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ccateni/UpdatedModels | ccateni | 2023-11-29T03:24:51Z | 0 | 0 | null | [
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SeanLiu/splat | SeanLiu | 2023-11-29T16:55:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:55:22Z | 2023-11-08T14:34:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | SeanLiu/splat | [
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longisland3/generate_experience | longisland3 | 2023-11-29T15:37:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:37:33Z | 2023-11-10T02:45:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | longisland3/generate_experience | [
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IA-GAB/SHINEE | IA-GAB | 2023-11-29T01:45:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T01:45:02Z | 2023-11-11T03:51:18.000Z | null | null | ---
license: apache-2.0
---
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ColbyDash/Dashs-RVC-Models | ColbyDash | 2023-11-29T20:15:58Z | 0 | 0 | null | [
"license:cc-by-2.0",
"region:us"
] | 2023-11-29T20:15:58Z | 2023-11-11T21:28:52.000Z | null | null | ---
license: cc-by-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | ColbyDash/Dashs-RVC-Models | [
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nekofura/avtr | nekofura | 2023-11-29T18:44:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T18:44:51Z | 2023-11-12T15:35:48.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | nekofura/avtr | [
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csukuangfj/vits-piper-fr_FR-tjiho-model1 | csukuangfj | 2023-11-29T08:44:37Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T08:44:37Z | 2023-11-14T02:20:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-fr_FR-tjiho-model1 | [
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csukuangfj/vits-piper-fr_FR-tjiho-model2 | csukuangfj | 2023-11-29T08:44:34Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T08:44:34Z | 2023-11-14T02:20:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-fr_FR-tjiho-model2 | [
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csukuangfj/vits-piper-fr_FR-tjiho-model3 | csukuangfj | 2023-11-29T08:44:37Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T08:44:37Z | 2023-11-14T02:21:03.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-fr_FR-tjiho-model3 | [
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phqlong/vietcuna-7b-v3-qlora-gpt4augmented-combined-v1 | phqlong | 2023-11-29T05:33:23Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:vilm/vietcuna-7b-v3",
"region:us"
] | 2023-11-29T05:33:23Z | 2023-11-14T04:47:18.000Z | null | null | ---
library_name: peft
base_model: vilm/vietcuna-7b-v3
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## 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: float16
### Framework versions
- PEFT 0.6.3.dev0
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Taylor658/Mistral07b-Nov23 | Taylor658 | 2023-11-29T03:03:18Z | 0 | 0 | null | [
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | 2023-11-29T03:03:18Z | 2023-11-15T02:08:59.000Z | null | null | ---
license: apache-2.0
---
# Model Card for Model ID
Fine Tuned with Medical Question Answers
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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Yasinjan99/xls-r-uyghur-cv11 | Yasinjan99 | 2023-11-29T21:32:50Z | 0 | 0 | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_11_0",
"generated_from_trainer",
"ug",
"dataset:common_voice_11_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:32:50Z | 2023-11-15T06:33:50.000Z | null | null | ---
language:
- ug
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_11_0
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: xls-r-uyghur-cv11
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. -->
# xls-r-uyghur-cv11
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - UG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Wer: 0.3257
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3958 | 4.36 | 500 | 3.3137 | 1.0 |
| 3.032 | 8.71 | 1000 | 2.8586 | 0.9993 |
| 1.3977 | 13.07 | 1500 | 0.4786 | 0.6375 |
| 1.2751 | 17.43 | 2000 | 0.3816 | 0.5393 |
| 1.2113 | 21.79 | 2500 | 0.3451 | 0.5099 |
| 1.156 | 26.14 | 3000 | 0.3245 | 0.4919 |
| 1.1226 | 30.5 | 3500 | 0.2992 | 0.4441 |
| 1.0913 | 34.86 | 4000 | 0.2831 | 0.4315 |
| 1.0615 | 39.22 | 4500 | 0.2808 | 0.4340 |
| 1.0455 | 43.57 | 5000 | 0.2713 | 0.4088 |
| 1.0228 | 47.93 | 5500 | 0.2622 | 0.3960 |
| 0.9936 | 52.29 | 6000 | 0.2525 | 0.3796 |
| 0.968 | 56.64 | 6500 | 0.2506 | 0.3798 |
| 0.9704 | 61.0 | 7000 | 0.2481 | 0.3735 |
| 0.9552 | 65.36 | 7500 | 0.2394 | 0.3643 |
| 0.9417 | 69.72 | 8000 | 0.2350 | 0.3537 |
| 0.9215 | 74.07 | 8500 | 0.2326 | 0.3507 |
| 0.9097 | 78.43 | 9000 | 0.2277 | 0.3487 |
| 0.9003 | 82.79 | 9500 | 0.2230 | 0.3362 |
| 0.8857 | 87.15 | 10000 | 0.2246 | 0.3362 |
| 0.882 | 91.5 | 10500 | 0.2236 | 0.3315 |
| 0.8719 | 95.86 | 11000 | 0.2203 | 0.3271 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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aashish-249/Telugu-sentiment_analysis | aashish-249 | 2023-11-29T11:14:37Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:14:37Z | 2023-11-16T05:13:33.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Telugu-sentiment_analysis
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. -->
# Telugu-sentiment_analysis
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9966
- Accuracy: 0.5381
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0084 | 1.0 | 1758 | 1.0049 | 0.5381 |
| 1.0044 | 2.0 | 3516 | 1.0062 | 0.5381 |
| 1.003 | 3.0 | 5274 | 1.0006 | 0.5381 |
| 1.0034 | 4.0 | 7032 | 0.9985 | 0.5381 |
| 1.0014 | 5.0 | 8790 | 0.9981 | 0.5381 |
| 1.0007 | 6.0 | 10548 | 0.9975 | 0.5381 |
| 1.0009 | 7.0 | 12306 | 0.9973 | 0.5381 |
| 1.0004 | 8.0 | 14064 | 0.9970 | 0.5381 |
| 0.9992 | 9.0 | 15822 | 0.9968 | 0.5381 |
| 0.9986 | 10.0 | 17580 | 0.9966 | 0.5381 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Xenos14/embeddings | Xenos14 | 2023-11-29T13:34:06Z | 0 | 0 | null | [
"has_space",
"region:us"
] | 2023-11-29T13:34:06Z | 2023-11-16T07:14:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Xenos14/embeddings | [
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katielink/example-gated-model | katielink | 2023-11-29T20:36:01Z | 0 | 0 | null | [
"chemistry",
"license:other",
"region:us"
] | 2023-11-29T20:36:01Z | 2023-11-16T18:51:39.000Z | null | null | ---
license: other
license_name: your-custom-license-here
license_link: LICENSE
extra_gated_heading: Acknowledge license to access the repository
extra_gated_prompt: You agree to all of the terms in the License.
extra_gated_fields:
Organization: text
Country: text
I agree to use this model for non-commercial use ONLY: checkbox
extra_gated_button_content: Acknowledge license
tags:
- chemistry
---
# Example Gated Model Repository
This is just an example model repo to showcase some of the options for releasing your model. | null | null | null | null | null | null | null | null | null | null | null | null | katielink/example-gated-model | [
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gianyrox/SeussDream | gianyrox | 2023-11-29T05:29:03Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T05:29:03Z | 2023-11-16T19:49:43.000Z | null | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of Dr. Seuss's Lorax
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - gianyrox/SeussDream
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of Dr. Seuss's Lorax using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | gianyrox/SeussDream | [
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ayurukaaa/RVC_MODEL | ayurukaaa | 2023-11-29T13:14:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:14:40Z | 2023-11-16T23:50:55.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ayurukaaa/RVC_MODEL | [
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SteffRhes/APIS_OEBL__NER_trf | SteffRhes | 2023-11-29T16:18:44Z | 0 | 0 | null | [
"spacy",
"token-classification",
"de",
"dataset:SteffRhes/APIS_OEBL__Named_Entity_Recognition",
"license:mit",
"region:us"
] | 2023-11-29T16:18:44Z | 2023-11-17T13:36:29.000Z | null | null | ---
license: mit
language:
- de
library_name: spacy
pipeline_tag: token-classification
datasets:
- SteffRhes/APIS_OEBL__Named_Entity_Recognition
---
# APIS ÖBL NER
A spaCy NER model, based on [de_dep_news_trf-3.6.1](https://github.com/explosion/spacy-models/releases/tag/de_dep_news_trf-3.6.1), trained on data originating from the [APIS project](https://www.oeaw.ac.at/acdh/projects/completed-projects/apis) and the [Austrian Biographical Lexicon (ÖBL)](https://www.oeaw.ac.at/acdh/oebl).
data split:
- train: 80%
- dev: 10%
- eval: 10%
Scores:
- P: 79.72
- R: 84.28
- F: 81.93
| null | spacy | token-classification | null | null | null | null | null | null | null | null | null | SteffRhes/APIS_OEBL__NER_trf | [
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pirocheto/phishing-url-detection | pirocheto | 2023-11-29T18:16:38Z | 0 | 0 | null | [
"sklearn",
"onnx",
"text-classification",
"phishing",
"url",
"dataset:pirocheto/phishing-url",
"license:mit",
"region:us"
] | 2023-11-29T18:16:38Z | 2023-11-17T18:36:30.000Z | null | null | ---
license: mit
library_name: sklearn
tags:
- text-classification
- sklearn
- phishing
- url
- onnx
model_format: pickle
model_file: model.pkl
inference: false
pipeline_tag: text-classification
datasets:
- pirocheto/phishing-url
---
# Model Description
The model predicts the probability that a URL is a phishing site.
To understand what phishing is, refer to the Wikipedia page:
[https://en.wikipedia.org/wiki/Phishing](https://en.wikipedia.org/wiki/Phishing)
-- this is not a phishing link 😜
- **Model type:** LinearSVM
- **Task:** Binary classification
- **License:** MIT
- **Repository:** https://github.com/pirocheto/phishing-url-detection
## Evaluation
| Metric | Value |
|-----------|----------|
| roc_auc | 0.986467 |
| accuracy | 0.948038 |
| f1 | 0.947817 |
| precision | 0.951872 |
| recall | 0.943796 |
# How to Get Started with the Model
Using pickle in Python is discouraged due to security risks during data deserialization, potentially allowing code injection.
It lacks portability across Python versions and interoperability with other languages.
Read more about this subject in the [Hugging Face Documentation](https://huggingface.co/docs/hub/security-pickle).
Instead, we recommend using the ONNX model, which is more secure.
In addition to being lighter and faster, it can be utilized by languages supported by the [ONNX runtime](https://onnxruntime.ai/docs/get-started/).
Below are some examples to get you start. For others languages please refer to the ONNX documentation
<details>
<summary><b>Python</b> - ONNX - [recommended 👍]</summary>
```python
import numpy as np
import onnxruntime
from huggingface_hub import hf_hub_download
REPO_ID = "pirocheto/phishing-url-detection"
FILENAME = "model.onnx"
model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
# Initializing the ONNX Runtime session with the pre-trained model
sess = onnxruntime.InferenceSession(
model_path,
providers=["CPUExecutionProvider"],
)
urls = [
"https://clubedemilhagem.com/home.php",
"http://www.medicalnewstoday.com/articles/188939.php",
]
inputs = np.array(urls, dtype="str")
# Using the ONNX model to make predictions on the input data
results = sess.run(None, {"inputs": inputs})[1]
for url, proba in zip(urls, results):
print(f"URL: {url}")
print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f} %")
print("----")
```
</details>
<details>
<summary><b>NodeJS</b>- ONNX - [recommended 👍]</summary>
```javascript
const ort = require('onnxruntime-node');
async function main() {
try {
// Make sure you have downloaded the model.onnx
// Creating an ONNX inference session with the specified model
const model_path = "./model.onnx";
const session = await ort.InferenceSession.create(model_path);
const urls = [
"https://clubedemilhagem.com/home.php",
"http://www.medicalnewstoday.com/articles/188939.php",
]
// Creating an ONNX tensor from the input data
const tensor = new ort.Tensor('string', urls, [urls.length,]);
// Executing the inference session with the input tensor
const results = await session.run({"inputs": tensor});
const probas = results['probabilities'].data;
// Displaying results for each URL
urls.forEach((url, index) => {
const proba = probas[index * 2 + 1];
const percent = (proba * 100).toFixed(2);
console.log(`URL: ${url}`);
console.log(`Likelihood of being a phishing site: ${percent}%`);
console.log("----");
});
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
}
};
main();
```
</details>
<details>
<summary><b>JavaScript</b> - ONNX - [recommended 👍]</summary>
```html
<!DOCTYPE html>
<html>
<header>
<title>Get Started with JavaScript</title>
</header>
<body>
<!-- import ONNXRuntime Web from CDN -->
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
<script>
// use an async context to call onnxruntime functions.
