is finetuned on MuRIL model.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~
|
wt-golf/distilbert-base-uncased-lora-text-classification-imdb-1k
|
wt-golf
| 2023-11-02T17:00:06Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-02T17:00:04Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
reneeshdenny/dqn-SpaceInvadersNoFrameskip-v4
|
reneeshdenny
| 2023-11-02T16:56:46Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T13:42:28Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 472.00 +/- 173.50
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga reneeshdenny -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga reneeshdenny -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga reneeshdenny
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Phando/switch-base-32-finetuned-hotpotqa
|
Phando
| 2023-11-02T16:54:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T16:50:15Z |
The switch-base-32 model was fine-tuned on the HotpotQA dataset.
Validation exact-match/F1-score: 67.55/84.60.
The prompt key in PromptSource: "generate_answer_affirmative".
|
SuphalerkB/my_model
|
SuphalerkB
| 2023-11-02T16:52:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/mT5_multilingual_XLSum",
"base_model:finetune:csebuetnlp/mT5_multilingual_XLSum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T14:17:25Z |
---
base_model: csebuetnlp/mT5_multilingual_XLSum
tags:
- generated_from_trainer
model-index:
- name: my_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_model
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) 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: 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: linear
- num_epochs: 2
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
nide01/finetuned_IMDB_1000
|
nide01
| 2023-11-02T16:51:21Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-02T14:11:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_IMDB_1000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_IMDB_1000
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: 0.3430
- Accuracy: 0.84
- F1: 0.8400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Tokenizers 0.14.1
|
Phando/switch-base-32-finetuned-copa
|
Phando
| 2023-11-02T16:49:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T15:41:49Z |
The switch-base-32 model was fine-tuned on the COPA dataset.
Validation accuracy: 68.00.
Prompt key in PromptSouce: "cause_effect".
|
Phando/switch-base-32-finetuned-squad
|
Phando
| 2023-11-02T16:47:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T16:42:05Z |
The switch-base-32 model was fine-tuned on the SQuAD dataset.
Validation exact-match/F1-score: 65.39/85.81.
The prompt key in PromptSource: "answer_given_context_and_question".
|
smitbutle/layoutlmv3-finetuned-funsd
|
smitbutle
| 2023-11-02T16:46:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-25T22:51:11Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd
type: funsd
config: funsd
split: test
args: funsd
metrics:
- name: Precision
type: precision
value: 0.7467652495378928
- name: Recall
type: recall
value: 0.8027819175360159
- name: F1
type: f1
value: 0.7737610725401005
- name: Accuracy
type: accuracy
value: 0.8188517770117675
---
<!-- 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. -->
# layoutlmv3-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5984
- Precision: 0.7468
- Recall: 0.8028
- F1: 0.7738
- Accuracy: 0.8189
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.67 | 100 | 1.0197 | 0.5025 | 0.5981 | 0.5462 | 0.6622 |
| No log | 1.34 | 200 | 0.6833 | 0.6203 | 0.7238 | 0.6680 | 0.7608 |
| No log | 2.01 | 300 | 0.6237 | 0.6401 | 0.7794 | 0.7030 | 0.7846 |
| No log | 2.68 | 400 | 0.6028 | 0.6892 | 0.7392 | 0.7133 | 0.7771 |
| 0.8343 | 3.36 | 500 | 0.5948 | 0.7175 | 0.7884 | 0.7512 | 0.7991 |
| 0.8343 | 4.03 | 600 | 0.5953 | 0.7135 | 0.8028 | 0.7555 | 0.7961 |
| 0.8343 | 4.7 | 700 | 0.5925 | 0.7354 | 0.7953 | 0.7642 | 0.8174 |
| 0.8343 | 5.37 | 800 | 0.6055 | 0.7397 | 0.7933 | 0.7656 | 0.8134 |
| 0.8343 | 6.04 | 900 | 0.5940 | 0.7535 | 0.8077 | 0.7797 | 0.8199 |
| 0.3468 | 6.71 | 1000 | 0.5984 | 0.7468 | 0.8028 | 0.7738 | 0.8189 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ydshieh/kosmos-2-patch14-224
|
ydshieh
| 2023-11-02T16:42:01Z | 56 | 54 |
transformers
|
[
"transformers",
"pytorch",
"kosmos-2",
"image-text-to-text",
"custom_code",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-07-29T17:44:41Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Kosmos-2: Grounding Multimodal Large Language Models to the World
**This model (remote code on the Hub) is deprecated. Please use https://huggingface.co/microsoft/kosmos-2-patch14-224**
**There are some changes in terms of input formats: see the model card in https://huggingface.co/microsoft/kosmos-2-patch14-224**
~~**(There is an on going effort to port `Kosmos-2` directly into `transformers`. This repository (remote code) might need some more bug fixes later, including breaking changes.)**~~
<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[An image of a snowman warming himself by a fire.]</b></figcaption></figure></a>
This Hub repository contains a HuggingFace's `transformers` implementation of [the original Kosmos-2 model](https://github.com/microsoft/unilm/tree/master/kosmos-2) from Microsoft.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
prompt = "<grounding>An image of"
url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)
# The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs.
# Uncomment the following 2 lines if you want to match the original demo's outputs.
# (One example is the `two_dogs.jpg` from the demo)
# image.save("new_image.jpg")
# image = Image.open("new_image.jpg")
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"][:, :-1],
attention_mask=inputs["attention_mask"][:, :-1],
img_features=None,
img_attn_mask=inputs["img_attn_mask"][:, :-1],
use_cache=True,
max_new_tokens=64,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Specify `cleanup_and_extract=False` in order to see the raw model generation.
processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
print(processed_text)
# `<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.`
# By default, the generated text is cleanup and the entities are extracted.
processed_text, entities = processor.post_process_generation(generated_text)
print(processed_text)
# `An image of a snowman warming himself by a fire.`
print(entities)
# `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`
```
## Draw the bounding bboxes of the entities on the image
Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image:
```python
import cv2
import numpy as np
import os
import requests
import torch
import torchvision.transforms as T
from PIL import Image
def is_overlapping(rect1, rect2):
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
def draw_entity_boxes_on_image(image, entities, show=False, save_path=None):
"""_summary_
Args:
image (_type_): image or image path
collect_entity_location (_type_): _description_
"""
if isinstance(image, Image.Image):
image_h = image.height
image_w = image.width
image = np.array(image)[:, :, [2, 1, 0]]
elif isinstance(image, str):
if os.path.exists(image):
pil_img = Image.open(image).convert("RGB")
image = np.array(pil_img)[:, :, [2, 1, 0]]
image_h = pil_img.height
image_w = pil_img.width
else:
raise ValueError(f"invaild image path, {image}")
elif isinstance(image, torch.Tensor):
# pdb.set_trace()
image_tensor = image.cpu()
reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
pil_img = T.ToPILImage()(image_tensor)
image_h = pil_img.height
image_w = pil_img.width
image = np.array(pil_img)[:, :, [2, 1, 0]]
else:
raise ValueError(f"invaild image format, {type(image)} for {image}")
if len(entities) == 0:
return image
new_image = image.copy()
previous_bboxes = []
# size of text
text_size = 1
# thickness of text
text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1))
box_line = 3
(c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
base_height = int(text_height * 0.675)
text_offset_original = text_height - base_height
text_spaces = 3
for entity_name, (start, end), bboxes in entities:
for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes:
orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
# draw bbox
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)
l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1
x1 = orig_x1 - l_o
y1 = orig_y1 - l_o
if y1 < text_height + text_offset_original + 2 * text_spaces:
y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
x1 = orig_x1 + r_o
# add text background
(text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
for prev_bbox in previous_bboxes:
while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
y1 += (text_height + text_offset_original + 2 * text_spaces)
if text_bg_y2 >= image_h:
text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
text_bg_y2 = image_h
y1 = image_h
break
alpha = 0.5
for i in range(text_bg_y1, text_bg_y2):
for j in range(text_bg_x1, text_bg_x2):
if i < image_h and j < image_w:
if j < text_bg_x1 + 1.35 * c_width:
# original color
bg_color = color
else:
# white
bg_color = [255, 255, 255]
new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8)
cv2.putText(
new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
)
# previous_locations.append((x1, y1))
previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
if save_path:
pil_image.save(save_path)
if show:
pil_image.show()
return new_image
# (The same image from the previous code example)
url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# From the previous code example
entities = [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
# Draw the bounding bboxes
draw_entity_boxes_on_image(image, entities, show=True)
```
Here is the annotated image:
<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a>
## Tasks
This model is capable of performing different tasks through changing the prompts.
First, let's define a function to run a prompt.
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)
def run_example(prompt):
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"][:, :-1],
attention_mask=inputs["attention_mask"][:, :-1],
img_features=None,
img_attn_mask=inputs["img_attn_mask"][:, :-1],
use_cache=True,
max_new_tokens=64,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
_processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
processed_text, entities = processor.post_process_generation(generated_text)
print(processed_text)
print(entities)
print(_processed_text)
```
Here are the tasks `Kosmos-2` could perform:
### Multimodal Grounding
#### • Phrase Grounding
```python
prompt = "<grounding><phrase> a snowman</phrase>"
run_example(prompt)
# a snowman is warming himself by the fire
# [('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])]
# <grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object>
```
#### • Referring Expression Comprehension
```python
prompt = "<grounding><phrase> a snowman next to a fire</phrase>"
run_example(prompt)
# a snowman next to a fire
# [('a snowman next to a fire', (0, 24), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding><phrase> a snowman next to a fire</phrase><object><patch_index_0044><patch_index_0863></object>
```
### Multimodal Referring
#### • Referring expression generation
```python
prompt = "<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is"
run_example(prompt)
# It is snowman in a hat and scarf
# [('It', (0, 2), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is snowman in a hat and scarf
```
### Perception-Language Tasks
#### • Grounded VQA
```python
prompt = "<grounding> Question: What is special about this image? Answer:"
run_example(prompt)
# Question: What is special about this image? Answer: The image features a snowman sitting by a campfire in the snow.
# [('a snowman', (71, 80), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (92, 102), [(0.109375, 0.640625, 0.546875, 0.984375)])]
# <grounding> Question: What is special about this image? Answer: The image features<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> sitting by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object> in the snow.
```
#### • Grounded VQA with multimodal referring via bounding boxes
```python
prompt = "<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer:"
run_example(prompt)
# Question: Where is the fire next to? Answer: Near the snowman.
# [('the fire', (19, 27), [(0.171875, 0.015625, 0.484375, 0.890625)]), ('the snowman', (50, 61), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer: Near<phrase> the snowman</phrase><object><patch_index_0044><patch_index_0863></object>.
```
### Grounded Image captioning
#### • Brief
```python
prompt = "<grounding> An image of"
run_example(prompt)
# An image of a snowman warming himself by a campfire.
# [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (41, 51), [(0.109375, 0.640625, 0.546875, 0.984375)])]
# <grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object>.
```
#### • Detailed
```python
prompt = "<grounding> Describe this image in detail:"
run_example(prompt)
# Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is wearing a hat, scarf, and gloves, with a pot nearby and a cup
# [('a campfire', (71, 81), [(0.171875, 0.015625, 0.484375, 0.984375)]), ('a hat', (109, 114), [(0.515625, 0.046875, 0.828125, 0.234375)]), ('scarf', (116, 121), [(0.515625, 0.234375, 0.890625, 0.578125)]), ('gloves', (127, 133), [(0.515625, 0.390625, 0.640625, 0.515625)]), ('a pot', (140, 145), [(0.078125, 0.609375, 0.265625, 0.859375)])]
# <grounding> Describe this image in detail: The image features a snowman sitting by<phrase> a campfire</phrase><object><patch_index_0005><patch_index_1007></object> in the snow. He is wearing<phrase> a hat</phrase><object><patch_index_0048><patch_index_0250></object>,<phrase> scarf</phrase><object><patch_index_0240><patch_index_0604></object>, and<phrase> gloves</phrase><object><patch_index_0400><patch_index_0532></object>, with<phrase> a pot</phrase><object><patch_index_0610><patch_index_0872></object> nearby and<phrase> a cup</phrase><object>
```
## Running the Flask Server
_flask_kosmos2.py_ shows the implementation of a Flask server for the model.
It allowes the model to be approached as a REST API.
After starting the server. You can send a POST request to `http://localhost:8005/process_prompt` with the following form data:
- `prompt`: For example `<grounding> an image of`
- `image`: The image file as binary data
This in turn will produce a reply with the following JSON format:
- `message`: The Kosmos-2 generated text
- `entities`: The extracted entities
An easy way to test this is through an application like Postman. Make sure the image field is set to `File`.
```python
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
from flask import Flask, request, jsonify
import json
app = Flask(__name__)
model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
@app.route('/process_prompt', methods=['POST'])
def process_prompt():
try:
# Get the uploaded image data from the POST request
uploaded_file = request.files['image']
prompt = request.form.get('prompt')
image = Image.open(uploaded_file.stream)
print(image.size)
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"][:, :-1],
attention_mask=inputs["attention_mask"][:, :-1],
img_features=None,
img_attn_mask=inputs["img_attn_mask"][:, :-1],
use_cache=True,
max_new_tokens=64,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# By default, the generated text is cleanup and the entities are extracted.
processed_text, entities = processor.post_process_generation(generated_text)
parsed_entities = entities_to_json(entities)
print(generated_text)
print(processed_text)
return jsonify({"message": processed_text, 'entities': parsed_entities})
except Exception as e:
return jsonify({"error": str(e)})
def entities_to_json(entities):
result = []
for e in entities:
label = e[0]
box_coords = e[1]
box_size = e[2][0]
entity_result = {
"label": label,
"boundingBoxPosition": {"x": box_coords[0], "y": box_coords[1]},
"boundingBox": {"x_min": box_size[0], "y_min": box_size[1], "x_max": box_size[2], "y_max": box_size[3]}
}
print(entity_result)
result.append(entity_result)
return result
if __name__ == '__main__':
app.run(host='localhost', port=8005)
```
|
Phando/switch-base-32-finetuned-winogrande
|
Phando
| 2023-11-02T16:41:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T16:35:54Z |
The switch-base-32 model was fine-tuned on the WinoGrande dataset.
Validation accuracy: 61.80.
The prompt key in PromptSouce: "True or False"
|
Phando/switch-base-32-finetuned-multirc
|
Phando
| 2023-11-02T16:33:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"switch_transformers",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T16:20:26Z |
The switch-base-32 model was fine-tuned on the MultiRC dataset.
Validation F1-score: 76.19.
Prompt key in PromptSource: "found_this_answer".
|
nickrobinson/distilbert-base-uncased-finetuned-imdb
|
nickrobinson
| 2023-11-02T16:21:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-02T16:12:10Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4966 |
| 2.5796 | 2.0 | 314 | 2.4282 |
| 2.5355 | 3.0 | 471 | 2.4510 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
liy140/bert-base-uncased_relevance_extractor_secondary_binary
|
liy140
| 2023-11-02T16:12:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-02T16:10:23Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-uncased_relevance_extractor_secondary_binary
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-uncased_relevance_extractor_secondary_binary
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2620
- F1: 0.8404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 24 | 0.2620 | 0.8404 |
| No log | 2.0 | 48 | 0.3159 | 0.8522 |
| No log | 3.0 | 72 | 0.2905 | 0.8604 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
hodgesz/llama-2-7b-chat-text2sql-test
|
hodgesz
| 2023-11-02T16:09:53Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-02T16:02:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
AperMesa/ppo-LunarLander-v2
|
AperMesa
| 2023-11-02T16:05:42Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T16:05:20Z |
---
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: 244.88 +/- 23.42
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
...
```
|
Nathamon/bert-finetuned-mrpc
|
Nathamon
| 2023-11-02T16:05:23Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-20T10:48:59Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-finetuned-mrpc
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.12.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
kwwww/bert-base-uncased-test_64_1000
|
kwwww
| 2023-11-02T16:03:58Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"region:us"
] | null | 2023-11-01T17:07:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
fearvel/CutifiedAnimeCharacterDesign_sdxl_lora_v1
|
fearvel
| 2023-11-02T16:02:19Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"text-to-image",
"StableDiffusionPipeline",
"stable-diffusion-diffusers",
"region:us"
] |
text-to-image
| 2023-11-02T15:26:45Z |
---
tags:
- stable-diffusion
- text-to-image
- diffusers
- StableDiffusionPipeline
- stable-diffusion-diffusers
---
## Model
|
devrunner09/llama2-qa-law-26k-v2
|
devrunner09
| 2023-11-02T15:56:07Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-02T15:47:01Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
akidse/dqn-SpaceInvadersNoFrameskip-v4
|
akidse
| 2023-11-02T15:46:54Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T15:46:20Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 580.50 +/- 125.05
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akidse -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga akidse -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga akidse
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
bartowski/SciPhi-Mistral-7B-32k-exl2
|
bartowski
| 2023-11-02T15:42:12Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-11-02T13:24:30Z |
---
license: mit
quantized_by: bartowski
---
## Exllama v2 Quantizations of SciPhi-Mistral-7B-32k
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext.parquet as calibration dataset.
Original model: https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k
<a href="https://huggingface.co/bartowski/SciPhi-Mistral-7B-32k-exl2//tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SciPhi-Mistral-7B-32k-exl2//tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SciPhi-Mistral-7B-32k-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/SciPhi-Mistral-7B-32k-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `SciPhi-Mistral-7B-32k-exl2`:
```shell
mkdir SciPhi-Mistral-7B-32k-exl2
huggingface-cli download bartowski/SciPhi-Mistral-7B-32k-exl2 --local-dir SciPhi-Mistral-7B-32k-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SciPhi-Mistral-7B-32k-exl2
huggingface-cli download bartowski/SciPhi-Mistral-7B-32k-exl2 --revision 4.0 --local-dir SciPhi-Mistral-7B-32k-exl2 --local-dir-use-symlinks False
```
|
nrynischdva/Trial_Fine_Tuning
|
nrynischdva
| 2023-11-02T15:34:28Z | 0 | 0 | null |
[
"en",
"dataset:pritam1984314/cool_job_dataset",
"region:us"
] | null | 2023-11-02T10:36:09Z |
---
datasets:
- pritam1984314/cool_job_dataset
metrics:
- accuracy
language:
- en
---
|
sakelariev/bg_news_trf
|
sakelariev
| 2023-11-02T15:34:28Z | 10 | 0 |
spacy
|
[
"spacy",
"ner",
"named entity recognition",
"token-classification",
"bg",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] |
token-classification
| 2023-11-01T12:03:56Z |
---
license: cc-by-nc-sa-3.0
language:
- bg
metrics:
- accuracy
library_name: spacy
pipeline_tag: token-classification
model-index:
- name: bg_news_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8890829694
- name: NER Recall
type: recall
value: 0.8886948931
- name: NER F Score
type: f_score
value: 0.8888888889
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9702076246
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9897910505
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9764380425
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9404442198
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9349327787
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8934417103
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9241131567
tags:
- ner
- named entity recognition
- spacy
---
| Feature | Description |
| --- | --- |
| **Name** | `bg_news_trf` |
| **Version** | `3.5.4` |
| **spaCy** | `>=3.5.4,<3.6.0` |
| **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` |
| **Components** | `transformer`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB) (Kiril Simov and Petya Osenova) <br> [BERT multilingual base model (uncased)](https://huggingface.co/bert-base-multilingual-uncased) (Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova)|
| **License** | CC-BY-NC-SA-3.0 |
| **Author** | [Ivaylo Sakelariev](https://github.com/sakelariev) |
Bulgarian transformers pipeline for BGspaCy. NER model was trained on privately annotated data (looking for the best way to share the dataset currently).
Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner
### Label Scheme
<details>