async function main() {
try {
const model_path = "./model.onnx";
const session = await ort.InferenceSession.create(model_path);
const urls = [
"https://clubedemilhagem.com/home.php",
"http://www.medicalnewstoday.com/articles/188939.php",
];
// Creating an ONNX tensor from the input data
const tensor = new ort.Tensor("string", urls, [urls.length]);
// Executing the inference session with the input tensor
const results = await session.run({ inputs: tensor });
const probas = results["probabilities"].data;
// Displaying results for each URL
urls.forEach((url, index) => {
const proba = probas[index * 2 + 1];
const percent = (proba * 100).toFixed(2);
document.write(`URL: ${url} <br>`);
document.write(
`Likelihood of being a phishing site: ${percent} % <br>`
);
document.write("---- <br>");
});
} catch (e) {
document.write(`failed to inference ONNX model: ${e}.`);
}
}
main();
</script>
</body>
</html>
```
</details>
<details>
<summary><b>Python</b> - Pickle - [not recommended ⚠️]</summary>
```python
import joblib
from huggingface_hub import hf_hub_download
REPO_ID = "pirocheto/phishing-url-detection"
FILENAME = "model.pkl"
# Download the model from the Hugging Face Model Hub
model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
urls = [
"https://clubedemilhagem.com/home.php",
"http://www.medicalnewstoday.com/articles/188939.php",
]
# Load the downloaded model using joblib
model = joblib.load(model_path)
# Predict probabilities for each URL
probas = model.predict_proba(urls)
for url, proba in zip(urls, probas):
print(f"URL: {url}")
print(f"Likelihood of being a phishing site: {proba[1] * 100:.2f} %")
print("----")
```
</details>
| null | sklearn | text-classification | null | null | null | null | null | null | null | null | null | pirocheto/phishing-url-detection | [
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jlbaker361/fine-tune_addition_decimal | jlbaker361 | 2023-11-29T06:53:57Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T06:53:57Z | 2023-11-18T07:36:35.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
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- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | jlbaker361/fine-tune_addition_decimal | [
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jlbaker361/fine-tune_subtraction_decimal_whole | jlbaker361 | 2023-11-29T06:48:31Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T06:48:31Z | 2023-11-18T13:35:43.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
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## Training procedure
### Framework versions
- PEFT 0.6.2
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jlbaker361/fine-tune_division_multiplication_decimal | jlbaker361 | 2023-11-29T01:31:02Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T01:31:02Z | 2023-11-18T16:04:48.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | jlbaker361/fine-tune_division_multiplication_decimal | [
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jlbaker361/fine-tune_addition_subtraction_decimal_whole | jlbaker361 | 2023-11-29T02:44:20Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T02:44:20Z | 2023-11-18T23:49:04.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
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- PEFT 0.6.2
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nikhilnikhil/audio-sound | nikhilnikhil | 2023-11-29T03:06:48Z | 0 | 0 | null | [
"dataset:nikhilnikhil/austin-music",
"region:us"
] | 2023-11-29T03:06:48Z | 2023-11-19T02:45:50.000Z | null | null | ---
datasets:
- nikhilnikhil/austin-music
--- | null | null | null | null | null | null | null | null | null | null | null | null | nikhilnikhil/audio-sound | [
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qdas/Perfection | qdas | 2023-11-29T23:25:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:25:16Z | 2023-11-19T05:30:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | qdas/Perfection | [
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jlbaker361/fine-tune_division_multiplication_decimal_whole | jlbaker361 | 2023-11-29T11:45:05Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:gpt2",
"region:us"
] | 2023-11-29T11:45:05Z | 2023-11-19T07:15:29.000Z | null | null | ---
library_name: peft
base_model: gpt2
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
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AyhanCagan/turkishReviews-ds-mini | AyhanCagan | 2023-11-29T11:02:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:02:13Z | 2023-11-19T13:46:38.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AyhanCagan/turkishReviews-ds-mini | [
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venketh/gpt2-xmedium-2048 | venketh | 2023-11-29T15:41:24Z | 0 | 0 | null | [
"gpt2",
"dataset:Skylion007/openwebtext",
"dataset:cerebras/SlimPajama-627B",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:41:24Z | 2023-11-20T03:50:43.000Z | null | null | ---
datasets:
- Skylion007/openwebtext
- cerebras/SlimPajama-627B
tags:
- gpt2
license: apache-2.0
--- | null | null | null | null | null | null | null | null | null | null | null | null | venketh/gpt2-xmedium-2048 | [
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] |
Intel/sd-1.5-lcm-openvino | Intel | 2023-11-29T13:02:04Z | 0 | 0 | null | [
"text-to-image",
"arxiv:2310.04378",
"license:mit",
"region:us"
] | 2023-11-29T13:02:04Z | 2023-11-20T16:27:21.000Z | null | null | ---
license: mit
pipeline_tag: text-to-image
---
# SD v1-5 Latent Consistency Models (LCM) Model Card
The original source of this model is : [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7).
This model is just optimized and converted to Intermediate Representation (IR) using OpenVino's Model Optimizer to run on Intel's Hardware - CPU, GPU.
Intended to be used with:
- GIMP plugin [openvino-ai-plugins-gimp](https://github.com/intel/openvino-ai-plugins-gimp.git)
### Latent Consistency Models
Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*.
Project Page: https://latent-consistency-models.github.io
### BibTeX
```bibtex
@misc{luo2023latent,
title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference},
author={Simian Luo and Yiqin Tan and Longbo Huang and Jian Li and Hang Zhao},
year={2023},
eprint={2310.04378},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Intel’s Human Rights Disclaimer:
Intel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right. | null | null | text-to-image | null | null | null | null | null | null | null | null | null | Intel/sd-1.5-lcm-openvino | [
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Amanastam/Das | Amanastam | 2023-11-29T14:59:04Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:59:04Z | 2023-11-21T02:43:21.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Amanastam/Das | [
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takakou2/zwa | takakou2 | 2023-11-29T06:00:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T06:00:21Z | 2023-11-21T05:03:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | takakou2/zwa | [
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ngocminhta/Llama-2-vitd-tinycode | ngocminhta | 2023-11-29T05:18:44Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"dataset:nampdn-ai/tiny-codes",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T05:18:44Z | 2023-11-21T07:20:42.000Z | null | null | ---
license: apache-2.0
datasets:
- nampdn-ai/tiny-codes
language:
- en
pipeline_tag: text-generation
tags:
- code
---
# Llama-2 Model for Code Generation of Big data projects
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned from Llama-2 7B for code generation towards integrations to some big data projects.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Ngoc-Minh Ta, Digital & IT Technology Department, IT Division, Viettel Group
- **Model type:** Llama-2
- **Language(s) (NLP):** English, code
- **License:** Apache 2.0
- **Finetuned from model:** meta-llama/Llama-2-7b-chat-hf | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ngocminhta/Llama-2-vitd-tinycode | [
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Winniewu/Llama-2-7b-chat-finetune | Winniewu | 2023-11-29T22:11:18Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:11:18Z | 2023-11-21T20:22:52.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Winniewu/Llama-2-7b-chat-finetune | [
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LoftQ/Mistral-7B-v0.1-4bit-32rank | LoftQ | 2023-11-29T04:27:14Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:27:14Z | 2023-11-22T05:05:56.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoftQ/Mistral-7B-v0.1-4bit-32rank | [
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NurtureAI/Orca-2-13B-16k-GGUF | NurtureAI | 2023-11-30T01:17:36Z | 0 | 5 | null | [
"gguf",
"orca",
"orca2",
"microsoft",
"text-generation",
"arxiv:2311.11045",
"license:other",
"region:us"
] | 2023-11-30T01:17:36Z | 2023-11-22T05:27:54.000Z | null | null | ---
pipeline_tag: text-generation
tags:
- orca
- orca2
- microsoft
license: other
license_name: microsoft-research-license
license_link: LICENSE
---
# Orca 2 13b extended to 16k context (GGUF files)
# Original Model
# Orca 2
<!-- Provide a quick summary of what the model is/does. -->
Orca 2 is a helpful assistant that is built for research purposes only and provides a single turn response
in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization.
The model is designed to excel particularly in reasoning.
We publicly release Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.
## What is Orca 2’s intended use(s)?
+ Orca 2 is built for research purposes only.
+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for
building better frontier models.
## How was Orca 2 evaluated?
+ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer
to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations.
## Model Details
Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities.
All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf).
Please refer to LLaMA-2 technical report for details on the model architecture.
## License
Orca 2 is licensed under the [Microsoft Research License](LICENSE).
Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
## Bias, Risks, and Limitations
Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
common limitations of other large language models or limitation caused by its training process,
including:
**Data Biases**: Large language models, trained on extensive data, can inadvertently carry
biases present in the source data. Consequently, the models may generate outputs that could
be potentially biased or unfair.
**Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting
in potential inaccuracies or nonsensical responses.
**Lack of Transparency**: Due to the complexity and size, large language models can act
as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or
decisions. We recommend reviewing transparency notes from Azure for more information.
**Content Harms**: There are various types of content harms that large language models
can cause. It is important to be aware of them when using these models, and to take
actions to prevent them. It is recommended to leverage various content moderation services
provided by different companies and institutions. On an important note, we hope for better
regulations and standards from government and technology leaders around content harms
for AI technologies in future. We value and acknowledge the important role that research
and open source community can play in this direction.
**Hallucination**: It is important to be aware and cautious not to entirely rely on a given
language model for critical decisions or information that might have deep impact as it is
not obvious how to prevent these models from fabricating content. Moreover, it is not clear
whether small models may be more susceptible to hallucination in ungrounded generation
use cases due to their smaller sizes and hence reduced memorization capacities. This is an
active research topic and we hope there will be more rigorous measurement, understanding
and mitigations around this topic.
**Potential for Misuse**: Without suitable safeguards, there is a risk that these models could
be maliciously used for generating disinformation or harmful content.
**Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution
of the tuning data. This correlation might limit its accuracy in areas underrepresented in
the training dataset such as math, coding, and reasoning.
**System messages**: Orca 2 demonstrates variance in performance depending on the system
instructions. Additionally, the stochasticity introduced by the model size may lead to
generation of non-deterministic responses to different system instructions.
**Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings.
While the model demonstrate very strong performance in zero-shot settings, it does not show
the same gains of using few-shot learning compared to other, specially larger, models.
**Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages
and shortcomings of the models and methods used for data generation. We posit that Orca
2 benefits from the safety measures incorporated during training and safety guardrails (e.g.,
content filter) within the Azure OpenAI API. However, detailed studies are required for
better quantification of such risks.
This model is solely designed for research settings, and its testing has only been carried
out in such environments. It should not be used in downstream applications, as additional
analysis is needed to assess potential harm or bias in the proposed application.
## Getting started with Orca 2
**Inference with Hugging Face library**
```python
import torch
import transformers
if torch.cuda.is_available():
torch.set_default_device("cuda")
else:
torch.set_default_device("cpu")
model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Orca-2-13b", device_map='auto')
# https://github.com/huggingface/transformers/issues/27132
# please use the slow tokenizer since fast and slow tokenizer produces different tokens
tokenizer = transformers.AutoTokenizer.from_pretrained(
"microsoft/Orca-2-13b",
use_fast=False,
)
system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?"
prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
inputs = tokenizer(prompt, return_tensors='pt')
output_ids = model.generate(inputs["input_ids"],)
answer = tokenizer.batch_decode(output_ids)[0]
print(answer)
# This example continues showing how to add a second turn message by the user to the conversation
second_turn_user_message = "Give me a list of the key points of your first answer."
# we set add_special_tokens=False because we dont want to automatically add a bos_token between messages
second_turn_message_in_markup = f"\n<|im_start|>user\n{second_turn_user_message}<|im_end|>\n<|im_start|>assistant"
second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False)
second_turn_input = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1)
output_ids_2 = model.generate(second_turn_input,)
second_turn_answer = tokenizer.batch_decode(output_ids_2)[0]
print(second_turn_answer)
```
**Safe inference with Azure AI Content Safety**
The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged
and can help prevent content harms. Azure AI Content Safety is a content moderation platform
that uses AI to keep your content safe. By integrating Orca 2 with Azure AI Content Safety,
we can moderate the model output by scanning it for sexual content, violence, hate, and
self-harm with multiple severity levels and multi-lingual detection.
```python
import os
import math
import transformers
import torch
from azure.ai.contentsafety import ContentSafetyClient
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import HttpResponseError
from azure.ai.contentsafety.models import AnalyzeTextOptions
CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"]
CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"]
# We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold
# For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/
def should_filter_out(input_text, threshold=4):
# Create an Content Safety client
client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY))
# Construct a request
request = AnalyzeTextOptions(text=input_text)
# Analyze text
try:
response = client.analyze_text(request)
except HttpResponseError as e:
print("Analyze text failed.")
if e.error:
print(f"Error code: {e.error.code}")
print(f"Error message: {e.error.message}")
raise
print(e)
raise
categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"]
max_score = -math.inf
for category in categories:
max_score = max(max_score, getattr(response, category).severity)
return max_score >= threshold
model_path = 'microsoft/Orca-2-13b'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = transformers.AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_path,
model_max_length=4096,
padding_side="right",
use_fast=False,
add_special_tokens=False,
)
system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
user_message = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No."
prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
inputs = tokenizer(prompt, return_tensors='pt')
inputs = inputs.to(device)
output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True)
sequence_length = inputs["input_ids"].shape[1]
new_output_ids = output_ids[:, sequence_length:]
answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True)
final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]"
print(final_output)
```
## Citation
```bibtex
@misc{mitra2023orca,
title={Orca 2: Teaching Small Language Models How to Reason},
author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah},
year={2023},
eprint={2311.11045},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
``` | null | null | text-generation | null | null | null | null | null | null | null | null | null | NurtureAI/Orca-2-13B-16k-GGUF | [
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binxia/llmga-sd15-t2i | binxia | 2023-11-29T07:42:34Z | 0 | 0 | null | [
"diffusers",
"license:mit",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T07:42:34Z | 2023-11-22T10:08:37.000Z | null | null | ---
license: mit
---
| null | diffusers | null | null | null | null | null | null | null | null | null | null | binxia/llmga-sd15-t2i | [
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grboviczoja/wav2vec2-base-timit-demo-colab | grboviczoja | 2023-11-29T13:33:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:33:36Z | 2023-11-22T12:21:38.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | grboviczoja/wav2vec2-base-timit-demo-colab | [
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Praghxx/Litlleprag | Praghxx | 2023-11-29T16:09:41Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:09:41Z | 2023-11-22T13:29:07.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Praghxx/Litlleprag | [
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nvidia/Llama2-70B-SteerLM-Chat | nvidia | 2023-11-29T01:51:32Z | 0 | 6 | null | [
"nemo",
"nvidia",
"steerlm",
"llama2",
"text-generation",
"en",
"dataset:nvidia/HelpSteer",
"dataset:OpenAssistant/oasst1",
"arxiv:2311.09528",
"arxiv:2310.05344",
"license:llama2",
"region:us"
] | 2023-11-29T01:51:32Z | 2023-11-22T19:02:52.000Z | null | null | ---
license: llama2
library_name: nemo
language:
- en
pipeline_tag: text-generation
inference: false
fine-tuning: true
tags:
- nvidia
- steerlm
- llama2
datasets:
- nvidia/HelpSteer
- OpenAssistant/oasst1
---
# Llama2-70B-SteerLM-Chat
## License
The use of this model is governed by the [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/)
## Description:
Llama2-70B-SteerLM-Chat is a 70 billion parameter generative language model instruct-tuned using SteerLM technique. It takes input with context length up to 4,096 tokens.
The model has been aligned using the SteerLM method developed by NVIDIA to allow for user control of model outputs (in creativity, complexity and verbosity) during inference while having simplified training compared to RLHF techniques.
Llama2-70B-SteerLM-Chat reaches **7.54** on MT Bench, the highest among **commercial-use-friendly** models trained on open-source datasets based on [MT Bench Leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) as of 15 Nov 2023.
Try this model instantly for free hosted by us at [NVIDIA AI Playground](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/llama2-70b-steerlm). You can use this in the provided UI or through a limited access API (up to 10, 000 requests within 30 days). If you would need more requests, we demonstrate how you can set up an inference server below.
<img src="https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat/resolve/main/mtbench_categories.png" alt="MT Bench Categories" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
HelpSteer Paper : [HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](http://arxiv.org/abs/2311.09528)
SteerLM Paper: [SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF](https://arxiv.org/abs/2310.05344)
Llama2-70B-SteerLM-Chat is trained with NVIDIA NeMo, an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.
## Model Architecture:
**Architecture Type:** Transformer
**Network Architecture:** Llama 2
The SteerLM method involves the following key steps:
1. Train an attribute prediction model on human annotated data to evaluate response quality.
2. Use this model to annotate diverse datasets and enrich training data.
3. Perform conditioned fine-tuning to align responses with specified combinations of attributes.
Llama2-70B-SteerLM-Chat applies this technique on top of the Llama 2 70B Foundational model architecture. It was pretrained on internet-scale data and then aligned using [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1) and [HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer).
## Software Integration:
**Runtime Engine(s):**
NVIDIA AI Enterprise
**Toolkit:**
NeMo Framework
**Supported Hardware Architecture Compatibility:**
H100, A100 80GB, A100 40GB
## Steps to run inference:
We demonstrate inference using NVIDIA NeMo Framework, which allows hassle-free model deployment based on [NVIDIA TRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), a highly optimized inference solution focussing on high throughput and low latency.
Pre-requisite: You would need at least a machine with 4 40GB or 2 80GB NVIDIA GPUs, and 300GB of free disk space.
1. Please sign up to get **free and immediate** access to [NVIDIA NeMo Framework container](https://developer.nvidia.com/nemo-framework). If you don’t have an NVIDIA NGC account, you will be prompted to sign up for an account before proceeding.
2. If you don’t have an NVIDIA NGC API key, sign into [NVIDIA NGC](https://ngc.nvidia.com/setup), selecting organization/team: ea-bignlp/ga-participants and click Generate API key. Save this key for the next step. Else, skip this step.
3. On your machine, docker login to nvcr.io using
```
docker login nvcr.io
Username: $oauthtoken
Password: <Your Saved NGC API Key>
```
4. Download the required container
```
docker pull nvcr.io/ea-bignlp/ga-participants/nemofw-inference:23.10
```
5. Download the checkpoint
```
git lfs install
git clone https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat
```
6. Convert checkpoint into nemo format
```
cd Llama2-70B-SteerLM-Chat/Llama2-70B-SteerLM-Chat
tar -cvf Llama2-70B-SteerLM-Chat.nemo .
mv Llama2-70B-SteerLM-Chat.nemo ../
cd ..
rm -r Llama2-70B-SteerLM-Chat
```
7. Run Docker container
```
docker run --gpus all -it --rm --shm-size=300g -p 8000:8000 -v ${PWD}/Llama2-70B-SteerLM-Chat.nemo:/opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo -w /opt/NeMo nvcr.io/ea-bignlp/ga-participants/nemofw-inference:23.10
```
8. Within the container, start the server in the background. This step does both conversion of the nemo checkpoint to TRT-LLM and then deployment using TRT-LLM. For an explanation of each argument and advanced usage, please refer to [NeMo FW Deployment Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/deployingthenemoframeworkmodel.html)
```
python scripts/deploy/deploy_triton.py --nemo_checkpoint /opt/checkpoints/Llama2-70B-SteerLM-Chat.nemo --model_type="llama" --triton_model_name Llama2-70B-SteerLM-Chat --triton_http_address 0.0.0.0 --triton_port 8000 --num_gpus 2 --max_input_len 3072 --max_output_len 1024 --max_batch_size 1 &
```
9. Once the server is ready (i.e. when you see this messages below), you are ready to launch your client code
```
Started HTTPService at 0.0.0.0:8000
Started GRPCInferenceService at 0.0.0.0:8001
Started Metrics Service at 0.0.0.0:8002
```
```python
from nemo.deploy import NemoQuery
PROMPT_TEMPLATE = """<extra_id_0>System
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
<extra_id_1>User
{prompt}
<extra_id_1>Assistant
<extra_id_2>quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4
"""
question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)
nq = NemoQuery(url="localhost:8000", model_name="Llama2-70B-SteerLM-Chat")
output = nq.query_llm(prompts=[prompt], max_output_token=15, top_k=1, top_p=0.0, temperature=1.0)
#this container currently does not support stop words but you do something like this as workaround
output = output[0][0].split("\n<extra_id_1>")[0]
print(output)
```
10. If you would support multi-turn conversations or adjust attribute values at inference time, here is some guidance:
Default template for Single Turn
```
<extra_id_0>System
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
<extra_id_1>User
{prompt 1}
<extra_id_1>Assistant
<extra_id_2>quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4
```
Default template for Multi-Turn
```
<extra_id_0>System
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
<extra_id_1>User
{prompt 1}
<extra_id_1>Assistant
<extra_id_2>quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4
{response 1}
<extra_id_1>User
{prompt 2}
<extra_id_1>Assistant
<extra_id_2>quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4
```
Each of the attributes (e.g. creativity, complexity and verbosity) can receive integer values in the range [0, 1, 2, 3, 4].
## Evaluation
MT-bench
|Category | score|
|---|---|
|total| 7.54|
|writing | 7.92|
|roleplay | 8.9|
|extraction | 8.98|
|stem | 9.2|
|humanities | 9.7|
|reasoning | 6.35|
|math | 4.55|
|coding | 4.75|
## Intended use
The SteerLM-Llama2-70B model is for users who want to customize a model’s response during inference.
Ethical use: Technology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the Llama 2 Community License Agreement.
## Limitations
The model was trained on the data that contains toxic language and societal biases originally crawled from the Internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
We recommend deploying the model with [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) to mitigate these potential issues.
## Contact
E-Mail: [Zhilin Wang](mailto:zhilinw@nvidia.com)
## Citation
If you find this model useful, please cite the following works
```bibtex
@misc{wang2023helpsteer,
title={HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM},
author={Zhilin Wang and Yi Dong and Jiaqi Zeng and Virginia Adams and Makesh Narsimhan Sreedhar and Daniel Egert and Olivier Delalleau and Jane Polak Scowcroft and Neel Kant and Aidan Swope and Oleksii Kuchaiev},
year={2023},
eprint={2311.09528},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{dong2023steerlm,
title={SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF},
author={Yi Dong and Zhilin Wang and Makesh Narsimhan Sreedhar and Xianchao Wu and Oleksii Kuchaiev},
year={2023},
eprint={2310.05344},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| null | nemo | text-generation | null | null | null | null | null | null | null | null | null | nvidia/Llama2-70B-SteerLM-Chat | [
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KXie/xieke80 | KXie | 2023-11-29T05:07:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T05:07:36Z | 2023-11-23T02:24:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | KXie/xieke80 | [
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PrunaAI/CompVis-stable-diffusion-v1-4-smashed | PrunaAI | 2023-11-29T15:47:14Z | 0 | 0 | null | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:47:14Z | 2023-11-23T03:25:53.000Z | null | null | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
# Simply make AI models cheaper, smaller, faster, and greener!
## Results
## Setup
You can run the smashed model on a A100 GPU by:
1. Installing and importing the `pruna-engine` (version 0.2.7) package. Use `pip install pruna --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com` for installation. See [Pypi](https://pypi.org/project/pruna-engine/) for detailed on the package.
2. Downloading the model files at `model_path`. This can be done using huggingface with this repository name or with manual downloading.
3. Loading the model
4. Running the model.
You can achieve this by running the following code:
```python
from transformers.utils.hub import cached_file
from pruna_engine.PrunaModel import PrunaModel # Step (1): install and import `pruna-engine` package.
...
model_path = cached_file("PrunaAI/CompVis-stable-diffusion-v1-4-smashed", "model") # Step (2): download the model files at `model_path`.
smashed_model = PrunaModel.load_model(model_path) # Step (3): load the model.
y = smashed_model(prompt="a silly prune with a face in high definition", image_height=512, image_width=512)[0] # Step (4): run the model.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | null | pruna-engine | null | null | null | null | null | null | null | null | null | null | PrunaAI/CompVis-stable-diffusion-v1-4-smashed | [
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iamshnoo/yi-alpaca-2-6b-hindi | iamshnoo | 2023-11-29T05:48:39Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T05:48:39Z | 2023-11-23T05:02:41.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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iamshnoo/yi-alpaca-2-34b-persian | iamshnoo | 2023-11-29T17:39:48Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T17:39:48Z | 2023-11-23T05:08:20.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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iamshnoo/yi-alpaca-2-34b-greek | iamshnoo | 2023-11-29T14:24:47Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T14:24:47Z | 2023-11-23T05:08:28.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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awaisakhtar/yolov8_finetunned | awaisakhtar | 2023-11-29T06:02:35Z | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
] | 2023-11-29T06:02:35Z | 2023-11-23T08:48:16.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | awaisakhtar/yolov8_finetunned | [
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Silvelter/STHMIAZU1024 | Silvelter | 2023-11-29T14:04:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:04:13Z | 2023-11-23T12:38:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Silvelter/STHMIAZU1024 | [
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PrunaAI/stabilityai-stable-diffusion-2-1-base-smashed | PrunaAI | 2023-11-29T15:44:46Z | 0 | 0 | null | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:44:46Z | 2023-11-23T17:42:23.000Z | null | null | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
# Simply make AI models cheaper, smaller, faster, and greener!
## Results
## Setup
You can run the smashed model on a A100 GPU by:
1. Installing and importing the `pruna-engine` (version 0.2.7) package. Use `pip install pruna --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com` for installation. See [Pypi](https://pypi.org/project/pruna-engine/) for detailed on the package.
2. Downloading the model files at `model_path`. This can be done using huggingface with this repository name or with manual downloading.
3. Loading the model
4. Running the model.
You can achieve this by running the following code:
```python
from transformers.utils.hub import cached_file
from pruna_engine.PrunaModel import PrunaModel # Step (1): install and import `pruna-engine` package.