<summary>View label scheme (999 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `A`, `A-pd`, `A-pi`, `Afsd`, `Afsi`, `Ams-e`, `Amsf`, `Amsh`, `Amsi`, `Ansd`, `Ansi`, `Cc`, `Cp`, `Cr`, `Cs`, `D`, `Dd`, `Dl`, `Dm`, `Dq`, `Dt`, `H-pd`, `H-pi`, `Hfsd`, `Hfsi`, `Hmsf`, `Hmsh`, `Hmsi`, `Hnsi`, `I`, `M`, `Mc--d`, `Mc--i`, `Mc-pd`, `Mc-pi`, `Mc-si`, `Mcf-d`, `Mcf-i`, `Mcfpd`, `Mcfpi`, `Mcfsd`, `Mcfsi`, `Mcm-i`, `Mcmpd`, `Mcmpi`, `Mcmsf`, `Mcmsi`, `Mcn-d`, `Mcn-i`, `Mcnpd`, `Mcnpi`, `Mcnsd`, `Mcnsi`, `Md--d`, `Md--i`, `Md-pd`, `Md-pi`, `Mo-pd`, `Mo-pi`, `Mofsd`, `Mofsi`, `Momsf`, `Momsh`, `Momsi`, `Monsd`, `Monsi`, `My--i`, `My-pi`, `Nc`, `Nc-ld`, `Nc-li`, `Ncfpd`, `Ncfpi`, `Ncfs-v`, `Ncfsd`, `Ncfsi`, `Ncmpd`, `Ncmpi`, `Ncms-v`, `Ncmsd`, `Ncmsf`, `Ncmsh`, `Ncmsi`, `Ncmt`, `Ncnpd`, `Ncnpi`, `Ncnsd`, `Ncnsi`, `Np`, `Np-li`, `Np-pi`, `Npfpd`, `Npfpi`, `Npfs-v`, `Npfsd`, `Npfsi`, `Npmpd`, `Npmpi`, `Npms-a`, `Npms-v`, `Npmsd`, `Npmsf`, `Npmsh`, `Npmsi`, `Npnpd`, `Npnpi`, `Npnsd`, `Npnsi`, `Pca--p`, `Pca--s-f`, `Pca--s-m`, `Pca--s-n`, `Pce-as-m`, `Pce-op`, `Pce-os-f`, `Pce-os-m`, `Pce-os-n`, `Pcl`, `Pcq--s-nd`, `Pda--p`, `Pda--s-f`, `Pda--s-m`, `Pda--s-n`, `Pde-op`, `Pde-os-f`, `Pde-os-m`, `Pde-os-n`, `Pdl`, `Pdm`, `Pdq`, `Pds`, `Pdt`, `Pfa--p`, `Pfa--s-f`, `Pfa--s-m`, `Pfa--s-n`, `Pfe-as-m`, `Pfe-op`, `Pfe-op--d`, `Pfe-op--i`, `Pfe-os-f`, `Pfe-os-fd`, `Pfe-os-fi`, `Pfe-os-m`, `Pfe-os-mf`, `Pfe-os-mh`, `Pfe-os-mi`, `Pfe-os-n`, `Pfe-os-ni`, `Pfl`, `Pfm`, `Pfp--s-n`, `Pfq-----i`, `Pft`, `Pfy-----i`, `Pia--p`, `Pia--s-f`, `Pia--s-m`, `Pia--s-n`, `Pic`, `Pie-as-m`, `Pie-op`, `Pie-os-f`, `Pie-os-m`, `Pie-os-n`, `Pil`, `Pim`, `Pip--s-f`, `Piq`, `Pit`, `Pna--p`, `Pna--s-f`, `Pna--s-m`, `Pna--s-n`, `Pne-as-m`, `Pne-ds-m`, `Pne-os-f`, `Pne-os-m`, `Pne-os-nd`, `Pne-os-ni`, `Pnl`, `Pnm`, `Pnp--s-f`, `Pnt`, `Ppe-op1`, `Ppe-op2`, `Ppe-op3`, `Ppe-os1`, `Ppe-os2`, `Ppe-os3f`, `Ppe-os3m`, `Ppe-os3n`, `Ppelap1`, `Ppelap2`, `Ppelap3`, `Ppelas1`, `Ppelas2`, `Ppelas3f`, `Ppelas3m`, `Ppelas3n`, `Ppeldp3`, `Ppelds1`, `Ppelds3m`, `Ppetap1`, `Ppetap2`, `Ppetap3`, `Ppetas1`, `Ppetas2`, `Ppetas3f`, `Ppetas3m`, `Ppetas3n`, `Ppetdp1`, `Ppetdp2`, `Ppetdp3`, `Ppetds1`, `Ppetds2`, `Ppetds3f`, `Ppetds3m`, `Ppetds3n`, `Ppetsp1`, `Ppetsp2`, `Ppetsp3`, `Ppetss1`, `Ppetss2`, `Ppetss3f`, `Ppetss3m`, `Pph-os2`, `Pphlas2`, `Pphtas2`, `Pphtds2`, `Pphtss2`, `Ppxla`, `Ppxta`, `Ppxtd`, `Ppxts`, `Pra--p`, `Pra--s`, `Pra--s-f`, `Pra--s-m`, `Pra--s-n`, `Prc`, `Pre--s`, `Pre-as-m`, `Pre-op`, `Pre-os-f`, `Pre-os-m`, `Pre-os-n`, `Prl`, `Prm`, `Prp--p`, `Prp--s-f`, `Prp--s-m`, `Prp--s-n`, `Prq`, `Prt`, `Pshl-p2-d`, `Pshl-p2-i`, `Pshl-s2fd`, `Pshl-s2fi`, `Pshl-s2mf`, `Pshl-s2mh`, `Pshl-s2mi`, `Pshl-s2nd`, `Pshl-s2ni`, `Psht--2`, `Psol-p1-d`, `Psol-p2-d`, `Psol-p3-df`, `Psol-p3-dm`, `Psol-p3-dn`, `Psol-p3-if`, `Psol-p3-im`, `Psol-s1fd`, `Psol-s1fi`, `Psol-s1mf`, `Psol-s1mh`, `Psol-s1mi`, `Psol-s1nd`, `Psol-s1ni`, `Psol-s2ni`, `Psol-s3fdf`, `Psol-s3fdm`, `Psol-s3fdn`, `Psol-s3fif`, `Psol-s3fim`, `Psol-s3mff`, `Psol-s3mfm`, `Psol-s3mfn`, `Psol-s3mhf`, `Psol-s3mhm`, `Psol-s3mhn`, `Psol-s3mim`, `Psol-s3min`, `Psol-s3ndf`, `Psol-s3ndm`, `Psol-s3ndn`, `Psol-s3nim`, `Psol-s3nin`, `Psot--1`, `Psot--2`, `Psot--3--f`, `Psot--3--m`, `Psot--3--n`, `Psxlop--d`, `Psxlop--i`, `Psxlos-fd`, `Psxlos-fi`, `Psxlos-mh`, `Psxlos-mi`, `Psxlos-nd`, `Psxlos-ni`, `Psxto`, `Pszl-p1-d`, `Pszl-p1-i`, `Pszl-p3-d`, `Pszl-p3-i`, `Pszl-s1fd`, `Pszl-s1fi`, `Pszl-s1mf`, `Pszl-s1mh`, `Pszl-s1mi`, `Pszl-s1nd`, `Pszl-s1ni`, `Pszl-s2fd`, `Pszl-s2mh`, `Pszl-s2mi`, `Pszl-s2nd`, `Pszl-s3fd`, `Pszl-s3fi`, `Pszl-s3mf`, `Pszl-s3mh`, `Pszl-s3mi`, `Pszl-s3nd`, `Pszl-s3ni`, `Pszt--1`, `Pszt--2`, `Pszt--3`, `R`, `T`, `Ta`, `Te`, `Ti`, `Tm`, `Tn`, `Tt`, `Tv`, `Tx`, `Unknown`, `V`, `Viitf-r3p`, `Vniicam-sni`, `Vniicao-sni`, `Vniif-m3s`, `Vniif-o3s`, `Vniif-r3s`, `Vnitcam-sni`, `Vnitcao-sni`, `Vnitf-m3s`, `Vnitf-r3s`, `Vnpicao-sni`, `Vnpif-o3s`, `Vnpif-r3s`, `Vnptcao-sni`, `Vnptf-m3s`, `Vpiicam-p-i`, `Vpiicam-sfi`, `Vpiicam-smi`, `Vpiicam-sni`, `Vpiicao-p-d`, `Vpiicao-p-i`, `Vpiicao-sfi`, `Vpiicao-smi`, `Vpiicao-sni`, `Vpiicar-p-d`, `Vpiicar-p-i`, `Vpiicar-sfd`, `Vpiicar-sfi`, `Vpiicar-smf`, `Vpiicar-smh`, `Vpiicar-smi`, `Vpiicar-snd`, `Vpiicar-sni`, `Vpiicv--sni`, `Vpiif-m1p`, `Vpiif-m1s`, `Vpiif-m2s`, `Vpiif-m3p`, `Vpiif-m3s`, `Vpiif-o1p`, `Vpiif-o1s`, `Vpiif-o3p`, `Vpiif-o3s`, `Vpiif-r1p`, `Vpiif-r1s`, `Vpiif-r2p`, `Vpiif-r2s`, `Vpiif-r3p`, `Vpiif-r3s`, `Vpiig`, `Vpiiz--2p`, `Vpiiz--2s`, `Vpitcam-p-i`, `Vpitcam-sfi`, `Vpitcam-smi`, `Vpitcam-sni`, `Vpitcao-p-i`, `Vpitcao-sfi`, `Vpitcao-smi`, `Vpitcao-sni`, `Vpitcar-p-d`, `Vpitcar-p-i`, `Vpitcar-sfd`, `Vpitcar-sfi`, `Vpitcar-smf`, `Vpitcar-smh`, `Vpitcar-smi`, `Vpitcar-snd`, `Vpitcar-sni`, `Vpitcv--p-d`, `Vpitcv--p-i`, `Vpitcv--sfd`, `Vpitcv--sfi`, `Vpitcv--smf`, `Vpitcv--smh`, `Vpitcv--smi`, `Vpitcv--snd`, `Vpitcv--sni`, `Vpitf-m1p`, `Vpitf-m1s`, `Vpitf-m2p`, `Vpitf-m2s`, `Vpitf-m3p`, `Vpitf-m3s`, `Vpitf-o1p`, `Vpitf-o1s`, `Vpitf-o2p`, `Vpitf-o2s`, `Vpitf-o3p`, `Vpitf-o3s`, `Vpitf-r1p`, `Vpitf-r1s`, `Vpitf-r2p`, `Vpitf-r2s`, `Vpitf-r3p`, `Vpitf-r3s`, `Vpitg`, `Vpitz--2p`, `Vpitz--2s`, `Vppicao-p-d`, `Vppicao-p-i`, `Vppicao-sfd`, `Vppicao-sfi`, `Vppicao-smf`, `Vppicao-smh`, `Vppicao-smi`, `Vppicao-snd`, `Vppicao-sni`, `Vppif-m3p`, `Vppif-m3s`, `Vppif-o1p`, `Vppif-o1s`, `Vppif-o2s`, `Vppif-o3p`, `Vppif-o3s`, `Vppif-r1p`, `Vppif-r1s`, `Vppif-r2p`, `Vppif-r2s`, `Vppif-r3p`, `Vppif-r3s`, `Vppiz--2p`, `Vppiz--2s`, `Vpptcam-smi`, `Vpptcao-p-d`, `Vpptcao-p-i`, `Vpptcao-sfd`, `Vpptcao-sfi`, `Vpptcao-smh`, `Vpptcao-smi`, `Vpptcao-snd`, `Vpptcao-sni`, `Vpptcv--p-d`, `Vpptcv--p-i`, `Vpptcv--sfd`, `Vpptcv--sfi`, `Vpptcv--smf`, `Vpptcv--smh`, `Vpptcv--smi`, `Vpptcv--snd`, `Vpptcv--sni`, `Vpptf-m3p`, `Vpptf-m3s`, `Vpptf-o1p`, `Vpptf-o1s`, `Vpptf-o2p`, `Vpptf-o2s`, `Vpptf-o3p`, `Vpptf-o3s`, `Vpptf-r1p`, `Vpptf-r1s`, `Vpptf-r2p`, `Vpptf-r2s`, `Vpptf-r3p`, `Vpptf-r3s`, `Vpptz--2p`, `Vpptz--2s`, `Vxitcat-p-i`, `Vxitcat-sfi`, `Vxitcat-smi`, `Vxitcat-sni`, `Vxitf-r1p`, `Vxitf-r1s`, `Vxitf-r2p`, `Vxitf-r2s`, `Vxitf-r3p`, `Vxitf-r3s`, `Vxitf-t1p`, `Vxitf-t1s`, `Vxitf-t2p`, `Vxitf-t2s`, `Vxitf-t3p`, `Vxitf-t3s`, `Vxitu-o1p`, `Vxitu-o1s`, `Vxitu-o2p`, `Vxitu-o2s`, `Vxitu-o3p`, `Vxitu-o3s`, `Vyptf-o3s`, `Vyptf-r1p`, `Vyptf-r1s`, `Vyptf-r2p`, `Vyptf-r2s`, `Vyptf-r3p`, `Vyptf-r3s`, `punct` |
| **`morphologizer`** | `POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=AUX`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART\|Polarity=Neg`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=INTJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=CCONJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Ind\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Number=Ptan\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|POS=PRON\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Dem`, `POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Ptan\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Degree=Pos\|NumType=Card\|Number=Plur\|POS=ADV`, `POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `Definite=Def\|Degree=Pos\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|POS=PRON\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Rel`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Definite=Def\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Animacy=Anim\|Definite=Ind\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `POS=ADV\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=SCONJ`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Def\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Count\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `POS=ADV\|PronType=Int`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NumType=Card\|POS=ADV\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Def\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NumType=Card\|POS=ADV\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Number=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `NumType=Card\|POS=ADV\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Cmp\|NumType=Card\|Number=Plur\|POS=ADV`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Def\|Degree=Cmp\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Degree=Pos\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Sup\|NumType=Card\|Number=Plur\|POS=ADV`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Degree=Cmp\|POS=ADV\|PronType=Dem`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `POS=PROPN`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Ptan\|POS=PROPN`, `Definite=Def\|Degree=Sup\|NumType=Card\|Number=Plur\|POS=ADV`, `Definite=Ind\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADJ`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `NumType=Ord\|POS=NUM`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=X`, `Foreign=Yes\|POS=X`, `Aspect=Imp\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Cmp\|NumType=Card\|Number=Plur\|POS=ADV`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 97.02 |
| `POS_ACC` | 98.98 |
| `MORPH_ACC` | 97.64 |
| `LEMMA_ACC` | 94.04 |
| `DEP_UAS` | 93.49 |
| `DEP_LAS` | 89.34 |
| `SENTS_P` | 92.62 |
| `SENTS_R` | 92.20 |
| `SENTS_F` | 92.41 |
| `ENTS_F` | 88.91 |
| `ENTS_P` | 88.87 |
| `ENTS_R` | 88.89 |
|
GiantTreeG/german-jeopardy-longt5-large-128
|
GiantTreeG
| 2023-11-02T15:27:25Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"longt5",
"text2text-generation",
"question-generation",
"german",
"generated_from_trainer",
"de",
"dataset:lmqg/qg_dequad",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T15:23:16Z |
---
language:
- de
tags:
- question-generation
- german
- text2text-generation
- generated_from_trainer
datasets:
- lmqg/qg_dequad
metrics:
- bleu4
- f1
- rouge
- exact_match
model-index:
- name: german-jeopardy-longt5-large-128
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: lmqg/qg_dequad
type: default
args: default
metrics:
- name: BLEU-4
type: bleu4
value: 6.99
- name: F1
type: f1
value: 28.39
- name: ROUGE-1
type: rouge1
value: 28.96
- name: ROUGE-2
type: rouge2
value: 11.91
- name: ROUGE-L
type: rougel
value: 27.92
- name: ROUGE-Lsum
type: rougelsum
value: 27.91
- name: Exact Match
type: exact_match
value: 0.95
---
<!-- 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. -->
# german-jeopardy-longt5-large-128
This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6149
- Brevity Penalty: 0.9386
- System Length: 19554
- Reference Length: 20793
- ROUGE-1: 28.96
- ROUGE-2: 11.91
- ROUGE-L: 27.92
- ROUGE-Lsum: 27.91
- Exact Match: 0.95
- BLEU: 6.99
- F1: 28.39
## Model description
See [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) for more information about the
model architecture.
The model was trained on a single NVIDIA RTX 3090 GPU with 24GB of VRAM.
## Intended uses & limitations
This model can be used for question generation on German text.
## Training and evaluation data
See [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 7
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adafactor
- lr_scheduler_type: constant
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Counts 1 | Counts 2 | Counts 3 | Counts 4 | Totals 1 | Totals 2 | Totals 3 | Totals 4 | Precisions 1 | Precisions 2 | Precisions 3 | Precisions 4 | Brevity Penalty | System Length | Reference Length | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-Lsum | Exact Match | BLEU | Mean Generated Length | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:----------------:|:-------:|:-------:|:-------:|:----------:|:-----------:|:-------:|:---------------------:|:------:|
| 7.5882 | 0.99 | 72 | 5.6823 | 3993 | 105 | 0 | 0 | 14790 | 12586 | 10382 | 8178 | 26.998 | 0.8343 | 0.0048 | 0.0031 | 0.6461 | 14790 | 21250 | 0.1101 | 0.0077 | 0.1078 | 0.1076 | 0.0 | 0.0872 | 9.7105 | 0.1155 |
| 5.2903 | 1.99 | 145 | 4.8721 | 3827 | 229 | 32 | 0 | 18894 | 16690 | 14486 | 12282 | 20.2551 | 1.3721 | 0.2209 | 0.0041 | 0.8828 | 18894 | 21250 | 0.0924 | 0.015 | 0.091 | 0.0909 | 0.0 | 0.351 | 16.7005 | 0.0964 |
| 4.6636 | 3.0 | 218 | 4.2806 | 3638 | 174 | 21 | 0 | 15268 | 13064 | 10860 | 8656 | 23.8276 | 1.3319 | 0.1934 | 0.0058 | 0.6758 | 15268 | 21250 | 0.0884 | 0.012 | 0.0876 | 0.0874 | 0.0 | 0.2933 | 8.9197 | 0.0925 |
| 4.2229 | 4.0 | 291 | 3.9210 | 4274 | 240 | 24 | 0 | 29308 | 27104 | 24900 | 22696 | 14.583 | 0.8855 | 0.0964 | 0.0022 | 1.0 | 29308 | 21250 | 0.0894 | 0.0109 | 0.0849 | 0.0849 | 0.0 | 0.2288 | 24.7015 | 0.1023 |
| 3.9434 | 4.99 | 363 | 3.6907 | 3652 | 218 | 35 | 1 | 16442 | 14238 | 12034 | 9830 | 22.2114 | 1.5311 | 0.2908 | 0.0102 | 0.7465 | 16442 | 21250 | 0.0856 | 0.0141 | 0.0843 | 0.0842 | 0.0 | 0.4204 | 12.3049 | 0.0898 |
| 3.6152 | 5.99 | 436 | 3.4603 | 4103 | 341 | 77 | 11 | 20581 | 18377 | 16173 | 13969 | 19.9359 | 1.8556 | 0.4761 | 0.0787 | 0.968 | 20581 | 21250 | 0.107 | 0.019 | 0.1023 | 0.1024 | 0.0 | 1.0505 | 14.3607 | 0.112 |
| 3.3814 | 7.0 | 509 | 3.2883 | 4342 | 675 | 218 | 43 | 17763 | 15559 | 13355 | 11151 | 24.4441 | 4.3383 | 1.6323 | 0.3856 | 0.8218 | 17763 | 21250 | 0.1264 | 0.0353 | 0.1234 | 0.1234 | 0.0005 | 2.3489 | 10.2418 | 0.1308 |
| 3.1711 | 8.0 | 582 | 3.0988 | 4820 | 856 | 246 | 44 | 19759 | 17555 | 15351 | 13147 | 24.3939 | 4.8761 | 1.6025 | 0.3347 | 0.9273 | 19759 | 21250 | 0.1503 | 0.0465 | 0.1455 | 0.1457 | 0.0005 | 2.6207 | 14.3249 | 0.1547 |
| 3.0147 | 8.99 | 654 | 2.9540 | 5167 | 1066 | 321 | 76 | 18725 | 16521 | 14317 | 12113 | 27.5941 | 6.4524 | 2.2421 | 0.6274 | 0.8739 | 18725 | 21250 | 0.1773 | 0.0588 | 0.1721 | 0.1721 | 0.0018 | 3.4764 | 14.3067 | 0.1816 |
| 2.7829 | 9.99 | 727 | 2.8288 | 5625 | 1267 | 420 | 124 | 17327 | 15123 | 12919 | 10715 | 32.4638 | 8.378 | 3.251 | 1.1573 | 0.7974 | 17327 | 21250 | 0.2127 | 0.0741 | 0.2067 | 0.2065 | 0.0045 | 4.5099 | 12.9741 | 0.2159 |
| 2.6093 | 10.99 | 800 | 2.7177 | 6005 | 1469 | 528 | 181 | 18625 | 16421 | 14217 | 12013 | 32.2416 | 8.9459 | 3.7139 | 1.5067 | 0.8685 | 18625 | 21250 | 0.229 | 0.0827 | 0.2215 | 0.2213 | 0.0064 | 5.5051 | 14.4791 | 0.231 |
| 2.453 | 12.0 | 873 | 2.5914 | 6396 | 1744 | 664 | 246 | 18307 | 16103 | 13899 | 11695 | 34.9375 | 10.8303 | 4.7773 | 2.1035 | 0.8515 | 18307 | 21250 | 0.2553 | 0.0998 | 0.2479 | 0.2478 | 0.0059 | 6.6865 | 13.7142 | 0.2565 |
| 2.3329 | 12.99 | 945 | 2.4993 | 6673 | 1888 | 741 | 291 | 18451 | 16247 | 14043 | 11839 | 36.1661 | 11.6206 | 5.2767 | 2.458 | 0.8592 | 18451 | 21250 | 0.2747 | 0.1114 | 0.2652 | 0.2652 | 0.0091 | 7.383 | 14.1751 | 0.2749 |
| 2.1663 | 13.99 | 1018 | 2.4196 | 6953 | 2052 | 834 | 337 | 18531 | 16327 | 14123 | 11919 | 37.5209 | 12.5681 | 5.9053 | 2.8274 | 0.8635 | 18531 | 21250 | 0.2886 | 0.1215 | 0.2773 | 0.277 | 0.0082 | 8.1343 | 14.6783 | 0.2889 |
| 2.0422 | 14.99 | 1091 | 2.3703 | 6968 | 2089 | 862 | 365 | 17984 | 15780 | 13576 | 11372 | 38.7456 | 13.2383 | 6.3494 | 3.2096 | 0.8339 | 17984 | 21250 | 0.2961 | 0.1268 | 0.2858 | 0.2857 | 0.0113 | 8.4322 | 13.6987 | 0.2951 |
| 1.9245 | 16.0 | 1164 | 2.3217 | 7500 | 2353 | 999 | 446 | 19017 | 16813 | 14609 | 12405 | 39.4384 | 13.9951 | 6.8383 | 3.5953 | 0.8892 | 19017 | 21250 | 0.3149 | 0.1407 | 0.3017 | 0.3017 | 0.0132 | 9.5973 | 14.77 | 0.314 |
| 1.8216 | 17.0 | 1237 | 2.2705 | 7444 | 2357 | 1044 | 488 | 18219 | 16015 | 13811 | 11607 | 40.8584 | 14.7175 | 7.5592 | 4.2044 | 0.8467 | 18219 | 21250 | 0.3201 | 0.1437 | 0.3081 | 0.3077 | 0.0132 | 9.9557 | 13.8031 | 0.3181 |
| 1.7503 | 17.99 | 1309 | 2.2386 | 7571 | 2487 | 1114 | 515 | 18275 | 16071 | 13867 | 11663 | 41.4282 | 15.4751 | 8.0335 | 4.4157 | 0.8498 | 18275 | 21250 | 0.3289 | 0.1512 | 0.3153 | 0.3151 | 0.0145 | 10.4354 | 13.9106 | 0.3265 |
| 1.6342 | 18.99 | 1382 | 2.2183 | 7697 | 2536 | 1155 | 537 | 18129 | 15925 | 13721 | 11517 | 42.4568 | 15.9246 | 8.4178 | 4.6627 | 0.8418 | 18129 | 21250 | 0.3342 | 0.1559 | 0.3224 | 0.3222 | 0.0177 | 10.7447 | 13.8494 | 0.3313 |
| 1.5474 | 19.79 | 1440 | 2.1956 | 7879 | 2632 | 1187 | 570 | 18815 | 16611 | 14407 | 12203 | 41.8762 | 15.8449 | 8.2391 | 4.671 | 0.8786 | 18815 | 21250 | 0.3398 | 0.1607 | 0.326 | 0.326 | 0.0177 | 11.1066 | 14.5136 | 0.3375 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
digiplay/futaall_v8_VAE_diffusers
|
digiplay
| 2023-11-02T15:26:45Z | 450 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-02T10:06:16Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Sample image generated by huggingface's API :
Model info:
https://civitai.com/models/127738?modelVersionId=139793
|
YieldInc/pythoncode
|
YieldInc
| 2023-11-02T15:17:54Z | 4 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-02T15:17:27Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
Norod78/yet-another-sdxl-tattoo-lora
|
Norod78
| 2023-11-02T15:12:51Z | 213 | 5 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"style",
"tattoos",
"tattoo",
"sdxl style lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2023-11-02T14:58:02Z |
---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- style
- tattoos
- tattoo
- sdxl style lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: tattoo
widget:
- text: "A cthulhu tattoo "
- text: "A Disney princess tattoo "
- text: "A tattoo of Alice in wonderland falling down the rabbit hole "
- text: "A tattoo of a psychedelic acid trip "
- text: "A rose tattoo "
- text: "A crazy clown tattoo "
- text: "Rainbow unicorn tattoo "
- text: "Rainbow unicorn tattoo "
- text: "A female Night elf WoW tattoo "
- text: "A male Night elf WoW tattoo "
---
# Yet another SDXL Tattoo LoRA
> A cthulhu tattoo
([CivitAI](https://civitai.com/models/186453))
<p>Use the word "tattoo" in your prompts.</p><p>I used very simple short prompts. I did not use "Negative" prompt. For some reason "Euler a" with 32 steps (80% SDXL base 20% Refiner) worked best for me.</p>
<p>Trained upon an 1024x1024 resized version of
[Drozdik/tattoo_v3](https://huggingface.co/datasets/Drozdik/tattoo_v3)
having the "on a white background" text suffixes trimmed</p>
## Image examples for the model:
> A Disney princess tattoo
> A tattoo of Alice in wonderland falling down the rabbit hole
> A tattoo of a psychedelic acid trip
> A rose tattoo
> A crazy clown tattoo
> Rainbow unicorn tattoo
> Rainbow unicorn tattoo
> A female Night elf WoW tattoo
> A male Night elf WoW tattoo
|
Alberto/twitter_xlm_robertta_sentiment_financial_news
|
Alberto
| 2023-11-02T15:06:23Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75",
"base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment",
"base_model:finetune:cardiffnlp/twitter-xlm-roberta-base-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-02T14:22:16Z |
---
base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twitter_xlm_robertta_sentiment_financial_news
results: []
datasets:
- Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
---
<!-- 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. -->
# twitter_xlm_robertta_sentiment_financial_news
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on [this]()https://huggingface.co/datasets/Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75 financial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4492
- F1: 0.8812
## 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_steps: 300
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.518 | 1.0 | 556 | 0.4881 | 0.8184 |
| 0.3534 | 2.0 | 1112 | 0.5041 | 0.8797 |
| 0.1781 | 3.0 | 1668 | 0.4492 | 0.8812 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.1
|
ArturGadek/ppo-Huggy
|
ArturGadek
| 2023-11-02T15:04:12Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-02T15:03:59Z |
---
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: ArturGadek/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bukun/distilbert-base-uncased-finetuned-sentence-intent
|
Bukun
| 2023-11-02T15:02:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-02T14:40:37Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sentence-intent
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-sentence-intent
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: 0.2630
- Accuracy: 0.0003
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2222 | 1.0 | 938 | 1.6962 | 0.0 |
| 1.1706 | 2.0 | 1876 | 0.4995 | 0.0 |
| 0.3065 | 3.0 | 2814 | 0.2630 | 0.0003 |
| 0.124 | 4.0 | 3752 | 0.2107 | 0.0003 |
| 0.0712 | 5.0 | 4690 | 0.2002 | 0.0003 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
xrcb/sd-class-butterflies-32
|
xrcb
| 2023-11-02T15:02:24Z | 3 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-23T13:15:58Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('xrcb/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
hatanp/gpt-fi-small
|
hatanp
| 2023-11-02T14:58:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"finnish",
"fi",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T11:31:33Z |
---
language:
- fi
tags:
- finnish
- gpt2
widget:
- text: "Jotta voidaan luoda tekstiä"
library:
- transformers
license: apache-2.0
---
## DEPRECATED!
This model is old and no longer relevant with the releases of all around better Finnish models such as GPT-3 models from [TurkuNLP](https://huggingface.co/TurkuNLP)
You may of course still use this for experiments and benchmarking, but I doubt this will work any better.
## Old description:
A small version of a larger model [gpt-fi](https://huggingface.co/hatanp/gpt-fi). This model has approximately 125M parameters compared to the 1.2B parameters of the larger model. For scripts and more complete model information refer to the large models' page.
|
hatanp/gpt-fi-distill
|
hatanp
| 2023-11-02T14:58:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"finnish",
"fi",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T11:18:17Z |
---
language:
- fi
tags:
- finnish
- gpt2
widget:
- text: "Jotta voidaan luoda tekstiä"
---
## DEPRECATED!
This model is old and no longer relevant with the releases of all around better Finnish models such as GPT-3 models from [TurkuNLP](https://huggingface.co/TurkuNLP)
You may of course still use this for experiments and benchmarking, but I doubt this will work any better.