...
model_path = cached_file("PrunaAI/stabilityai-stable-diffusion-2-1-base-smashed", "model") # Step (2): download the model files at `model_path`.
smashed_model = PrunaModel.load_model(model_path) # Step (3): load the model.
y = smashed_model(prompt="a silly prune with a face in high definition", image_height=768, image_width=768)[0] # Step (4): run the model.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | null | pruna-engine | null | null | null | null | null | null | null | null | null | null | PrunaAI/stabilityai-stable-diffusion-2-1-base-smashed | [
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] |
likyoo/BAN | likyoo | 2023-11-29T07:41:49Z | 0 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-11-29T07:41:49Z | 2023-11-24T00:46:46.000Z | null | null | ---
license: cc-by-4.0
---
# Bi-Temporal Adapter Network for Remote Sensing Image Change Detection
Code: https://github.com/likyoo/BAN | null | null | null | null | null | null | null | null | null | null | null | null | likyoo/BAN | [
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PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-smashed | PrunaAI | 2023-11-29T15:46:26Z | 0 | 0 | null | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:46:26Z | 2023-11-24T03:53:37.000Z | null | null | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
# Simply make AI models cheaper, smaller, faster, and greener!
## Results
## Setup
You can run the smashed model on a A100 GPU by:
1. Installing and importing the `pruna-engine` (version 0.2.6) package. Use `pip install pruna --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com` for installation. See [Pypi](https://pypi.org/project/pruna-engine/) for detailed on the package.
2. Downloading the model files at `model_path`. This can be done using huggingface with this repository name or with manual downloading.
3. Loading the model
4. Running the model.
You can achieve this by running the following code:
```python
from transformers.utils.hub import cached_file
from pruna_engine.PrunaModel import PrunaModel # Step (1): install and import `pruna-engine` package.
...
model_path = cached_file("PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-smashed", "model") # Step (2): download the model files at `model_path`.
smashed_model = PrunaModel.load_model(model_path) # Step (3): load the model.
y = smashed_model(prompt="a silly prune with a face in high definition", image_height=1024, image_width=1024)[0] # Step (4): run the model.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | null | pruna-engine | null | null | null | null | null | null | null | null | null | null | PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-smashed | [
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sinjuku/models | sinjuku | 2023-11-29T06:28:47Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T06:28:47Z | 2023-11-24T06:44:05.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sinjuku/models | [
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PrunaAI/stabilityai-stable-diffusion-2-1-smashed | PrunaAI | 2023-11-29T15:46:46Z | 0 | 0 | null | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:46:46Z | 2023-11-24T10:07:37.000Z | null | null | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
# Simply make AI models cheaper, smaller, faster, and greener!
## Results
## Setup
You can run the smashed model on a A100 GPU by:
1. Installing and importing the `pruna-engine` (version 0.2.6) package. Use `pip install pruna --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com` for installation. See [Pypi](https://pypi.org/project/pruna-engine/) for detailed on the package.
2. Downloading the model files at `model_path`. This can be done using huggingface with this repository name or with manual downloading.
3. Loading the model
4. Running the model.
You can achieve this by running the following code:
```python
from transformers.utils.hub import cached_file
from pruna_engine.PrunaModel import PrunaModel # Step (1): install and import `pruna-engine` package.
...
model_path = cached_file("PrunaAI/stabilityai-stable-diffusion-2-1-smashed", "model") # Step (2): download the model files at `model_path`.
smashed_model = PrunaModel.load_model(model_path) # Step (3): load the model.
y = smashed_model(prompt="a silly prune with a face in high definition", image_height=768, image_width=768)[0] # Step (4): run the model.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | null | pruna-engine | null | null | null | null | null | null | null | null | null | null | PrunaAI/stabilityai-stable-diffusion-2-1-smashed | [
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mojemai/ddpm-butterflies-16 | mojemai | 2023-11-29T10:57:19Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T10:57:19Z | 2023-11-24T10:48:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mojemai/ddpm-butterflies-16 | [
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Subhadeep/Hindi_English_Finetune_Single_Model_V2 | Subhadeep | 2023-11-29T09:41:12Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:41:12Z | 2023-11-24T12:48:37.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Subhadeep/Hindi_English_Finetune_Single_Model_V2 | [
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chen70335/longformer-base-4096-with-sampling | chen70335 | 2023-11-30T00:18:06Z | 0 | 0 | null | [
"transformers",
"pytorch",
"longformer",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:18:06Z | 2023-11-25T03:10:21.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | chen70335/longformer-base-4096-with-sampling | [
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EarthnDusk/DecemberBackMixes | EarthnDusk | 2023-11-29T11:27:32Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T11:27:32Z | 2023-11-25T03:11:13.000Z | null | null | ---
license: creativeml-openrail-m
---
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hoang-quoc-trung/MathNougat-small | hoang-quoc-trung | 2023-11-29T14:40:25Z | 0 | 0 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:40:25Z | 2023-11-25T05:41:05.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | hoang-quoc-trung/MathNougat-small | [
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lennych/SINTEIX | lennych | 2023-11-29T22:25:53Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T22:25:53Z | 2023-11-25T08:59:20.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | lennych/SINTEIX | [
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zuazo/whisper-large-gl | zuazo | 2023-11-29T19:56:28Z | 0 | 0 | null | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"gl",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-large",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:56:28Z | 2023-11-25T10:24:27.000Z | null | null | ---
language:
- gl
license: apache-2.0
base_model: openai/whisper-large
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large Galician
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_13_0 gl
type: mozilla-foundation/common_voice_13_0
config: gl
split: test
args: gl
metrics:
- name: Wer
type: wer
value: 6.939845474613686
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large Galician
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_13_0 gl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3605
- Wer: 6.9398
## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0126 | 4.01 | 1000 | 0.2128 | 8.3558 |
| 0.0032 | 9.01 | 2000 | 0.2262 | 6.9416 |
| 0.0022 | 14.01 | 3000 | 0.2528 | 7.1123 |
| 0.0025 | 19.01 | 4000 | 0.2643 | 7.3641 |
| 0.0015 | 24.01 | 5000 | 0.2596 | 7.3365 |
| 0.0014 | 29.01 | 6000 | 0.2723 | 7.6366 |
| 0.0008 | 34.01 | 7000 | 0.2778 | 7.6090 |
| 0.0003 | 39.01 | 8000 | 0.2880 | 7.2261 |
| 0.0004 | 44.01 | 9000 | 0.2920 | 7.6745 |
| 0.0001 | 49.01 | 10000 | 0.2854 | 7.4089 |
| 0.0 | 54.01 | 11000 | 0.3027 | 7.4365 |
| 0.0 | 59.01 | 12000 | 0.3159 | 7.4055 |
| 0.0 | 64.01 | 13000 | 0.3242 | 7.3693 |
| 0.0 | 69.01 | 14000 | 0.3312 | 7.3072 |
| 0.0 | 74.01 | 15000 | 0.3379 | 7.0226 |
| 0.0 | 79.01 | 16000 | 0.3442 | 7.0019 |
| 0.0 | 84.01 | 17000 | 0.3500 | 6.9933 |
| 0.0 | 89.01 | 18000 | 0.3550 | 6.9605 |
| 0.0 | 94.01 | 19000 | 0.3589 | 6.9467 |
| 0.0 | 99.01 | 20000 | 0.3605 | 6.9398 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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neukg/TechGPT-2.0-alpaca-hf | neukg | 2023-11-29T14:27:29Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2304.08177",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:27:29Z | 2023-11-25T12:16:22.000Z | null | null | # TechGPT 2.0: Technology-Oriented Generative Pretrained Transformer 2.0
Demo: [TechGPT-neukg](http://techgpt.neukg.com) <br>
HuggingFace🤗: [neukg/TechGPT-7B](https://huggingface.co/neukg)
<div align="center">
[](https://github.com/neukg/TechGPT/blob/main/LICENSE)
[](https://huggingface.co/neukg)
</div>
## 引言
着大模型时代的到来，大模型与知识图谱融合的工作日益成为当前研究的热点。为了对这项工作提供研究基础，东北大学知识图谱研究组于2023年6月发布了[TechGPT-1.0](https://github.com/neukg/TechGPT)大模型。在此基础上，经过几个月的工作，东北大学知识图谱研究组发布 **TechGPT-2.0** 大模型。在继承TechGPT-1.0全部功能的基础上，TechGPT-2.0 **对其多项功能进行了增强**，并**扩充了一些新功能**。
## 内容导引
| 章节 | 描述 |
|----------------------|-----------------------------|
| [💁🏻♂️模型简介](#模型简介) | 简要介绍本项目 TechGPT 2.0 模型的技术特点 |
| [📝模型亮点](#模型亮点) | 介绍了 TechGPT 2.0 大模型的独特之处 |
| [⏬模型下载与体验](#模型下载与体验) | TechGPT 2.0 大模型下载地址与Demo体验 |
| [💻环境部署](#推理与部署) | 介绍了如何使用个人环境部署并体验大模型 |
| [💯系统效果](#系统效果) | 展示了模型在部分任务上的效果 |
## 模型简介
TechGPT-2.0 为 TechGPT-1.0 基础上的改进版本，此次共发布两个7B版本的模型分别为**TechGPT2-Alpaca**、**TechGPT2-Atom**。
TechGPT-2.0 较 TechGPT-1.0 新加了许多领域知识。除了 TechGPT-1.0 所具备的计算机科学、材料、机械、冶金、金融和航空航天等十余种垂直专业领域能力，TechGPT-2.0 还在**医学、法律领域**展现出优秀的能力，并扩充了**地理地区、运输、组织、作品、生物、自然科学、天文对象、建筑**等领域能力。除此之外，我们的工作还对**幻觉、不可回答问题、长文本**等任务进行了研究。
**东北大学知识图谱研究组**与**华为沈阳人工智能计算中心**合作，使用**华为昇腾服务器**（具体为4机*8卡-32G 910A）进行**全量微调**。
## 模型亮点
TechGPT-2.0 在继承了 TechGPT-1.0 的能力上进行了重要的改进，其中最显著的优化是**引入了命名实体识别的领域数据**，并且**使模型具备了对嵌套实体的抽取能力**。以下为TechGPT-2.0大模型的亮点介绍：
- 首先，TechGPT-2.0 在**医学领域**的表达和理解方面取得了显著的提升。模型能够对疾病、药物、专业术语等实体的进行更准确识别，这表明模型在处理医学文本时能够更全面地理解上下文信息。除此之外，TechGPT-2.0 还能够理解医学文本中的复杂关系、疾病诊断、治疗方案等内容。这种全面的医学分析能力使得模型可以用于协助医生阅读医学文献、提供患者诊断建议等应用场景，从而提高医学领域的信息处理效率和准确性。
- 其次，TechGPT-2.0 能够理解和解释法律文本，包括法规、合同和案例法等。这使得模型在**法律领域**的应用更为广泛，并可以用于解决自动化合同审查、法规遵循检查等任务。模型通过学习法律用语和结构，能够更准确地捕捉文本中的法律关系和条款，为用户提供更有深度和专业性的法律分析。
- 再次，TechGPT-2.0 的另一个重要特性是能够**抽取嵌套实体**。这意味着模型可以更灵活地处理实体之间的复杂关系，深入挖掘文本中的层次结构，提高了对复杂文本的理解和生成能力。例如，在医学文献中，可能存在嵌套的实体关系，如疾病的亚型、药物的剂量信息等，TechGPT-2.0 能够更好地捕捉这些信息，并在生成回应时更准确地反映上下文的语境。
- 另外，TechGPT-2.0 还在**幻觉、长文本以及与人类价值观对齐方面进行了强化**。模型通过对话和理解上下文，能够更好地理解人类的感受和价值观，并在回应中考虑这些因素。此外，TechGPT-2.0能够支持最大长度12K的文本输入。这使得模型能够更好地与人类用户进行交互，更好地满足用户的需求和期望，进一步提升了人机交互的质量和用户体验。
- 最后，TechGPT-2.0 的训练使用**华为昇腾910A服务器**，并基于**华为Mindspore框架**以及Mindformer套件进行开发，采用分布式训练方案，机器数量为4机*8卡（32G）。目前TechGPT-2.0推理部署在使用**NVIDIA**的显卡并使用float16的情况下显存仅需**15G**，INT8量化下显存占用**8G**，INT4量化下显存仅需**5G**；NAVID 4090单机单卡、或昇腾910A单机单卡就可完成推理部署。
总体而言，TechGPT-2.0 在继承了 TechGPT-1.0 的强大自然语言处理能力的同时，**通过增加多领域、多任务的数据，展现出了嵌套实体的抽取、幻觉回答、回答不可回答问题和回答长文本问题的能力**。这些改进使得模型更适用于广泛的应用场景，为用户提供了更准确、更深入的信息处理和生成能力。
## 模型下载与体验
### 下载地址
| 模型名称 | 类型 | 训练方式 | 大小 | 下载地址 |
|:----------------------|:----:|:------:|:-----:|:------------------------------------------------------------------:|
| TechGPT-1.0 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-7B) |
| TechGPT-2.0-Alpaca 🆕 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-alpaca-hf) |
| TechGPT-2.0-Atom 🆕 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-atom-hf) |
| TechGPT-2.0-QLora 🆕 | 指令模型 | Lora微调 | 3 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-QLora-hf) |
### 模型说明
**TechGPT2** 在不同规模且经过扩充后的**TechKG**大规模的中文学术语料支持下训练完成。
- **TechGPT2-Alpaca** 使用HFL发布的Chinese-Alpaca-2-7B模型，Chinese-Alpaca-2-7B模型是在Chinese-LLaMA-2-7B的基础上进一步通过指令精调获得。Chinese-LLaMA-2-7B为基座模型，是在LLaMA-2的基础上进行中文增量训练获得。
- **TechGPT2-Atom** 使用Llama中文社区联合AtomEcho（原子回声）共同发布的Atom-7B-chat，并在此基础上进行全量微调后获得。
- 此外，我们使用**QLora线性插值法**微调的长文本模型的Lora权重也已经发布在huggingface上，请自行下载合并。
我们TechGPT-2的两个**7B**版本的模型已经在Hugging Face和GitHub上开源，使用后续在此模型基础上的改进，也将开源到相同账号，欢迎各位使用并提出宝贵的意见。
### 模型体验
我们目前对外提供TechGPT-1.0与TechGPT2-Atom版本的在线服务：http://techgpt.neukg.com/
作为一个学术组织，我们无法同时提供三个模型的在线服务，因此建议大家后续通过开源权重自行体验，共同创建更好的中文大模型开源环境。
## 环境部署
### 在华为昇腾 910 NPU 服务器上的环境要求
- 硬件：Ascend 910A/910B
- Python：3.9
- MindSpore：2.1.1
- CANN: 6.3.0 RC2
- MindFormers版本：dev
- 7b 推理可在单机单卡上完成部署
1. 在mindformers环境下执行推理部署时，需要使用ckpt权重；如果没有ckpt权重，则在mindformers目录下需要运行如下[转换脚本](https://github.com/neukg/TechGPT-2.0/blob/main/ckpt_weight_convert/convert_weight.py)，将huggingface权重转为ckpt权重，才能使用NPU进行推理：
``` shell
python mindformers/models/llama/convert_weight.py \
--torch_ckpt_dir TORCH_CKPT_DIR \
--mindspore_ckpt_path {path}/MS_CKPT_NAME
```
2. 初次在mindformers环境下执行推理时，会在```mindspore_inference.py```的同级目录下生成```checkpoint_download```文件夹，其中包含了推理所需的```yaml```配置文件和```tokenizer.model```词表等，需要将词表换成该项目huggingface上的对应词表，并将配置文件替换为```mindspore_inference```目录下的```yaml```文件。
### 在 GPU 服务器上的环境要求
请注意TechGPT2-Alpaca和TechGPT2-Atom模型在**训练**和**推理**阶段所使用的prompt格式是不同。
**TechGPT2-Alpaca 使用的prompt格式为：**
``` python
<s> A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [INST] <<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。请你提供专业、有逻辑、内容真实、有价值的详细回复。\n<</SYS>>\n\n{instruction} [/INST] ASSISTANT:
```
**TechGPT2-Atom 使用的prompt格式为：**
``` python
<s>Human: {instruction} \n</s><s>Assistant:
```
请在使用TechGPT之前保证你已经安装好`transfomrers`和`torch`：
```shell
pip install transformers
pip install torch
```
- 注意，必须保证安装的 `transformers` 的版本中已经有 `LlamaForCausalLM` 。<br>
- Note that you must ensure that the installed version of `transformers` already has `LlamaForCausalLM`.