## Old description:
Knowledge distilled version of a larger model [gpt-fi](https://huggingface.co/hatanp/gpt-fi). This model has approximately 300M parameters compared to the 1.2B parameters of the larger model. For scripts and more complete model information refer to the large models' page.
|
yobett/dreamshaper_8-t_QO1DxVwt3KtECwuf
|
yobett
| 2023-11-02T14:48:29Z | 2 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-02T14:28:53Z |
---
license: creativeml-openrail-m
base_model: dreamshaper_8
instance_prompt: apo1102 man
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - yobett/dreamshaper_8-t_QO1DxVwt3KtECwuf
This is a dreambooth model derived from dreamshaper_8. The weights were trained on apo1102 man using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
IdoCK/DRL_HuggingFace
|
IdoCK
| 2023-11-02T14:46:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T14:46:18Z |
---
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: 271.74 +/- 20.87
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
...
```
|
KBlueLeaf/kohaku-xl-beta5
|
KBlueLeaf
| 2023-11-02T14:44:38Z | 195 | 4 |
diffusers
|
[
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-11-02T14:41:10Z |
---
license: creativeml-openrail-m
---
|
vishwa27/wav2vec2-large-xls-r-300m-gn
|
vishwa27
| 2023-11-02T14:42:19Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-large-xlsr-53-spanish",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53-spanish",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-02T12:22:51Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53-spanish
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-gn
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: gn
split: test
args: gn
metrics:
- name: Wer
type: wer
value: 0.3430613460393091
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-gn
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3713
- Wer: 0.3431
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7177 | 3.62 | 400 | 0.3649 | 0.5816 |
| 0.2738 | 7.24 | 800 | 0.4029 | 0.5024 |
| 0.1768 | 10.86 | 1200 | 0.3779 | 0.4285 |
| 0.1128 | 14.48 | 1600 | 0.3929 | 0.4205 |
| 0.0842 | 18.1 | 2000 | 0.3683 | 0.3916 |
| 0.0616 | 21.72 | 2400 | 0.3943 | 0.3675 |
| 0.0461 | 25.34 | 2800 | 0.4127 | 0.3571 |
| 0.0368 | 28.96 | 3200 | 0.3713 | 0.3431 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
sh-holmes/ppo-LunarLander-v2
|
sh-holmes
| 2023-11-02T14:42:10Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T14:41:51Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.58 +/- 19.30
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
...
```
|
zhixuluo/my_awesome_asr_mind_model
|
zhixuluo
| 2023-11-02T14:35:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-26T12:41:46Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: my_awesome_asr_mind_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_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9977
- Wer: 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.1676 | 100.0 | 500 | 3.1581 | 1.0 |
| 2.8849 | 200.0 | 1000 | 2.9977 | 1.0 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
fawasak997/tinyllama-colorist-lora
|
fawasak997
| 2023-11-02T14:15:06Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2023-11-02T13:55:38Z |
---
license: apache-2.0
base_model: PY007/TinyLlama-1.1B-Chat-v0.3
tags:
- generated_from_trainer
model-index:
- name: tinyllama-colorist-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. -->
# tinyllama-colorist-lora
This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
jord-hanus/my_awesome_qa_model
|
jord-hanus
| 2023-11-02T14:13:28Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-11-02T14:07:10Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
nexus888/mistral_b_finance_finetuned_test
|
nexus888
| 2023-11-02T14:08:35Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-11-02T11:48:03Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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]
- **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.
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## Training Details
### Training Data
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## Evaluation
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### Testing Data, Factors & Metrics
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
sakelariev/bg_news_sm
|
sakelariev
| 2023-11-02T14:05:23Z | 52 | 0 |
spacy
|
[
"spacy",
"ner",
"named entity recognition",
"token-classification",
"bg",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] |
token-classification
| 2023-11-01T10:14:34Z |
---
license: cc-by-nc-sa-3.0
language:
- bg
metrics:
- accuracy
library_name: spacy
pipeline_tag: token-classification
model-index:
- name: bg_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8646547782
- name: NER Recall
type: recall
value: 0.8254037538
- name: NER F Score
type: f_score
value: 0.8445734703
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9381672598
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9749149525
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9485224023
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9218374826
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8823201753
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8238961039
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9163310962
tags:
- ner
- spacy
- named entity recognition
---
| Feature | Description |
| --- | --- |
| **Name** | `bg_news_sm` |
| **Version** | `3.5.4` |
| **spaCy** | `>=3.5.4,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB) (Kiril Simov and Petya Osenova) |
| **License** | CC-BY-NC-SA-3.0 |
| **Author** | [Ivaylo Sakelariev](https://github.com/sakelariev) |
Bulgarian small sized pipeline for BGspaCy. Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner
### Label Scheme
<details>
<summary>View label scheme (999 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `A`, `A-pd`, `A-pi`, `Afsd`, `Afsi`, `Ams-e`, `Amsf`, `Amsh`, `Amsi`, `Ansd`, `Ansi`, `Cc`, `Cp`, `Cr`, `Cs`, `D`, `Dd`, `Dl`, `Dm`, `Dq`, `Dt`, `H-pd`, `H-pi`, `Hfsd`, `Hfsi`, `Hmsf`, `Hmsh`, `Hmsi`, `Hnsi`, `I`, `M`, `Mc--d`, `Mc--i`, `Mc-pd`, `Mc-pi`, `Mc-si`, `Mcf-d`, `Mcf-i`, `Mcfpd`, `Mcfpi`, `Mcfsd`, `Mcfsi`, `Mcm-i`, `Mcmpd`, `Mcmpi`, `Mcmsf`, `Mcmsi`, `Mcn-d`, `Mcn-i`, `Mcnpd`, `Mcnpi`, `Mcnsd`, `Mcnsi`, `Md--d`, `Md--i`, `Md-pd`, `Md-pi`, `Mo-pd`, `Mo-pi`, `Mofsd`, `Mofsi`, `Momsf`, `Momsh`, `Momsi`, `Monsd`, `Monsi`, `My--i`, `My-pi`, `Nc`, `Nc-ld`, `Nc-li`, `Ncfpd`, `Ncfpi`, `Ncfs-v`, `Ncfsd`, `Ncfsi`, `Ncmpd`, `Ncmpi`, `Ncms-v`, `Ncmsd`, `Ncmsf`, `Ncmsh`, `Ncmsi`, `Ncmt`, `Ncnpd`, `Ncnpi`, `Ncnsd`, `Ncnsi`, `Np`, `Np-li`, `Np-pi`, `Npfpd`, `Npfpi`, `Npfs-v`, `Npfsd`, `Npfsi`, `Npmpd`, `Npmpi`, `Npms-a`, `Npms-v`, `Npmsd`, `Npmsf`, `Npmsh`, `Npmsi`, `Npnpd`, `Npnpi`, `Npnsd`, `Npnsi`, `Pca--p`, `Pca--s-f`, `Pca--s-m`, `Pca--s-n`, `Pce-as-m`, `Pce-op`, `Pce-os-f`, `Pce-os-m`, `Pce-os-n`, `Pcl`, `Pcq--s-nd`, `Pda--p`, `Pda--s-f`, `Pda--s-m`, `Pda--s-n`, `Pde-op`, `Pde-os-f`, `Pde-os-m`, `Pde-os-n`, `Pdl`, `Pdm`, `Pdq`, `Pds`, `Pdt`, `Pfa--p`, `Pfa--s-f`, `Pfa--s-m`, `Pfa--s-n`, `Pfe-as-m`, `Pfe-op`, `Pfe-op--d`, `Pfe-op--i`, `Pfe-os-f`, `Pfe-os-fd`, `Pfe-os-fi`, `Pfe-os-m`, `Pfe-os-mf`, `Pfe-os-mh`, `Pfe-os-mi`, `Pfe-os-n`, `Pfe-os-ni`, `Pfl`, `Pfm`, `Pfp--s-n`, `Pfq-----i`, `Pft`, `Pfy-----i`, `Pia--p`, `Pia--s-f`, `Pia--s-m`, `Pia--s-n`, `Pic`, `Pie-as-m`, `Pie-op`, `Pie-os-f`, `Pie-os-m`, `Pie-os-n`, `Pil`, `Pim`, `Pip--s-f`, `Piq`, `Pit`, `Pna--p`, `Pna--s-f`, `Pna--s-m`, `Pna--s-n`, `Pne-as-m`, `Pne-ds-m`, `Pne-os-f`, `Pne-os-m`, `Pne-os-nd`, `Pne-os-ni`, `Pnl`, `Pnm`, `Pnp--s-f`, `Pnt`, `Ppe-op1`, `Ppe-op2`, `Ppe-op3`, `Ppe-os1`, `Ppe-os2`, `Ppe-os3f`, `Ppe-os3m`, `Ppe-os3n`, `Ppelap1`, `Ppelap2`, `Ppelap3`, `Ppelas1`, `Ppelas2`, `Ppelas3f`, `Ppelas3m`, `Ppelas3n`, `Ppeldp3`, `Ppelds1`, `Ppelds3m`, `Ppetap1`, `Ppetap2`, `Ppetap3`, `Ppetas1`, `Ppetas2`, `Ppetas3f`, `Ppetas3m`, `Ppetas3n`, `Ppetdp1`, `Ppetdp2`, `Ppetdp3`, `Ppetds1`, `Ppetds2`, `Ppetds3f`, `Ppetds3m`, `Ppetds3n`, `Ppetsp1`, `Ppetsp2`, `Ppetsp3`, `Ppetss1`, `Ppetss2`, `Ppetss3f`, `Ppetss3m`, `Pph-os2`, `Pphlas2`, `Pphtas2`, `Pphtds2`, `Pphtss2`, `Ppxla`, `Ppxta`, `Ppxtd`, `Ppxts`, `Pra--p`, `Pra--s`, `Pra--s-f`, `Pra--s-m`, `Pra--s-n`, `Prc`, `Pre--s`, `Pre-as-m`, `Pre-op`, `Pre-os-f`, `Pre-os-m`, `Pre-os-n`, `Prl`, `Prm`, `Prp--p`, `Prp--s-f`, `Prp--s-m`, `Prp--s-n`, `Prq`, `Prt`, `Pshl-p2-d`, `Pshl-p2-i`, `Pshl-s2fd`, `Pshl-s2fi`, `Pshl-s2mf`, `Pshl-s2mh`, `Pshl-s2mi`, `Pshl-s2nd`, `Pshl-s2ni`, `Psht--2`, `Psol-p1-d`, `Psol-p2-d`, `Psol-p3-df`, `Psol-p3-dm`, `Psol-p3-dn`, `Psol-p3-if`, `Psol-p3-im`, `Psol-s1fd`, `Psol-s1fi`, `Psol-s1mf`, `Psol-s1mh`, `Psol-s1mi`, `Psol-s1nd`, `Psol-s1ni`, `Psol-s2ni`, `Psol-s3fdf`, `Psol-s3fdm`, `Psol-s3fdn`, `Psol-s3fif`, `Psol-s3fim`, `Psol-s3mff`, `Psol-s3mfm`, `Psol-s3mfn`, `Psol-s3mhf`, `Psol-s3mhm`, `Psol-s3mhn`, `Psol-s3mim`, `Psol-s3min`, `Psol-s3ndf`, `Psol-s3ndm`, `Psol-s3ndn`, `Psol-s3nim`, `Psol-s3nin`, `Psot--1`, `Psot--2`, `Psot--3--f`, `Psot--3--m`, `Psot--3--n`, `Psxlop--d`, `Psxlop--i`, `Psxlos-fd`, `Psxlos-fi`, `Psxlos-mh`, `Psxlos-mi`, `Psxlos-nd`, `Psxlos-ni`, `Psxto`, `Pszl-p1-d`, `Pszl-p1-i`, `Pszl-p3-d`, `Pszl-p3-i`, `Pszl-s1fd`, `Pszl-s1fi`, `Pszl-s1mf`, `Pszl-s1mh`, `Pszl-s1mi`, `Pszl-s1nd`, `Pszl-s1ni`, `Pszl-s2fd`, `Pszl-s2mh`, `Pszl-s2mi`, `Pszl-s2nd`, `Pszl-s3fd`, `Pszl-s3fi`, `Pszl-s3mf`, `Pszl-s3mh`, `Pszl-s3mi`, `Pszl-s3nd`, `Pszl-s3ni`, `Pszt--1`, `Pszt--2`, `Pszt--3`, `R`, `T`, `Ta`, `Te`, `Ti`, `Tm`, `Tn`, `Tt`, `Tv`, `Tx`, `Unknown`, `V`, `Viitf-r3p`, `Vniicam-sni`, `Vniicao-sni`, `Vniif-m3s`, `Vniif-o3s`, `Vniif-r3s`, `Vnitcam-sni`, `Vnitcao-sni`, `Vnitf-m3s`, `Vnitf-r3s`, `Vnpicao-sni`, `Vnpif-o3s`, `Vnpif-r3s`, `Vnptcao-sni`, `Vnptf-m3s`, `Vpiicam-p-i`, `Vpiicam-sfi`, `Vpiicam-smi`, `Vpiicam-sni`, `Vpiicao-p-d`, `Vpiicao-p-i`, `Vpiicao-sfi`, `Vpiicao-smi`, `Vpiicao-sni`, `Vpiicar-p-d`, `Vpiicar-p-i`, `Vpiicar-sfd`, `Vpiicar-sfi`, `Vpiicar-smf`, `Vpiicar-smh`, `Vpiicar-smi`, `Vpiicar-snd`, `Vpiicar-sni`, `Vpiicv--sni`, `Vpiif-m1p`, `Vpiif-m1s`, `Vpiif-m2s`, `Vpiif-m3p`, `Vpiif-m3s`, `Vpiif-o1p`, `Vpiif-o1s`, `Vpiif-o3p`, `Vpiif-o3s`, `Vpiif-r1p`, `Vpiif-r1s`, `Vpiif-r2p`, `Vpiif-r2s`, `Vpiif-r3p`, `Vpiif-r3s`, `Vpiig`, `Vpiiz--2p`, `Vpiiz--2s`, `Vpitcam-p-i`, `Vpitcam-sfi`, `Vpitcam-smi`, `Vpitcam-sni`, `Vpitcao-p-i`, `Vpitcao-sfi`, `Vpitcao-smi`, `Vpitcao-sni`, `Vpitcar-p-d`, `Vpitcar-p-i`, `Vpitcar-sfd`, `Vpitcar-sfi`, `Vpitcar-smf`, `Vpitcar-smh`, `Vpitcar-smi`, `Vpitcar-snd`, `Vpitcar-sni`, `Vpitcv--p-d`, `Vpitcv--p-i`, `Vpitcv--sfd`, `Vpitcv--sfi`, `Vpitcv--smf`, `Vpitcv--smh`, `Vpitcv--smi`, `Vpitcv--snd`, `Vpitcv--sni`, `Vpitf-m1p`, `Vpitf-m1s`, `Vpitf-m2p`, `Vpitf-m2s`, `Vpitf-m3p`, `Vpitf-m3s`, `Vpitf-o1p`, `Vpitf-o1s`, `Vpitf-o2p`, `Vpitf-o2s`, `Vpitf-o3p`, `Vpitf-o3s`, `Vpitf-r1p`, `Vpitf-r1s`, `Vpitf-r2p`, `Vpitf-r2s`, `Vpitf-r3p`, `Vpitf-r3s`, `Vpitg`, `Vpitz--2p`, `Vpitz--2s`, `Vppicao-p-d`, `Vppicao-p-i`, `Vppicao-sfd`, `Vppicao-sfi`, `Vppicao-smf`, `Vppicao-smh`, `Vppicao-smi`, `Vppicao-snd`, `Vppicao-sni`, `Vppif-m3p`, `Vppif-m3s`, `Vppif-o1p`, `Vppif-o1s`, `Vppif-o2s`, `Vppif-o3p`, `Vppif-o3s`, `Vppif-r1p`, `Vppif-r1s`, `Vppif-r2p`, `Vppif-r2s`, `Vppif-r3p`, `Vppif-r3s`, `Vppiz--2p`, `Vppiz--2s`, `Vpptcam-smi`, `Vpptcao-p-d`, `Vpptcao-p-i`, `Vpptcao-sfd`, `Vpptcao-sfi`, `Vpptcao-smh`, `Vpptcao-smi`, `Vpptcao-snd`, `Vpptcao-sni`, `Vpptcv--p-d`, `Vpptcv--p-i`, `Vpptcv--sfd`, `Vpptcv--sfi`, `Vpptcv--smf`, `Vpptcv--smh`, `Vpptcv--smi`, `Vpptcv--snd`, `Vpptcv--sni`, `Vpptf-m3p`, `Vpptf-m3s`, `Vpptf-o1p`, `Vpptf-o1s`, `Vpptf-o2p`, `Vpptf-o2s`, `Vpptf-o3p`, `Vpptf-o3s`, `Vpptf-r1p`, `Vpptf-r1s`, `Vpptf-r2p`, `Vpptf-r2s`, `Vpptf-r3p`, `Vpptf-r3s`, `Vpptz--2p`, `Vpptz--2s`, `Vxitcat-p-i`, `Vxitcat-sfi`, `Vxitcat-smi`, `Vxitcat-sni`, `Vxitf-r1p`, `Vxitf-r1s`, `Vxitf-r2p`, `Vxitf-r2s`, `Vxitf-r3p`, `Vxitf-r3s`, `Vxitf-t1p`, `Vxitf-t1s`, `Vxitf-t2p`, `Vxitf-t2s`, `Vxitf-t3p`, `Vxitf-t3s`, `Vxitu-o1p`, `Vxitu-o1s`, `Vxitu-o2p`, `Vxitu-o2s`, `Vxitu-o3p`, `Vxitu-o3s`, `Vyptf-o3s`, `Vyptf-r1p`, `Vyptf-r1s`, `Vyptf-r2p`, `Vyptf-r2s`, `Vyptf-r3p`, `Vyptf-r3s`, `punct` |