[TechGPT2-Alpca Example:](https://github.com/neukg/TechGPT-2.0/blob/main/pytorch_inference/techgpt2-alpaca_infer.py)
``` python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
import torch
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。请你提供专业、有逻辑、内容真实、有价值的详细回复。"""
TEMPLATE = (
"<s> A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [INST] <<SYS>>\n"
"{system_prompt}\n"
"<</SYS>>\n\n"
"{instruction} [/INST] ASSISTANT: "
)
def generate_prompt(instruction):
return TEMPLATE.format_map({'instruction': instruction, 'system_prompt': DEFAULT_SYSTEM_PROMPT})
ckpt_path = './LLM/TechGPT2-Alpaca-hf/'
load_type = torch.float16
device = torch.device(1)
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
tokenizer.pad_token_id = 0
tokenizer.bos_token_id = 1
tokenizer.eos_token_id = 2
tokenizer.padding_side = "left"
model_config = AutoConfig.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
model.to(device)
model.eval()
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
max_new_tokens=128,
min_new_tokens=10,
do_sample=True,
)
example = '请把下列标题扩写成摘要, 不少于100字: 基于视觉语言多模态的实体关系联合抽取的研究。'
instruction = generate_prompt(instruction=example)
instruction = tokenizer(instruction, return_tensors="pt")
input_ids = instruction["input_ids"].to(device)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1.2,
)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
print(output)
```
[TechGPT2-Atom Example:](https://github.com/neukg/TechGPT-2.0/blob/main/pytorch_inference/techgpt2-atom_infer.py)
``` python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
import torch
TEMPLATE = (
"<s>Human: "
"{instruction} \n</s><s>Assistant: "
)
def generate_prompt(instruction):
return TEMPLATE.format_map({'instruction': instruction})
ckpt_path = './LLM/TechGPT2-Atom-hf/'
load_type = torch.float16
device = torch.device(1)
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
tokenizer.pad_token_id = 2
tokenizer.bos_token_id = 1
tokenizer.eos_token_id = 2
tokenizer.padding_side = "left"
model_config = AutoConfig.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
model.to(device)
model.eval()
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
bos_token_id=1,
eos_token_id=2,
pad_token_id=2,
max_new_tokens=128,
min_new_tokens=20,
do_sample=True,
)
example = '抽取出下面文本的实体和实体类型：《女人树》，国产电视剧，由导演田迪执导，根据作家子页的原著改编，故事从1947年开始，跨越了解放战争和建国初期两大历史时期，展现了战斗在隐形战线上的人民英雄是如何不惧怕任何危险，不计较个人牺牲，甚至不顾人民内部的误解和生死裁决，都不暴露个人真实身份，至死不渝，与敌人周旋到底的英雄故事。'
instruction = generate_prompt(instruction=example)
instruction = tokenizer(instruction, return_tensors="pt")
input_ids = instruction["input_ids"].to(device)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1.2,
)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
print(output)
```
## 系统效果
TechGPT-2.0 在保留了 TechGPT-1.0 模型的通用能力、实体识别能力以及三元组抽取能力的基础上，能够完成**更为复杂的嵌套实体识别**，并在**医学、法律领域**展现出优秀的能力，此外对**幻觉、不可回答问题以及长文本问题**有着较好的解决能力。
下面展示了TechGPT-2.0 的几个新增能力的样例。
1. **医学NER**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>这句话中是否有实体？如果有，请列举出来。<br/>依据主要病变部位分为下列数型：脊髓型最常见，为脊髓前角细胞受损所致，表现为分布不对称、不规则的弛缓性肌肉瘫痪，四肢多见，下肢尤甚。 </td> <td>实体名:先天畸形, 实体类型:疾病<br/>实体名:发育障碍, 实体类型:临床表现<br/>实体名:上、下呼吸道梗阻, 实体类型:疾病<br/>实体名:膈疝, 实体类型:疾病<br/>实体名:肺部受压迫, 实体类型:临床表现<br/>实体名:肺, 实体类型:身体<br/>实体名:呼吸衰竭, 实体类型:疾病
</td></tr>
<tr><td>毒蕈类以1∶5000高锰酸钾或活性炭悬液反复洗胃，硫酸镁导泻；用二巯基丙磺酸钠每次5mg/kg，每天2～3次，肌内注射；阿托品0.05mg/kg，肌内注射，每15分钟1次，直至阿托品化后减量。<br/>在这句话中，是否可以找到任何实体名称？ </td> <td>实体名:毒蕈, 实体类型:微生物类<br/>实体名:高锰酸钾, 实体类型:药物<br/>实体名:活性炭, 实体类型:药物<br/>实体名:硫酸镁, 实体类型:药物<br/>实体名:导泻, 实体类型:医疗程序<br/>实体名:二巯基丙磺酸钠, 实体类型:药物<br/>实体名:肌内注射, 实体类型:医疗程序<br/>实体名:阿托品, 实体类型:药物<br/>实体名:肌内注射, 实体类型:医疗程序<br/>实体名:阿托品, 实体类型:药物
</td></tr>
</table>
2. **嵌套NER**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>一位原招商银行信用卡中心负责人告诉记者。请抽取出其中的命名实体。 </td> <td>实体名: 招商银行，实体类型：企业<br/>实体名: 中国，实体类型：地点<br/>实体名: 招商银行信用卡中心， 实体类型：机构<br/>实体名: 银行，实体类型：企业
</td></tr>
<tr><td>这句话中是否有实体？如果有，请列举出来。<br/>依据主要病变部位分为下列数型：脊髓型最常见，为脊髓前角细胞受损所致，表现为分布不对称、不规则的弛缓性肌肉瘫痪，四肢多见，下肢尤甚。</td> <td>实体名:脊髓, 实体类型:身体<br/>实体名:脊髓前角细胞, 实体类型:身体<br/>实体名:脊髓前角细胞受损, 实体类型:临床表现<br/>实体名:分布不对称、不规则的弛缓性肌肉瘫痪, 实体类型:疾病<br/>实体名:肢体, 实体类型:身体<br/>实体名:下肢, 实体类型:身体
</td></tr>
<tr><td>这句话是否包含任何命名实体？如果有，请将其列举出来。<br/>在弥漫性肺损伤和肺泡萎陷时，要获得比较好的氧合，需要在机械通气时保持压力的平台，以使肺泡持续扩张，气体在不同时间常数的肺泡单位内移动，达到压力平台，使得到较好的肺泡通气/灌流，减少肺内分流。</td> <td>实体名:弥漫性肺损伤, 实体类型:疾病<br/>实体名:肺泡萎陷, 实体类型:临床表现<br/>实体名:肺泡, 实体类型:身体<br/>实体名:机械通气, 实体类型:医疗程序<br/>实体名:压力的平台, 实体类型:医疗设备<br/>实体名:肺泡持续扩张, 实体类型:临床表现<br/>实体名:气体不同时间常数, 实体类型:医学检验项目<br/>实体名:肺泡通气/灌流, 实体类型:医疗程序<br/>实体名:肺内分流, 实体类型:临床表现<br/>实体名:肺内, 实体类型:身体
</td></tr>
<tr><td>出血因凝血因子大量被消耗、血小板减少及继发纤溶亢进，发生出血。<br/>请列示这段文本中的所有特定实体。</td> <td>实体名:出血, 实体类型:临床表现<br/>实体名:凝血因子, 实体类型:身体<br/>实体名:凝血因子大量被消耗, 实体类型:临床表现<br/>实体名:血小板, 实体类型:身体<br/>实体名:血小板减少, 实体类型:临床表现<br/>实体名:继发纤溶亢进, 实体类型:疾病<br/>实体名:出血, 实体类型:临床表现
</td></tr>
</table>
3. **泛化NER（根据背景知识泛化的NER）**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>下面句子中的实体有哪些《女人树》，国产电视剧，由导演田迪执导，根据作家子页的原著改编，故事从1947年开始，跨越了解放战争和建国初期两大历史时期，展现了战斗在隐形战线上的人民英雄是如何不惧怕任何危险，不计较个人牺牲，甚至不顾人民内部的误解和生死裁决，都不暴露个人真实身份，至死不渝，与敌人周旋到底的英雄故事。 </td> <td>实体名: 女人树，实体类型：影视作品<br/>实体名: 田迪，实体类型：人物<br/>实体名: 中国，实体类型：国家<br/>实体名: 女人树，实体类型：图书作品<br/>实体名: 子页，实体类型：人物
</td></tr>
<tr><td>俄德两国4架战机全部逼近芬兰空中边界，芬兰空军歼击机迅速起飞干预，驱逐俄德两国歼击机，并将其“护送”。请抽取出其中的命名实体。 </td> <td>实体名: 俄罗斯，实体类型：国家<br/>实体名: 德国，实体类型：国家<br/>实体名: 芬兰，实体类型：地点<br/>实体名: 芬兰空军，实体类型：机构/人物<br/>实体名: 芬兰，实体类型：国家
</td></tr>
</table>
4. **法律案件梳理**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>苏国平签订的劳动合同期限是什么？<br/>经审理查明,原告苏国平于1979年4月顶替父亲进入常州东方印染厂(国有企业)工作,1996年6月与常州东方印染厂与华源集团合资成立的常州华昌染工有限公司签订劳动合同,合同期限为1996年6月1日至1997年5月31日。后双方续签,最后一份劳动合同期限为1998年5月31日至2001年5月31日。1999年8月30日,常州华昌染工有限公司作出华昌(1999)3号《关于苏国平开除出公司的决定》,该决定载明“苏国平原系华昌染工有限公司前道主管……是公司多年培养的技术骨干,于99年8月初为了个人利益置公司利益而不顾,未办任何手续不告而别,直接为其他印染厂提供技术服务,使本公司利益受到严重伤害……经公司经理会研究决定将苏国平开除出公司,并要求其负经济赔偿责任”,该决定还载明“抄送:市仲裁处、市纺工局、市郊区劳服公司、本人”。1999年9月,常州华昌染工有限公司由常州东方印染厂托管,东方印染厂劳资经办人员在1999年11月办理解除劳动合同手续时,在解除原因一栏填写了“除名”。苏国平的社保参保缴费证明显示其1999年8月前在常州华昌染工有限公司参保、1999年10月起改至常州市金田纺织印染厂,至2019年2月累计缴费31年1月。2019年2月20日,常州视音电子科技有限公司为原告申办企业职工基本养老保险待遇申领手续,被告于2月25日作出本案退休待遇审核表。原告不服,诉至本院。庭审中,各方当事人围绕案涉开除决定是否合法有效各执己见。</td> <td>1996年6月1日至1997年5月31日;1998年5月31日至2001年5月31日
</td></tr>
<tr><td>承包方何时进行行贿？<br/>经审理查明:2012年7月,被告人陈x1以不具备承建资质的衡南县向阳镇水口基建队的名义承包了国有事业单位衡南县教师进修学校学员宿舍改造土建项目工程。为获取不正当利益,被告人陈x1于2012年9月底至2013年底分三次送给该校校长、党支部书记程x9好处费共计6万元。具体犯罪事实如下:1、2012年9月份的一天,被告人陈x1为感谢程x9将衡南县进修学校学员宿舍楼改造改建工程交给其做,争取程x9对其做工程给予关照,事后多安排工程给其做,在程x9的办公室送给程x91万元现金,程x9予以收受。2、2012年底的一天,被告人陈x1为承建衡南县进修学校罕舍楼改造的其他后续工程,在衡南县教师进修学校宿舍改造工地上送给程x91万元现金,程x9予以收受,并将后续工程全部交由陈x1承建。3、2013年底的一天,被告人陈x1为感谢程x9在其承建该校工程中给予的关照并争取被告人程x9同意及时尽多拨付工程款,在程x9的办公室送给程4万元现金,程x9予以收受,并尽可能地拨付工程款给陈x1。2014年3月,程x9得知衡南县纪律检查委员会在对其违纪违法行为进行调查后,于3月18日安排其妻子秦某某将从被告人陈x1处收受的6万元贿赂款退还给了陈x1。2014年5月12日,被告人陈x1主动向湖xxxxxxxxx0投案,如实供述了自己的犯罪事实,并上缴了其用于行贿的6万元钱。上述事实有被告人陈x1于衡南县教师进修学校签订的建筑安装工程施工合同、被告人陈x1的常口信息等书证;同案犯程x9的供述及证人秦某某、罗某某等人的证言;被告人陈x1的供述与辩解等证据证实,经庭审质证,被告人陈x1均无异议,足以认定。 </td> <td>2012年9月份<br/>2012年底<br/>2013年底
</td></tr>
<tr><td>案中法院作出过什么民事判决书<br/>经审理查明,原、被告曾7合伙关系,双方因纠纷经本院调解达成(2014)泰兴戴民初字第0746号民事调解书,被告龚x1依调解协议约定,于2015年2月12日将应支付给原告的19万元缴纳至本院。2015年2月12日,被告龚x1向本院提起诉讼,要求案外人窦x6立即给付其货款16万元,并将本案原告列为该案第三人。2015年3月27日,依龚x1的申请,本院作出(2015)泰兴商初字第123号民事裁定书,对原告的19万元(被告缴纳至本院的)进行了保全。2015年4月23日,本院作出(2015)泰兴商初字第123号民事判决书,判决:1、窦x6支付龚x1、兴化市星权化工产品经营部31003.4元;2、驳回龚x1、兴化市星权化工产品经营部的其他诉讼请求。后因龚x1不服,上诉至泰州市中级人民法院,该院于2016年1月5日作出(2015)泰中商四终字第73号民事判决:驳回上诉,维持原判。2016年2月5日,本院解除了对原告财产的保全,原告亦于当日取得了19万元。另查明,涉案的19万元系被告应支付给原告的履行款,由被告缴纳至兴化市财政局在江苏吴江农村商业银行股份有限公司开设的账户内。2015年3月27日,中国人民银行同期贷款利率为年利率5.35%,江苏吴江农村商业银行同期活期存款利率为年利率0.35%。以上事实有原告提供的民事调解书、民事判决书、民事裁定书、被告提供的现金解款单及本院庭审笔录在卷佐证。 </td> <td>（2015）泰兴商初字第123号民事判决书<br/>（2015）泰中商四终字第73号民事判决
</td></tr>
</table>
5. **法律咨询**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>夫妻双方已经离婚，有一个儿子，因为丈夫犯了错，老人明确写下了与丈夫断绝关系，财产由另外两个子女平分，多年来妻子从未过问大家庭一次，老人生病也连一句问候都没有，现在老人走了，妻子有权利争夺财产吗？</td> <td>没有权利。根据你的描述，老人的遗嘱是明确的将遗产留给了其他兄弟姐妹，而你并没有继承权；同时你作为丈夫的前妻和儿子的母亲也没有任何的法定继承人资格来分割遗产。因此，你没有权利去争抢这个遗产份额。
</td> </tr>
<tr><td>你好，请问在工地干活左手手腕筋断一根和断血管一根能赔多少钱？ </td> <td>工伤赔偿项目包括：医疗费、住院伙食补助费、停工留薪期工资、护理费、交通费等。具体赔偿金额需要根据实际情况来确定的。建议您先申请劳动能力鉴定，确定伤残等级后按照伤残等级计算赔偿数额。
</td> </tr>
<tr><td>无证醉酒驾驶摩托车，喝了一杯白酒，怎么处罚？ </td> <td>饮酒后驾车属于危险驾驶行为。根据《中华人民共和国道路交通安全法》的规定：未取得机动车驾驶证、机动车驾驶证被吊销或者机动车驾驶证被暂扣期间驾驶机动车的处二百元以上二千元以下罚款，可以并处十五日以下拘留。<br/>同时，如果造成事故构成犯罪的，还要承担刑事责任。具体来说，酒后驾驶情节严重的，可能会面临拘役的处罚；而醉驾致人死亡或重伤的，则可能会被判处三年以上七年以下的有期徒刑。因此，建议您不要酒驾，以免给自己和他人带来不必要的伤害和损失。
</td> </tr>
</table>
6. **医学咨询**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>年10岁近两天再次出现干呕恶心呕吐腹痛，喝了止泻药但情况没太大变动，应该如何治疗，如何吃晚饭？</td> <td>您描述的症状主要包括胃部不适症状如恶心、呕吐以及肚子痛，建议立即就医确诊病因并采取合适的治疗方法。以下是一些可能帮助您的方法和建议：<br/>* 首先应该考虑是否是因为最近吃了什么不健康的食物或饮料导致消化不良所引起。如果是这样，可以避免这些食物以促进消化吸收。此外，可以考虑喝一杯温水以刺激肠胃运动，或者使用小苏打来缓解酸碱失衡问题（请咨询医生）。<br/>* 如果以上方案无效或是其他原因引起的疼痛等病症持续存在并且影响日常生活时间较长的话最好去医院检查一下是否存在某种疾病感染，进行相应的处理方式会因具体病情而异。同时需要评估治疗费用负担的能力状况，以免造成经济损失。<br/>* 在生病期间饮食选择上需要注意营养均衡的选择多样化多样的食品组合，避免过量或不当搭配的情况产生不良反应。例如食用易腐烂变质食物要加强注意；辛辣刺激性食物需减少甚至禁食等等。
</td> </tr>
<tr><td>邻居家的女宝宝才1岁，近几天，孩子的妈妈说孩子耳朵有点疼，察觉到，偶尔还会有黄色的液体流出，同时，食欲也不好，嗓子也不舒服，请问：小孩儿中耳炎耳朵胀痛怎样治效果好。 </td> <td>抗生素药物是目前治疗中耳炎比较常用的，可酌情选。也可以配合一些局部治疗的方法，比如消炎型的滴耳液医治，如孩子耳痛流黄水的症状严重，建议选用一些镇痛药缓解症状，要是伴随发高烧的情况，那么根据孩子的症状使用药物，严重的情况请尽快去医院进行救治，存在耳膜穿孔的情况家长可考虑可孩子手术治疗，期间注意让孩子充分休息，并且多吃许多蔬菜水果。
</td> </tr>
<tr><td>早期胃癌手术四十天了，其它都正常，就是夜里有点虚汗，平时吃稀饭和面条以及鱼汤或骨头汤等，现在可以吃点米饭吗？饮食上吃点什么可以增强营养。 </td> <td>病情分析：您好！根据您的描述来看，您目前情况考虑为术后恢复期的问题指导意见：建议您可以适当进食一些易消化的食物，如粥、面片、馄饨等，同时注意补充蛋白质及维生素C的摄取，以促进伤口愈合，提高免疫力，预防感染的发生。祝您早日康复！
</td> </tr>
</table>
7. **幻觉问题**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>地球上一共装了多少台行星发动机？</td> <td>实际上，地球并没有安装任何的“行星引擎”。这是一个常见的误解或谣言来源之一。以下是一些详细的解释：<br/>首先需要明确的是，我们通常所说的“行星引擎”指的是一种人造卫星上的推进系统，它通过使用燃料和氧化剂来产生推力以使卫星在轨道上运动并维持其运行状态的一种装置。这些推进系统的类型包括化学火箭、核反应堆等。然而，这种类型的设备并不存在于地球的表面或者大气层中。相反地，它们是由人类设计和建造的用于太空探索和研究的工具。因此，我们不能说地球上安装了任何形式的“行星引擎”。<br/>其次，即使将这个问题看作是指地球上存在某种类似于外星文明的先进技术，我们也无法确定该文明是否真的拥有类似星际飞船这样的交通工具以及它们的运作方式是什么样的。这涉及到许多科学和技术难题，如宇宙物理学、生命起源等等，而这些问题的答案目前还没有被完全解决。 因此，关于外星人存在的证据仍然非常有限且难以证明，而所谓的“行星引擎”也只是虚构的概念而已。<br/>综上所述，没有确切的数据表明地球上有安装在地面上的任何“行星引擎”的存在。这只是一个流传已久但无根据的说法。
</td> </tr>
<tr><td>中国最好的烹饪学校是哪里？ </td> <td>没有一个“最好”的烹饪学校，因为每个人对“好”的定义不同。但是以下是一些受欢迎和有影响力的学校：<br/>1. 北京厨艺学院（Beijing Culinary College）- 该校是中国最古老的厨师培训机构之一，成立于1956年。它提供各种课程，包括中餐、西餐、糕点等。该学校的师资力量强大，许多学生毕业后成为知名餐厅的主厨或餐饮企业高管。<br/>2. 上海交通大学附属高级职业技术学院（Shanghai Jiao Tong University Affiliated Vocational Technical School of Advanced Professional Technology and Arts） - 这所院校位于上海，是一所综合性的职业教育机构。该校拥有多个专业领域，其中包括烹饪艺术。该校注重实践教学，为学生提供了丰富的实习机会和实践经验。<br/>3. 广东省粤菜文化研究院（Guangdong Province Guangzhou Chinese Cookery Culture Research Institute）- 这是一所专注于广东菜肴文化的研究机构和培训中心。该校的课程涵盖了从基础到高端的菜肴制作技巧，以及中国传统饮食文化和历史背景等方面的知识。该校还与多家著名酒店合作，为学员提供实际操作的机会。<br/>4. 四川成都川味厨房职业技能培训学校（Chengdu Sichuan Kitchen Skills Training School）- 位于四川成都的一家私立学校，专门教授川菜烹饪技艺。该校的教学方法独特，强调理论与实践相结合，让学生能够快速掌握川菜的制作方法和技巧。此外，该校也开设了其他相关领域的课程，如中式面点、点心等。<br/>以上是一些备受欢迎和有影响的烹饪学校，但选择哪个学校取决于个人兴趣和专业需求。在选择时需要考虑学费、地理位置、师资力量等因素。
</td> </tr>
<tr><td>目前公认的最好的职业是什么？ </td> <td>没有一种职业是“最好”的，因为每个人都有自己的兴趣、技能和目标。然而，以下是一些被认为是最有前途的职业：
<br/>1. 数据科学家/数据分析师 - 随着数字化时代的到来，对数据的需求越来越高，因此数据科学成为了一个非常热门的领域。数据科学家可以帮助企业分析大量数据并做出明智的决策。
<br/>2. 网络安全专家 - 在数字时代，网络攻击变得越来越多样化且复杂。网络安全专业人员需要保护企业和个人免受黑客和其他威胁的攻击。
<br/>3. 医生 - 尽管医疗保健行业面临着许多挑战，但医生仍然是最受欢迎的职业之一。随着人口老龄化和健康意识的提高，人们对医生的需求和期望也在不断增加。
<br/>4. 金融顾问 - 由于金融市场的复杂性和高风险性，金融顾问的需求也日益增长。他们可以为客户提供投资建议和指导，以帮助他们实现财务目标和规划未来。
<br/>5. 开发工程师 - 编程是一项非常有前景的工作，尤其是在技术领域。开发工程师可以在各种行业中工作，包括软件公司、游戏工作室等。
<br/>6. 教师 - 虽然教育行业的竞争激烈，但是教师是一个稳定的职业选择，而且对于那些喜欢与孩子互动的人来说也是一个很好的选择。
<br/>7. 律师 - 作为法律领域的重要角色，律师能够为客户解决问题并提供咨询服务。虽然这个职位的竞争也很大，但它仍然被视为一项很有前途的职业。
<br/>总之，以上这些职业都是目前被广泛认为是最有前途的职业之一。当然，还有很多其他的职业也非常有价值，具体取决于个人的兴趣和能力。
</td> </tr>
</table>
8. **三元组抽取**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>请给出文中的三元组 《善德女王》是由金根洪、朴弘均联合执导，金英贤编剧，高贤贞、李瑶媛、金南佶、严泰雄、朴艺珍、俞承豪主演的一部韩国历史剧。</td> <td><善德女王, 主演, 金南佶>
<br/><善德女王, 导演, 金根洪>
<br/><善德女王, 编剧, 金英贤>
<br/><善德女王, 主演, 李瑶媛>
<br/><善德女王, 主演, 高贤贞>
<br/><善德女王, 主演, 严泰雄>
<br/><善德女王, 主演, 朴艺珍>
<br/><善德女王, 导演, 朴弘均>
<br/><善德女王, 主演, 俞承豪>
</td> </tr>
<tr><td>张开建，男，汉族，山东梁山人，1953年11月出生，1969年12月参加工作，1973年9月加入中国共产党，1979年1月北京大学地质系地震地质专业毕业，大学学历，高级工程师<br/>你能否将文本中所有的实体关系三元组列举出来吗？三元组形式是什么呢？ </td> <td>
<br/><张开建, 民族, 汉族>
<br/><张建, 国籍, 中国>
<br/><张开建, 出生地, 山东梁山>
<br/><张开建, 毕业院校, 北京大学>
<br/><张开建, 出生日期, 1953年11月>
</td> </tr>
<tr><td>请给出下文中的所有关系三元组，三元组形式为<主语，关系，宾语><br/>文本：今天，才知道了国歌的名字叫《义勇军进行曲》，知道了国歌的词曲作者田汉和聂耳，还知道了国歌象征着中华民族永不屈服的抗争精神</td> <td>
<br/><义勇军进行曲, 作词, 田汉>
<br/><义勇军进行曲, 作曲, 聂耳>
</td> </tr>
</table>
9. **不可回答问题**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>我想知道2045年美国总统是谁？</td> <td>无法确定，因为到那时还没有选举。
</td> </tr>
<tr><td>哪个城市的房子一定会升值？ </td> <td>无法确定，因为房价受多种因素影响。
</td> </tr>
<tr><td>哪只球队在每年的中超联赛中都进球最多？</td> <td>无法回答，因为没有提供具体的年份信息。需要知道哪个赛季的中超联赛才能确定哪一支球队进球最多。
</td> </tr>
</table>
10. **通用能力**
- 生成标题
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>请用一个简单而有力的标题来概括这篇论文的主要观点，使人们更容易理解它的主旨。
<br/>摘要：单纯降低凿岩爆破的直接成本,而忽视爆破效果不良所造成的掘进效率低、矿石大块率、贫化与损失率提高等,将给生产带来较大的经济损失。金岭铁矿加强爆破工程管理、提高职工技术水平和思想素质,保证爆破效果,提高了矿山的经济效益。
<br/>领域：冶金工业及金属 </td> <td>浅谈金岭铁矿的爆破效果分析及其对经济效益的影响——以金岭铁矿为例
</td> </tr>
</table>
- 生成摘要
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>标题：基于混合稀疏基字典学习的微波辐射图像重构方法
<br/>领域：信息与通信工程
<td>为提高雷达目标的检测性能，提出了一种新的基于混合稀疏基(MSG)字典学习算法的微波成像重建模型。该模型利用了信号和噪声之间的相关性来提取出一组特征向量，然后通过在训练样本上建立一个MSG字典对特征向量进行分类，从而实现对目标回波信息的恢复。实验结果表明，所提算法具有较高的鲁棒性、较强的抗噪能力以及较低的计算复杂度，能够有效地改善目标检测效果。
</td> </tr>
</table>
- 其他
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>给一个房产广告写一篇文案，突出该房产的特点和优势。