| **`morphologizer`** | `POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=AUX`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART\|Polarity=Neg`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=INTJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=CCONJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Ind\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Number=Ptan\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|POS=PRON\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Dem`, `POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Ptan\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|Degree=Pos\|NumType=Card\|Number=Plur\|POS=ADV`, `POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `Definite=Def\|Degree=Pos\|NumType=Card\|Number=Plur\|POS=ADV`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|POS=PRON\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Rel`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Definite=Def\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Animacy=Anim\|Definite=Ind\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `POS=ADV\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=SCONJ`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Def\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Count\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `POS=ADV\|PronType=Int`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `NumType=Card\|POS=ADV\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Def\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `NumType=Card\|POS=ADV\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Number=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Ind`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `NumType=Card\|POS=ADV\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Cmp\|NumType=Card\|Number=Plur\|POS=ADV`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|POS=PRON\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Def\|Degree=Cmp\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Degree=Pos\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Sup\|NumType=Card\|Number=Plur\|POS=ADV`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Mood=Ind\|Number=Plur\|POS=AUX\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Degree=Cmp\|POS=ADV\|PronType=Dem`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Aspect=Imp\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `POS=PROPN`, `Aspect=Imp\|Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Voc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Ptan\|POS=PROPN`, `Definite=Def\|Degree=Sup\|NumType=Card\|Number=Plur\|POS=ADV`, `Definite=Ind\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADJ`, `Aspect=Perf\|Definite=Def\|Degree=Pos\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `NumType=Ord\|POS=NUM`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=X`, `Foreign=Yes\|POS=X`, `Aspect=Imp\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Def\|Degree=Cmp\|NumType=Card\|Number=Plur\|POS=ADV`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part\|Voice=Act` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 93.82 |
| `POS_ACC` | 97.49 |
| `MORPH_ACC` | 94.85 |
| `LEMMA_ACC` | 92.18 |
| `DEP_UAS` | 88.23 |
| `DEP_LAS` | 82.39 |
| `SENTS_P` | 91.51 |
| `SENTS_R` | 91.76 |
| `SENTS_F` | 91.63 |
| `ENTS_F` | 86.47 |
| `ENTS_P` | 82.54 |
| `ENTS_R` | 84.46 |
|
EasyTerms/etsummerizer_v2
|
EasyTerms
| 2023-11-02T14:04:26Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"en",
"dataset:EasyTerms/Manuel_dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-08-23T10:13:48Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: etsummerizer_v2
results: []
datasets:
- EasyTerms/Manuel_dataset
language:
- en
library_name: transformers
pipeline_tag: summarization
---
<!-- 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. -->
# etsummerizer_v2
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on [EasyTerms/Manuel_dataset](https://huggingface.co/datasets/EasyTerms/Manuel_dataset).
It achieves the following results on the evaluation set:
- Loss: 0.3484
- Rouge1: 0.5448
- Rouge2: 0.3092
- Rougel: 0.4363
- Rougelsum: 0.4370
## Model description
This model was finetuned on legal text extracted from different terms and conditions documents. Its objective is to efficiently summerize such text and present the generation
in a simplified version lacking in legal jargon.
## Intended uses & limitations
As it is the second version of this model it effectively summerize legal text however, further training will be required to improve the simplification task.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.5 | 1.0 | 30 | 0.5565 | 0.5111 | 0.2863 | 0.4092 | 0.4093 |
| 0.3056 | 2.0 | 60 | 0.3612 | 0.5267 | 0.3021 | 0.4277 | 0.4286 |
| 0.1716 | 3.0 | 90 | 0.3484 | 0.5448 | 0.3092 | 0.4363 | 0.4370 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.13.3
|
IvoSchols/bert-finetuned-ner
|
IvoSchols
| 2023-11-02T13:59:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-11-02T09:20:09Z |
---
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4003
- Precision: 0.5385
- Recall: 0.2063
- F1: 0.2983
- Accuracy: 0.9405
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 385 | 0.3522 | 0.5717 | 0.1175 | 0.1949 | 0.9353 |
| 0.1984 | 2.0 | 770 | 0.3887 | 0.5670 | 0.1904 | 0.2850 | 0.9395 |
| 0.0884 | 3.0 | 1155 | 0.4003 | 0.5385 | 0.2063 | 0.2983 | 0.9405 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
digiplay/AingDiffusion7.5
|
digiplay
| 2023-11-02T13:54:16Z | 885 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-02T13:28:15Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/34553?modelVersionId=116371
|
colemane/poca-SoccerTwos
|
colemane
| 2023-11-02T13:46:19Z | 61 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-11-02T13:46:11Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: colemane/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
elemosynov/FrozenLake
|
elemosynov
| 2023-11-02T13:38:37Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T12:41:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake
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 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="elemosynov/FrozenLake", 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"])
```
|
KevinReuben5/ppo-LunarLander-v2
|
KevinReuben5
| 2023-11-02T13:33:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T13:17:43Z |
---
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: 291.75 +/- 18.43
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
...
```
|
zyy519/dqn-SpaceInvadersNoFrameskip-v4
|
zyy519
| 2023-11-02T13:26:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-02T13:25:30Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 616.00 +/- 245.73
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zyy519 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga zyy519 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga zyy519
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
digiplay/AingDiffusion9.2
|
digiplay
| 2023-11-02T13:25:37Z | 393 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-22T13:02:20Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/34553?modelVersionId=156882
Sample image I made generated by huggingface's API :
bright color ,sharp focus ,girl,looking at viewer ,lake,ultra-detailed ,clear lines,light lines,4k 8k,movie ,upper body ,photo-realistic ,ultra photorealistic ,photo background,masterpiece ,3d,movie Lighting ,skin details ,
|
sanjana-m/wav2vec2-large-xls-r-300m-gn-pt
|
sanjana-m
| 2023-11-02T13:20:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-02T12:06:47Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-gn-pt
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: gn
split: test
args: gn
metrics:
- name: Wer
type: wer
value: 0.5964860035735556
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-gn-pt
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6576
- Wer: 0.5965
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 35
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7255 | 4.85 | 400 | 3.0288 | 1.0 |
| 1.1814 | 9.7 | 800 | 0.5687 | 0.7317 |
| 0.3099 | 14.55 | 1200 | 0.6297 | 0.6828 |
| 0.1719 | 19.39 | 1600 | 0.7157 | 0.6992 |
| 0.1185 | 24.24 | 2000 | 0.6896 | 0.6537 |
| 0.0871 | 29.09 | 2400 | 0.7071 | 0.6215 |
| 0.0647 | 33.94 | 2800 | 0.6576 | 0.5965 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Raghulkannan/mistral-finetuned-samsum
|
Raghulkannan
| 2023-11-02T13:19:17Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-11-01T10:02:11Z |
---
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: mistral-finetuned-samsum
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. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-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
- training_steps: 250
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
aninditr/wav2vec2-large-xls-r-300m-gn-pt-colab
|
aninditr
| 2023-11-02T13:16:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-02T02:57:35Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: wav2vec2-large-xls-r-300m-gn-pt-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-gn-pt-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9577
- eval_wer: 0.7061
- eval_runtime: 59.418
- eval_samples_per_second: 13.649
- eval_steps_per_second: 1.717
- epoch: 20.5
- step: 20800
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Kooten/Utopia-13B-4bpw-h8-exl2
|
Kooten
| 2023-11-02T13:16:24Z | 9 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-02T12:33:18Z |
---
license: cc-by-nc-4.0
---
## Description
Exllama 2 quant of [Undi95/Utopia-13B](https://huggingface.co/Undi95/Utopia-13B)
4 BPW, Head bit set to 8
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## VRAM
My VRAM usage with 13B models are:
| Bits per weight | Context | VRAM |
|--|--|--|
| 8bpw | 8k | 22gb |
| 8bpw | 4k | 19gb |
| 6bpw | 8k | 19gb |
| 6bpw | 4k | 16gb |
| 4bpw | 8k | 16gb |
| 4bpw | 4k | 13gb |
| 3bpw | 8k | 15gb |
| 3bpw | 4k | 12gb |
I have rounded up, these arent exact numbers, this is also on a windows machine, they should be slightly lower on linux.