</td>
<td>如果您正在寻找一处舒适、安全且具有投资价值的住宅，那么我们诚挚地邀请您来参观我们的楼盘——XX小区！
<br/>首先，让我们来看看这个区域的地理位置。XX小区位于市中心的黄金地段，交通便利，周边商业配套齐全，生活便捷。同时，这里也是教育资源丰富的区域之一，拥有多所优质学校，为孩子的成长提供了良好的保障。
<br/>其次，我们来看看这个小区的建筑风格。XX小区采用现代化的设计理念，建筑外观简洁大方，内部装修豪华精致，让您感受到家的温馨与舒适。此外，小区内还配备了完善的安保系统，24小时保安巡逻，为您和家人的安全保驾护航。
<br/>最后，让我们来看一下这个小区的投资价值。由于地处繁华商圈，加上优质的物业管理和服务，XX小区的房价一直处于高位，升值潜力巨大。而且，随着城市的发展，未来房价还将持续上涨，是您投资的不二之选。
<br/>总之，XX小区是一个集居住、工作和教育于一体的完美选择。无论是自住还是出租，都是您的理想之地。欢迎前来看房，我们将竭诚为您提供最优质的服务！
</td> </tr>
<tr><td>生成一首诗，以“冬雪”为主题。
</td>
<td>冬日白雪皑皑落，
<br/>寒风凛冽吹不停。
<br/>银装素裹山岗色，
<br/>美景如画在眼前。
</td> </tr>
<tr><td>发表一篇关于母爱的文章，探讨母爱的力量和影响，至少500字。</td>
<td>母爱是世间最伟大的力量之一，它像一股温暖的阳光照耀着我们的心灵深处，让我们感受到生命的美好与温暖。母爱是一种无私的奉献、一种无微不至的关怀，它是人类生命中最为珍贵的财富。
<br/>母亲是我们人生中最重要的人之一，她给予我们生命，抚育我们的成长，陪伴我们在人生的道路上前行。母亲的爱如同春雨般滋润着每一个人的心灵，给我们带来无尽的温馨和感动。无论是在生活中还是工作中，我们都需要母亲的支持和理解，因为她们总是默默地为我们付出一切。
<br/>母爱的影响不仅仅体现在情感上，它还能够改变一个人的命运和生活方式。一个有良好家庭教育的孩子往往会更加自信、勇敢、独立，因为他们在成长的过程中得到了父母的关爱和支持；而缺乏母爱的孩子则往往容易变得自卑、内向、孤僻，甚至产生心理问题。因此，母爱对于孩子的成长和发展具有重要的作用。
<br/>同时，母爱也是推动社会进步的重要因素。在一个家庭中，如果父母都能够以身作则地传递爱心和责任感，那么整个社会的道德水平也会得到提高。这种爱的传承不仅可以帮助孩子们树立正确的人生观和价值观，还可以促进家庭的和谐稳定和社会的稳定发展。
<br/>总之，母爱是人类最美好的礼物之一，它不仅仅是一种情感的表达，更是一种责任的体现。作为子女，我们要学会感恩母亲的恩情，珍惜这份宝贵的亲情；作为家长，我们也要时刻关注子女的身心健康，为他们创造良好的生活和学习环境。只有这样，才能让母爱在我们的生命中发挥出更大的价值和意义。
</td> </tr>
</table>
经过我们的微调，目前TechGPT-2.0对其不知道的问题会直接进行不可回答的回复，对与人类价值观相悖的问题会进行否定回答并给出合理建议。
另外，还需注意，上述结果并非设置为相同的输出长度。在默认输出长度设置下，模型输出的结果偏保守，即很多正确的结果在强制大模型输出更长结果的条件下可以得到，但在默认输出长度设置条件下，这些正确的结果并没有被模型输出；有时也可能因为强制模型输出的长度过长，导致模型过分填充一些无用字符。
## 总结
**TechGPT-2.0** 在许多方面进行了改进，使模型能够拥有更强的知识图谱构建能力尤其是在**嵌套NER**以及**医学、法律**领域能力的提升，并且模型对**幻觉等边界问题以及长文本问题**的回答也得到了改善。
目前，我们对TechGPT的研究也在持续进行中，如外挂知识库、大模型与知识图谱融合、长文本问题优化等方向。
## 主要贡献者
排名不分先后
<table>
<tr>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/86044648?v=4" alt="Contributor 1" height="150">
<br>
<b>wangjiaqi @ Northeastern University</b>
<br>
<a href='https://github.com/wangjiaqi886'>TechGPT-2.0 项目的核心开发成员</a>
</td>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/109963333?v=4" alt="Contributor 2" height="150">
<br>
<b>Chang Yuying @ Northeastern University</b>
<br>
<a href='https://github.com/changyuying'>TechGPT-2.0 项目的核心开发成员</a>
</td>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/49021250?v=4" alt="Contributor 3" height="150">
<br>
<b>SIGMOID @ Northeastern University</b>
<br>
<a href='https://github.com/lizhongv'>TechGPT-2.0 项目的核心开发成员</a>
</td>
</tr>
</table>
## 免责声明
该项目仅供学习交流使用，禁止用于商业用途。在使用过程中，使用者需认真阅读并遵守以下声明:
1. 本项目仅为大模型测试功能而生，使用者需自行承担风险和责任，如因使用不当而导致的任何损失或伤害，本项目概不负责。
2. 本项目中出现的第三方链接或库仅为提供便利而存在，其内容和观点与本项目无关。使用者在使用时需自行辨别，本项目不承担任何连带责任；
3. 使用者在测试和使用模型时，应遵守相关法律法规，如因使用不当而造成损失的，本项目不承担责任，使用者应自行承担；若项目出现任何错误，请向我方反馈，以助于我们及时修复；
4. 本模型中出现的任何违反法律法规或公序良俗的回答，均不代表本项目观点和立场，我们将不断完善模型回答以使其更符合社会伦理和道德规范。
使用本项目即表示您已经仔细阅读、理解并同意遵守以上免责声明。本项目保留在不预先通知任何人的情况下修改本声明的权利。
## 引用
如果使用本项目的代码、数据或模型，请引用本项目。
```
@misc{TechGPT,
author = {Feiliang Ren, Jiaqi Wang, Yuying Chang, Zhong Li},
title = {TechGPT 2.0: Technology-Oriented Generative Pretrained Transformer 2.0},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/neukg/TechGPT}},
}
```
## 致谢
**我们对 [Chinese-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) 和 [Atom](https://github.com/FlagAlpha/Llama2-Chinese) 的相关项目和研究开发人员表示衷心的感谢！**
```
@article{Chinese-LLaMA-Alpaca,
title={Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca},
author={Cui, Yiming and Yang, Ziqing and Yao, Xin},
journal={arXiv preprint arXiv:2304.08177},
url={https://arxiv.org/abs/2304.08177},
year={2023}
}
```
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binxia/llmga-sdxl-t2i | binxia | 2023-11-29T07:44:13Z | 0 | 0 | null | [
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-29T07:44:13Z | 2023-11-25T13:35:13.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | binxia/llmga-sdxl-t2i | [
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Plasmati/AIDRIVEORNOTMODELS2 | Plasmati | 2023-11-29T12:35:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:35:01Z | 2023-11-25T18:58:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Plasmati/AIDRIVEORNOTMODELS2 | [
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sametayhan/Reinforce-cartpole | sametayhan | 2023-11-29T17:11:57Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T17:11:57Z | 2023-11-25T19:42:46.000Z | null | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | sametayhan/Reinforce-cartpole | [
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neukg/TechGPT-2.0-atom-hf | neukg | 2023-11-29T14:26:26Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2304.08177",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:26:26Z | 2023-11-26T13:02:26.000Z | null | null | # TechGPT 2.0: Technology-Oriented Generative Pretrained Transformer 2.0
Demo: [TechGPT-neukg](http://techgpt.neukg.com) <br>
HuggingFace🤗: [neukg/TechGPT-7B](https://huggingface.co/neukg)
<div align="center">
[](https://github.com/neukg/TechGPT/blob/main/LICENSE)
[](https://huggingface.co/neukg)
</div>
## 引言
着大模型时代的到来，大模型与知识图谱融合的工作日益成为当前研究的热点。为了对这项工作提供研究基础，东北大学知识图谱研究组于2023年6月发布了[TechGPT-1.0](https://github.com/neukg/TechGPT)大模型。在此基础上，经过几个月的工作，东北大学知识图谱研究组发布 **TechGPT-2.0** 大模型。在继承TechGPT-1.0全部功能的基础上，TechGPT-2.0 **对其多项功能进行了增强**，并**扩充了一些新功能**。
## 内容导引
| 章节 | 描述 |
|----------------------|-----------------------------|
| [💁🏻♂️模型简介](#模型简介) | 简要介绍本项目 TechGPT 2.0 模型的技术特点 |
| [📝模型亮点](#模型亮点) | 介绍了 TechGPT 2.0 大模型的独特之处 |
| [⏬模型下载与体验](#模型下载与体验) | TechGPT 2.0 大模型下载地址与Demo体验 |
| [💻环境部署](#推理与部署) | 介绍了如何使用个人环境部署并体验大模型 |
| [💯系统效果](#系统效果) | 展示了模型在部分任务上的效果 |
## 模型简介
TechGPT-2.0 为 TechGPT-1.0 基础上的改进版本，此次共发布两个7B版本的模型分别为**TechGPT2-Alpaca**、**TechGPT2-Atom**。
TechGPT-2.0 较 TechGPT-1.0 新加了许多领域知识。除了 TechGPT-1.0 所具备的计算机科学、材料、机械、冶金、金融和航空航天等十余种垂直专业领域能力，TechGPT-2.0 还在**医学、法律领域**展现出优秀的能力，并扩充了**地理地区、运输、组织、作品、生物、自然科学、天文对象、建筑**等领域能力。除此之外，我们的工作还对**幻觉、不可回答问题、长文本**等任务进行了研究。
**东北大学知识图谱研究组**与**华为沈阳人工智能计算中心**合作，使用**华为昇腾服务器**（具体为4机*8卡-32G 910A）进行**全量微调**。
## 模型亮点
TechGPT-2.0 在继承了 TechGPT-1.0 的能力上进行了重要的改进，其中最显著的优化是**引入了命名实体识别的领域数据**，并且**使模型具备了对嵌套实体的抽取能力**。以下为TechGPT-2.0大模型的亮点介绍：
- 首先，TechGPT-2.0 在**医学领域**的表达和理解方面取得了显著的提升。模型能够对疾病、药物、专业术语等实体的进行更准确识别，这表明模型在处理医学文本时能够更全面地理解上下文信息。除此之外，TechGPT-2.0 还能够理解医学文本中的复杂关系、疾病诊断、治疗方案等内容。这种全面的医学分析能力使得模型可以用于协助医生阅读医学文献、提供患者诊断建议等应用场景，从而提高医学领域的信息处理效率和准确性。
- 其次，TechGPT-2.0 能够理解和解释法律文本，包括法规、合同和案例法等。这使得模型在**法律领域**的应用更为广泛，并可以用于解决自动化合同审查、法规遵循检查等任务。模型通过学习法律用语和结构，能够更准确地捕捉文本中的法律关系和条款，为用户提供更有深度和专业性的法律分析。
- 再次，TechGPT-2.0 的另一个重要特性是能够**抽取嵌套实体**。这意味着模型可以更灵活地处理实体之间的复杂关系，深入挖掘文本中的层次结构，提高了对复杂文本的理解和生成能力。例如，在医学文献中，可能存在嵌套的实体关系，如疾病的亚型、药物的剂量信息等，TechGPT-2.0 能够更好地捕捉这些信息，并在生成回应时更准确地反映上下文的语境。