|
gradjitta/mistral-7b-ultrachat100k-merged
|
gradjitta
| 2023-11-02T13:14:24Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"dataset:kaitchup/ultrachat-100k-flattened",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-02T12:34:26Z |
---
license: mit
datasets:
- kaitchup/ultrachat-100k-flattened
---
---
library_name: peft
---
## Training procedure
Finetuned on ultrachat-100k-flattened dataset for 1 epoch, took around 40 hrs on A100 80BG
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
### Prompt
Use the following for prompting
prompt = "### Human: "+instruction+"### Assistant: "
#### merged following the gist https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930
#### Guidance from https://kaitchup.substack.com/
#### Work supported by https://datacrunch.io/
|
MiroMirror/bert-base-japanese-v3-wrime-sentiment
|
MiroMirror
| 2023-11-02T13:13:34Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"bert",
"region:us"
] | null | 2023-10-24T17:04:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
digiplay/fishmix_other_v1
|
digiplay
| 2023-11-02T13:04:59Z | 414 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T19:04:48Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
23-3-9-实验-咸鱼mix风格化 ——fish mix the other Style
https://civitai.com/models/17565/23-3-9-mix-fish-mix-the-other-style
Original Author's DEMO image :
Sample image I made : (using huggingface API)
image prompt + ***realistic*** keywords
image prompt ***with no realistic*** keywords
photorealism (8k UHD RAW,photorealistic,realistic:1.6) ,golden medium hair beautiful girl
|
juliajoanna/testowy
|
juliajoanna
| 2023-11-02T13:02:25Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:juliajoanna/sdxl-flintstones_finetuning_1",
"base_model:finetune:juliajoanna/sdxl-flintstones_finetuning_1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-11-01T15:54:19Z |
---
license: creativeml-openrail-m
base_model: juliajoanna/sdxl-flintstones_finetuning_1
dataset: None
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - juliajoanna/testowy
This pipeline was finetuned from **juliajoanna/sdxl-flintstones_finetuning_1** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Fred is driving a car:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
DevJaiz/flan-model-test
|
DevJaiz
| 2023-11-02T12:57:11Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:adapter:google/flan-t5-large",
"region:us"
] | null | 2023-11-02T12:57:09Z |
---
library_name: peft
base_model: google/flan-t5-large
---
# 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Quinta6728/my_awesome_wnut_model
|
Quinta6728
| 2023-11-02T12:54:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-02T12:53:51Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.6145833333333334
- name: Recall
type: recall
value: 0.32808155699721964
- name: F1
type: f1
value: 0.4277945619335347
- name: Accuracy
type: accuracy
value: 0.9424992518490017
---
<!-- 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_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2697
- Precision: 0.6146
- Recall: 0.3281
- F1: 0.4278
- Accuracy: 0.9425
## 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 | 213 | 0.2833 | 0.5687 | 0.2530 | 0.3502 | 0.9391 |
| No log | 2.0 | 426 | 0.2697 | 0.6146 | 0.3281 | 0.4278 | 0.9425 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Mabel465/bert-finetuned-ner.default_parameters
|
Mabel465
| 2023-11-02T12:45:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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
| 2023-11-02T12:32:28Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner.default_parameters
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.default_parameters
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2075
- Precision: 0.5645
- Recall: 0.4450
- F1: 0.4977
- Accuracy: 0.9228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 425 | 0.1828 | 0.5952 | 0.3553 | 0.4449 | 0.9136 |
| 0.1165 | 2.0 | 850 | 0.1917 | 0.5760 | 0.4127 | 0.4808 | 0.9181 |
| 0.0438 | 3.0 | 1275 | 0.2075 | 0.5645 | 0.4450 | 0.4977 | 0.9228 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
digiplay/AingDiffusion8.5
|
digiplay
| 2023-11-02T12:43:55Z | 331 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-16T18:36:26Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/34553?modelVersionId=135792
Original Author's DEMO images :
|
Felladrin/mlc-chat-Mistral-7B-v0.1-q4f32_1
|
Felladrin
| 2023-11-02T12:39:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-02T11:19:45Z |
---
license: apache-2.0
---
# Mistral 7B v0.1 for Web-LLM q4f32_1
This is a compiled version of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) for [MLC Web-LLM](https://webllm.mlc.ai/), using `q4f32_1` quantization.
## Usage
```javascript
import * as webLLM from "@mlc-ai/web-llm";
const modelId = "Mistral-7B-v0.1-q4f32_1";
const appConfig = {
model_list: [
{
model_url:
"https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-v0.1-q4f32_1/resolve/main/params/",
local_id: modelId,
},
],
model_lib_map: {
[modelId]:
"https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-v0.1-q4f32_1/resolve/main/Mistral-7B-v0.1-q4f32_1-webgpu.wasm",
},
};
const chatConfig = {
temperature: 0,
repetition_penalty: 1.2,
top_p: 1
};
async function main() {
const chat = new webLLM.ChatModule();
await chat.reload(modelId, chatConfig, appConfig);
let lastResponse = "";
const generateProgressCallback = (_, message = "") => {
if (message.length === 0) return chat.interruptGenerate();
lastResponse = message;
console.log(`Partial response: ${lastResponse}`);
};
const fistPrompt = "Could answer some questions?";
await chat.generate(fistPrompt, generateProgressCallback);
console.log(`Complete response: ${lastResponse}`);
const secondPrompt = "What's Mistral?";
await chat.generate(secondPrompt, generateProgressCallback);
console.log(`Complete response: ${lastResponse}`);
console.info(await chat.runtimeStatsText());
}
main();
```
|
ChengKang520/chatglm2-6b-int4-Psy
|
ChengKang520
| 2023-11-02T12:38:16Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"chatglm",
"glm",
"thudm",
"custom_code",
"zh",
"en",
"arxiv:2103.10360",
"arxiv:2210.02414",
"arxiv:1911.02150",
"endpoints_compatible",
"region:us"
] | null | 2023-11-02T11:56:30Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM2-6B
<p align="center">
💻 <a href="https://github.com/THUDM/ChatGLM2-6B" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2103.10360" target="_blank">[GLM@ACL 22]</a> <a href="https://github.com/THUDM/GLM" target="_blank">[GitHub]</a> • 📃 <a href="https://arxiv.org/abs/2210.02414" target="_blank">[GLM-130B@ICLR 23]</a> <a href="https://github.com/THUDM/GLM-130B" target="_blank">[GitHub]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://join.slack.com/t/chatglm/shared_invite/zt-1y7pqoloy-9b1g6T6JjA8J0KxvUjbwJw" target="_blank">Slack</a> and <a href="https://github.com/THUDM/ChatGLM-6B/blob/main/resources/WECHAT.md" target="_blank">WeChat</a>
</p>
## 介绍
ChatGLM**2**-6B 是开源中英双语对话模型 [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) 的第二代版本，在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM**2**-6B 引入了如下新特性：
1. **更强大的性能**：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 [GLM](https://github.com/THUDM/GLM) 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，[评测结果](#评测结果)显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。
2. **更长的上下文**：基于 [FlashAttention](https://github.com/HazyResearch/flash-attention) 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。
3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。
ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features:
1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.
## 软件依赖
```shell
pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate
```
## 代码调用
可以通过如下代码调用 ChatGLM-6B 模型来生成对话：
```ipython
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:
1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。
如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
关于更多的使用说明，包括如何运行命令行和网页版本的 DEMO，以及使用模型量化以节省显存，请参考我们的 [Github Repo](https://github.com/THUDM/ChatGLM2-6B)。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B).
## Change Log
* v1.0
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM2-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
## 引用
如果你觉得我们的工作有帮助的话，请考虑引用下列论文，ChatGLM2-6B 的论文会在近期公布，尽情期待～
```
@article{zeng2022glm,
title={Glm-130b: An open bilingual pre-trained model},
author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
journal={arXiv preprint arXiv:2210.02414},
year={2022}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
|
digiplay/AingDiffusion8.17
|
digiplay
| 2023-11-02T12:36:36Z | 388 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-16T18:37:09Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
https://civitai.com/models/34553?modelVersionId=141083
Original Author's DEMO images :
|
elarrahondo/gpt2-finetuning
|
elarrahondo
| 2023-11-02T12:26:32Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2023-10-31T01:03:35Z |
---
library_name: peft
base_model: gpt2
---
# 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
taozi555/TinyLlama-4.0bpw
|
taozi555
| 2023-11-02T11:56:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"dataset:bigcode/starcoderdata",
"dataset:cerebras/SlimPajama-627B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-02T11:49:02Z |
---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
- bigcode/starcoderdata
- cerebras/SlimPajama-627B
language:
- en
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
#### Base model:
PY007/TinyLlama-1.1B-intermediate-step-480k-1T
#### Dataset:
Fine tuned on OpenOrca GPT4 subset for 1 epoch,Using CHATML format
#### Model License:
Apache 2.0, following the TinyLlama base model.
#### Quantisation:
- GPTQ:https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GPTQ
- AWQ:https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-AWQ
- GGUF:https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF
#### Hardware and training details:
Hardware: 1*RTX A5000, ~16 hours to complete 1 epoch. GPU from autodl.com, cost around $3 for this finetuning.
https://wandb.ai/jeff200402/TinyLlama-Orca?workspace= for more details.
|
sofia-todeschini/BioBERT-LitCovid-v1.3.1
|
sofia-todeschini
| 2023-11-02T11:52:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-02T10:05:01Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioBERT-LitCovid-v1.3.1
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. -->
# BioBERT-LitCovid-v1.3.1
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6731
- Hamming loss: 0.0186
- F1 micro: 0.8434
- F1 macro: 0.3657
- F1 weighted: 0.8790
- F1 samples: 0.8763
- Precision micro: 0.7702
- Precision macro: 0.2942
- Precision weighted: 0.8399
- Precision samples: 0.8618
- Recall micro: 0.9320
- Recall macro: 0.7288
- Recall weighted: 0.9320
- Recall samples: 0.9432
- Roc Auc: 0.9581
- Accuracy: 0.6841
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Hamming loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 1.1065 | 1.0 | 2272 | 0.4899 | 0.0357 | 0.7347 | 0.2684 | 0.8280 | 0.8274 | 0.6112 | 0.2128 | 0.7700 | 0.7976 | 0.9207 | 0.7424 | 0.9207 | 0.9370 | 0.9437 | 0.5688 |
| 0.8552 | 2.0 | 4544 | 0.4641 | 0.0246 | 0.8018 | 0.3270 | 0.8588 | 0.8548 | 0.7057 | 0.2595 | 0.8123 | 0.8327 | 0.9282 | 0.7833 | 0.9282 | 0.9424 | 0.9531 | 0.6325 |
| 0.7061 | 3.0 | 6816 | 0.5058 | 0.0227 | 0.8166 | 0.3320 | 0.8679 | 0.8652 | 0.7201 | 0.2640 | 0.8146 | 0.8402 | 0.9429 | 0.7601 | 0.9429 | 0.9522 | 0.9611 | 0.6548 |
| 0.5914 | 4.0 | 9088 | 0.6116 | 0.0196 | 0.8368 | 0.3588 | 0.8758 | 0.8719 | 0.7572 | 0.2869 | 0.8321 | 0.8533 | 0.9353 | 0.7398 | 0.9353 | 0.9456 | 0.9591 | 0.6706 |
| 0.294 | 5.0 | 11360 | 0.6731 | 0.0186 | 0.8434 | 0.3657 | 0.8790 | 0.8763 | 0.7702 | 0.2942 | 0.8399 | 0.8618 | 0.9320 | 0.7288 | 0.9320 | 0.9432 | 0.9581 | 0.6841 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
Nbanks4/Xmas
|
Nbanks4
| 2023-11-02T11:41:29Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-11-02T11:41:29Z |
---
license: other
license_name: openai
license_link: https://app.alpha3d.io/new-project
---
|
linchunlu/falcon-7b-sharded-bf16-finetuned-mental-health-conversational
|
linchunlu
| 2023-11-02T11:34:21Z | 0 | 1 | null |
[
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"base_model:finetune:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | 2023-11-01T04:58:42Z |
---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: falcon-7b-sharded-bf16-finetuned-mental-health-conversational
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. -->
# falcon-7b-sharded-bf16-finetuned-mental-health-conversational
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
pyro-glitch/NEMO_AI_Compainon_v0.3
|
pyro-glitch
| 2023-11-02T11:32:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-02T11:19:28Z |
---
pipeline_tag: conversational
---
|
Duxiaoman-DI/XuanYuan-70B-Chat-8bit
|
Duxiaoman-DI
| 2023-11-02T11:30:37Z | 7 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-10-28T12:05:14Z |
---
license: llama2
---
XuanYuan-70B是基于Llama2-70b模型进行中文增强的一系列金融大模型，包含大量中英文语料增量预训练之后的底座模型以及使用高质量指令数据进行对齐的chat模型。
我们的目标是：大模型通用能力尽可能保持的同时，金融领域能力得到明显提升，服务于金融领域。
目前发布的模型和下载链接如下：
| | 基座模型 | Chat模型 | 8-bit量化Chat模型 | 4-bit量化Chat模型 |
| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| XuanYuan-70B-8k | 🤗 [XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B) | 🤗 [XuanYuan-70B-Chat](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B-Chat) | 🤗 [XuanYuan-70B-Chat-8bit](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B-Chat-8bit ) | 🤗 [XuanYuan-70B-Chat-4bit](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B-Chat-4bit) |
# 模型介绍
考虑到金融场景下存在非常多长文本的业务，基于我们高效的分布式训练框架，我们将模型的上下文长度在预训练阶段从4k扩充到了8k和16k，据我们所知，这也是首个在70B参数量级上达到8k及以上上下文长度的开源大模型。
具体细节参考：[XuanYuan-70B](https://github.com/Duxiaoman-DI/XuanYuan)
## 基座模型预训练
（1）**数据质量**
- 我们设计了一套数据清洗流水线，精心准备了各类通用数据（互联网网页、百科、论坛、社交媒体、问答等）以及金融相关数据（金融资讯、公司公告、金融百科、金融书籍、证书试题等）高质量数据
- 中英数据：首先llama2的英文能力足够优秀，所以为了保证英文能力不降，我们扩充词表之后，使用高质量的中英语料进行增量预训练，其中中英配比为3:1；
- 通用金融数据：为了提升模型在金融能力上效果，预训练过程中通用语料与金融预料比例为9:1，且随着训练进行，逐步提升金融语料的占比。
（2）**模型训练**
- 训练效率：我们采取了一系列的加速优化策略， 包括对底层数据加载和分布式训练框架的多处优化，使用flash attention2替代self-attention模块，使用基于CPP CUDA的Fused算子替代原始llama的python实现等
- 上下文长度：基于上述的优化方式，同时考虑到金融场景长上下文情景较多，我们能够在预训练阶段把llama2原始上下文4k的长度扩展到8k和16k；
我们在100台8卡A800(80G)的GPU集群中，训练情况如下：
| 模型 | 上下文长度 | 吞吐量 | 显卡利用 |
| ------------ | ---------- | ---------------- | -------- |
| XuanYuan-70B | 8192 | 340 tokens/s/gpu | 190TFOPS |
备注：（1）训练没有开梯度累计；（2）原始llama2-70b在4k上下文长度下的的吞吐量为323 tokens/s/gpu，说明我们的训练效率达到当前领先水平。
## Chat模型指令微调
基于上述的XuanYuan-70B基座模型，我们进行了详细的指令微调，基座使模型具备对话和遵循人类指令的能力。
我们采取了两阶段的指令微调，具体来说：
- 第一阶段：使用开源的大量的指令数据对基座模型来进行训练，这一部分我们收集了约10M条开源的多语种指令微调数据，并行清洗与深度过滤。这一阶段的目的是为了覆盖指令的多样性，提升模型指令遵循能力。
- 第二阶段：使用自研的高质量的指令数据来继续进行指令微调训练。这一阶段，我们精心自研约20万条通用+金融的指令微调数据，其中大部分数据均做了校验、改写来保证质量。 这一阶段是能够更加使得模型根据不同的需求和侧重来进行最后的训练。
我们自研的指令微调数据预期模型能够在通用对话能力保留的同时，更加侧重金融领域的问答。具体来说，通用指令数据分为以下几个大类：常识百科、代码编程、逻辑推理、数学计算、创意生成、安全无害、摘要提取、翻译等。其中每一大类下又设计了多个子类，来尽可能保证指令数据的多样性和丰富度。
对于金融领域的指令数据，我们进行了更加详细的子类划分，来覆盖金融经济的各个领域。在训练阶段，我们采取的配比为：通用指令数据与金融指令数据配比为4：1。
在训练过程中，我们同样保持8k的上下文长度，未采取外推的措施来提升上下文。后续我们将继续在预训练阶段来提升上下文长度。 同时训练数据中的question-answer pair，我们仅对answer部分计算损失。
# 快速使用
基座模型、Chat模型以及8-bit和4bit量化Chat模型均已发布在Hugging Face。下面我们给出基座模型和Chat模型的推理部署使用方法。
## 依赖安装
```
torch >= 2.0
transformers >= 4.33.1
accelerate
sentencepiece
bitsandbytes(8bit量化所需)
optimum(4bit量化所需)
auto-gptq(4bit量化所需)
vllm(推理加速所需)
```
资源需求：
- 对于基座模型和Chat模型，部署至少需要2张80G的显卡进行加载模型
- 对于8bit量化版本，推理部署至少需要1张80G的显卡进行加载模型
- 对于4bit量化版本，，推理部署至少需要1张40G的显卡进行加载模型
## Base模型使用方法
因为XuanYuan-70B系列模型均是基于Llama2-70B进行增量预训练而来，因此基座模型的使用方法与Llama2基座模型保持一致。
```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
model_name_or_path = "Duxiaoman-DI/XuanYuan-70B"
tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True)
model = LlamaForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.bfloat16,device_map="auto")
model.eval()
inputs = tokenizer("问题：李时珍是哪一个朝代的人？回答：", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1)
outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(outputs)
```
## Chat模型使用方法
在指令微调构造prompt的时候，我们参考了[FastChat](https://github.com/lm-sys/FastChat)的对话构造方式，简单代码示例如下：
```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
model_name_or_path = "Duxiaoman-DI/XuanYuan-70B-Chat"
tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True)
model = LlamaForCausalLM.from_pretrained(model_name_or_path, device_map="auto")
model.eval()
system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n"
seps = [" ", "</s>"]
roles = ["Human", "Assistant"]
content = "介绍下你自己"
prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":"
print(f"输入: {content}")
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(f"输出: {outputs}")
```
- 示例同时支持8bit和4bit的量化模型
- 示例仅为最简单的部署代码，没有考虑多轮、推理加速等; 完整demo请参考cli_demo.py
## CLI工具
我们github主页提供一个了基于命令行的demo，支持多轮对话和基于vLLM的推理加速。
> vllm暂时不支持量化模型
```
python3 cli_vllm_demo.py --checkpoint_path <XuanYuan-70B-Chat Path>
```
举例如下：
```
输入: 你好
输出: 你好，很高兴能为你提供帮助。
输入: 介绍下你自己
输出: 我是轩辕大模型，一个由度小满数据智能应用部AI Lab 开发的人工智能助手，我可以回答各种问题，提供实用的建议和帮助，帮助用户完成各种任务。
输入: 有2块五仁月饼，3块莲蓉月饼，2块豆沙月饼，这些月饼的大小形状质量完全相同。从这7块月饼中，任意取出3块，那么三种月饼都取到 的可能性是几分之几？
输出: 这是一个组合数学问题，我们可以通过计算组合数来解答。
三种月饼都取到，即取到五仁、莲蓉和豆沙各一块。
五仁月饼的选取方法有2种，莲蓉月饼的选取方法有3种，豆沙月饼的选取方法有2种，所以总的取出一种五仁、一种莲蓉、一种豆沙的方法有2*3*2=12种。
从7块月饼中任意取出3块月饼的总的组合数为C(7,3)=35种。
所以，从这7块月饼中，任意取出3块，三种月饼都取到 的可能性为12/35。
```
## 量化部署
为了降低用户在本地使用XuanYuan的成本，降低显存需求，我们提供量化好的Xuanyuan-70B-Chat模型8bit和4bit模型。
**8bit离线量化模型**
在8bit量化算法上，我们使用目前社区广泛使用的[bitsandbytes](https://github.com/TimDettmers/bitsandbytes)库。该库包含LLM.int8()量化算法的实现以及一系列量化的工具，
同时该方法已在transformers库里做了集成，使用较为容易。经过我们的测试，8bit量化可以近乎无损。
**4bit离线量化模型**
在4bit量化算法上，我们使用[auto-gptq](https://github.com/PanQiWei/AutoGPTQ)工具。该库实现的GPTQ算法是目前4bit量化最受欢迎的方法，
同时该方法在transformers库和optimum库里做了集成，使用较为容易。
下表给出了不同模型所需显存，以及在三个评测基准上CEVAL，CMMLU和MMLU上效果：
| 模型 | 显存 | CEVAL | CMMLU | MMLU |
| ---------------------- | ---- | ----- | ----- | ---- |
| XuanYuan-70B-Chat | 129G | 62.15 | 60.41 | 65.3 |
| XuanYuan-70B-Chat-8bit | 65G | 62.25 | 59.99 | 65.0 |
| XuanYuan-70B-Chat-4bit | 35G | 60.94 | 58.76 | 63.0 |
可以看出：
- 8bit和4bit的量化模型相比原始float16的模型，空间分别降低为原来的1/2和1/4。能够显著降低硬件需求。
- 8bit的量化模型相原始float16的模型，效果近乎无损，4bit的量化模型，大概下降2个点左右。
- 此外，我们也对量化版本的Chat模型进行对话人工评测，结论与评测基准类似。
使用量化模请参考上面的Chat模型使用方法的示例代码。
|
hongyin/agent-llama2-13b-80k
|
hongyin
| 2023-11-02T11:28:13Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"zh",
"arxiv:2302.13173",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-27T04:16:53Z |
---
language:
- en
- zh
pipeline_tag: text-generation
---
## hongyin/agent-informer-13b-80k
I am pleased to introduce an English-Chinese AI agent designed to reduce the cost of inference. It is trained based on the THUDM/agentlm-13b, with a unique vocabulary and 13 billion parameters.
Losing fat is the only way to solve all problems.
```python
```
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
---
|
hongyin/chat-mistral-7b-80k
|
hongyin
| 2023-11-02T11:26:14Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"zh",
"arxiv:2302.13173",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-27T04:17:17Z |
---
language:
- en
- zh
pipeline_tag: text-generation
---
## hongyin/chat-mistral-7b-80k
I am pleased to introduce an English-Chinese conversation assistant designed to reduce the cost of inference. It is trained based on the Mistral-7B-Instruct, with a unique vocabulary and 7 billion parameters.
Losing fat is the only way to solve all problems.
```python
```
## Bibtex entry and citation info
Please cite if you find it helpful.
```
@article{zhu2023metaaid,
title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
author={Zhu, Hongyin},
journal={arXiv preprint arXiv:2302.13173},
year={2023}
}
```
---
license: other
---
|
jayclifford345/vibration-autoencoder
|
jayclifford345
| 2023-11-02T11:24:51Z | 11 | 1 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"anomaly-detection",
"vibration",
"autoencoder",
"region:us"
] | null | 2023-06-28T13:59:55Z |
---
library_name: keras
tags:
- anomaly-detection
- vibration
- autoencoder
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
lsvignesh12596/wav2vec2-large-xls-r-300m-hindi-cv13-colab
|
lsvignesh12596
| 2023-11-02T11:24:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-02T09:13:48Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-cv13-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 0.6041002277904328
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-cv13-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9057
- Wer: 0.6041
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.5977 | 4.08 | 400 | 1.4545 | 0.8438 |
| 0.6234 | 8.16 | 800 | 0.9057 | 0.6041 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
cadaeic/Llama2-7B-QLoRA-cooking_2
|
cadaeic
| 2023-11-02T11:09:57Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-11-02T10:09:16Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# 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]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
sharkMeow/ADL_test
|
sharkMeow
| 2023-11-02T10:54:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-02T04:22:33Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: ADL_test
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. -->
# ADL_test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9924
- Rouge-1: 19.8975
- Rouge-2: 7.0134
- Rouge-l: 18.2845
- Gen Len: 14.3786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l |
|:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:-------:|:-------:|
| 6.0773 | 1.0 | 1356 | 11.8835 | 4.1822 | 17.4884 | 6.039 | 16.4024 |
| 5.0033 | 2.0 | 2713 | 13.385 | 4.0972 | 18.9145 | 6.6873 | 17.5342 |
| 4.7707 | 3.0 | 4068 | 14 | 4.019 | 19.62 | 6.908 | 18.08 |
| 4.7391 | 4.0 | 5424 | 14.3786 | 3.992 | 19.9 | 7.013 | 18.28 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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