- 另外，TechGPT-2.0 还在**幻觉、长文本以及与人类价值观对齐方面进行了强化**。模型通过对话和理解上下文，能够更好地理解人类的感受和价值观，并在回应中考虑这些因素。此外，TechGPT-2.0能够支持最大长度12K的文本输入。这使得模型能够更好地与人类用户进行交互，更好地满足用户的需求和期望，进一步提升了人机交互的质量和用户体验。
- 最后，TechGPT-2.0 的训练使用**华为昇腾910A服务器**，并基于**华为Mindspore框架**以及Mindformer套件进行开发，采用分布式训练方案，机器数量为4机*8卡（32G）。目前TechGPT-2.0推理部署在使用**NVIDIA**的显卡并使用float16的情况下显存仅需**15G**，INT8量化下显存占用**8G**，INT4量化下显存仅需**5G**；NAVID 4090单机单卡、或昇腾910A单机单卡就可完成推理部署。
总体而言，TechGPT-2.0 在继承了 TechGPT-1.0 的强大自然语言处理能力的同时，**通过增加多领域、多任务的数据，展现出了嵌套实体的抽取、幻觉回答、回答不可回答问题和回答长文本问题的能力**。这些改进使得模型更适用于广泛的应用场景，为用户提供了更准确、更深入的信息处理和生成能力。
## 模型下载与体验
### 下载地址
| 模型名称 | 类型 | 训练方式 | 大小 | 下载地址 |
|:----------------------|:----:|:------:|:-----:|:------------------------------------------------------------------:|
| TechGPT-1.0 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-7B) |
| TechGPT-2.0-Alpaca 🆕 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-alpaca-hf) |
| TechGPT-2.0-Atom 🆕 | 指令模型 | 全量微调 | 13 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-atom-hf) |
| TechGPT-2.0-QLora 🆕 | 指令模型 | Lora微调 | 3 GB | [[🤗HF]](https://huggingface.co/neukg/TechGPT-2.0-QLora-hf) |
### 模型说明
**TechGPT2** 在不同规模且经过扩充后的**TechKG**大规模的中文学术语料支持下训练完成。
- **TechGPT2-Alpaca** 使用HFL发布的Chinese-Alpaca-2-7B模型，Chinese-Alpaca-2-7B模型是在Chinese-LLaMA-2-7B的基础上进一步通过指令精调获得。Chinese-LLaMA-2-7B为基座模型，是在LLaMA-2的基础上进行中文增量训练获得。
- **TechGPT2-Atom** 使用Llama中文社区联合AtomEcho（原子回声）共同发布的Atom-7B-chat，并在此基础上进行全量微调后获得。
- 此外，我们使用**QLora线性插值法**微调的长文本模型的Lora权重也已经发布在huggingface上，请自行下载合并。
我们TechGPT-2的两个**7B**版本的模型已经在Hugging Face和GitHub上开源，使用后续在此模型基础上的改进，也将开源到相同账号，欢迎各位使用并提出宝贵的意见。
### 模型体验
我们目前对外提供TechGPT-1.0与TechGPT2-Atom版本的在线服务：http://techgpt.neukg.com/
作为一个学术组织，我们无法同时提供三个模型的在线服务，因此建议大家后续通过开源权重自行体验，共同创建更好的中文大模型开源环境。
## 环境部署
### 在华为昇腾 910 NPU 服务器上的环境要求
- 硬件：Ascend 910A/910B
- Python：3.9
- MindSpore：2.1.1
- CANN: 6.3.0 RC2
- MindFormers版本：dev
- 7b 推理可在单机单卡上完成部署
1. 在mindformers环境下执行推理部署时，需要使用ckpt权重；如果没有ckpt权重，则在mindformers目录下需要运行如下[转换脚本](https://github.com/neukg/TechGPT-2.0/blob/main/ckpt_weight_convert/convert_weight.py)，将huggingface权重转为ckpt权重，才能使用NPU进行推理：
``` shell
python mindformers/models/llama/convert_weight.py \
--torch_ckpt_dir TORCH_CKPT_DIR \
--mindspore_ckpt_path {path}/MS_CKPT_NAME
```
2. 初次在mindformers环境下执行推理时，会在```mindspore_inference.py```的同级目录下生成```checkpoint_download```文件夹，其中包含了推理所需的```yaml```配置文件和```tokenizer.model```词表等，需要将词表换成该项目huggingface上的对应词表，并将配置文件替换为```mindspore_inference```目录下的```yaml```文件。
### 在 GPU 服务器上的环境要求
请注意TechGPT2-Alpaca和TechGPT2-Atom模型在**训练**和**推理**阶段所使用的prompt格式是不同。
**TechGPT2-Alpaca 使用的prompt格式为：**
``` python
<s> A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [INST] <<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。请你提供专业、有逻辑、内容真实、有价值的详细回复。\n<</SYS>>\n\n{instruction} [/INST] ASSISTANT:
```
**TechGPT2-Atom 使用的prompt格式为：**
``` python
<s>Human: {instruction} \n</s><s>Assistant:
```
请在使用TechGPT之前保证你已经安装好`transfomrers`和`torch`：
```shell
pip install transformers
pip install torch
```
- 注意，必须保证安装的 `transformers` 的版本中已经有 `LlamaForCausalLM` 。<br>
- Note that you must ensure that the installed version of `transformers` already has `LlamaForCausalLM`.
[TechGPT2-Alpca Example:](https://github.com/neukg/TechGPT-2.0/blob/main/pytorch_inference/techgpt2-alpaca_infer.py)
``` python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
import torch
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。请你提供专业、有逻辑、内容真实、有价值的详细回复。"""
TEMPLATE = (
"<s> A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [INST] <<SYS>>\n"
"{system_prompt}\n"
"<</SYS>>\n\n"
"{instruction} [/INST] ASSISTANT: "
)
def generate_prompt(instruction):
return TEMPLATE.format_map({'instruction': instruction, 'system_prompt': DEFAULT_SYSTEM_PROMPT})
ckpt_path = './LLM/TechGPT2-Alpaca-hf/'
load_type = torch.float16
device = torch.device(1)
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
tokenizer.pad_token_id = 0
tokenizer.bos_token_id = 1
tokenizer.eos_token_id = 2
tokenizer.padding_side = "left"
model_config = AutoConfig.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
model.to(device)
model.eval()
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
bos_token_id=1,
eos_token_id=2,
pad_token_id=0,
max_new_tokens=128,
min_new_tokens=10,
do_sample=True,
)
example = '请把下列标题扩写成摘要, 不少于100字: 基于视觉语言多模态的实体关系联合抽取的研究。'
instruction = generate_prompt(instruction=example)
instruction = tokenizer(instruction, return_tensors="pt")
input_ids = instruction["input_ids"].to(device)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1.2,
)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
print(output)
```
[TechGPT2-Atom Example:](https://github.com/neukg/TechGPT-2.0/blob/main/pytorch_inference/techgpt2-atom_infer.py)
``` python
from transformers import LlamaTokenizer, AutoModelForCausalLM, AutoConfig, GenerationConfig
import torch
TEMPLATE = (
"<s>Human: "
"{instruction} \n</s><s>Assistant: "
)
def generate_prompt(instruction):
return TEMPLATE.format_map({'instruction': instruction})
ckpt_path = './LLM/TechGPT2-Atom-hf/'
load_type = torch.float16
device = torch.device(1)
tokenizer = LlamaTokenizer.from_pretrained(ckpt_path)
tokenizer.pad_token_id = 2
tokenizer.bos_token_id = 1
tokenizer.eos_token_id = 2
tokenizer.padding_side = "left"
model_config = AutoConfig.from_pretrained(ckpt_path)
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=load_type, config=model_config)
model.to(device)
model.eval()
generation_config = GenerationConfig(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
bos_token_id=1,
eos_token_id=2,
pad_token_id=2,
max_new_tokens=128,
min_new_tokens=20,
do_sample=True,
)
example = '抽取出下面文本的实体和实体类型：《女人树》，国产电视剧，由导演田迪执导，根据作家子页的原著改编，故事从1947年开始，跨越了解放战争和建国初期两大历史时期，展现了战斗在隐形战线上的人民英雄是如何不惧怕任何危险，不计较个人牺牲，甚至不顾人民内部的误解和生死裁决，都不暴露个人真实身份，至死不渝，与敌人周旋到底的英雄故事。'
instruction = generate_prompt(instruction=example)
instruction = tokenizer(instruction, return_tensors="pt")
input_ids = instruction["input_ids"].to(device)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
repetition_penalty=1.2,
)
output = generation_output.sequences[0]
output = tokenizer.decode(output, skip_special_tokens=True)
print(output)
```
## 系统效果
TechGPT-2.0 在保留了 TechGPT-1.0 模型的通用能力、实体识别能力以及三元组抽取能力的基础上，能够完成**更为复杂的嵌套实体识别**，并在**医学、法律领域**展现出优秀的能力，此外对**幻觉、不可回答问题以及长文本问题**有着较好的解决能力。
下面展示了TechGPT-2.0 的几个新增能力的样例。
1. **医学NER**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>这句话中是否有实体？如果有，请列举出来。<br/>依据主要病变部位分为下列数型：脊髓型最常见，为脊髓前角细胞受损所致，表现为分布不对称、不规则的弛缓性肌肉瘫痪，四肢多见，下肢尤甚。 </td> <td>实体名:先天畸形, 实体类型:疾病<br/>实体名:发育障碍, 实体类型:临床表现<br/>实体名:上、下呼吸道梗阻, 实体类型:疾病<br/>实体名:膈疝, 实体类型:疾病<br/>实体名:肺部受压迫, 实体类型:临床表现<br/>实体名:肺, 实体类型:身体<br/>实体名:呼吸衰竭, 实体类型:疾病
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<tr><td>毒蕈类以1∶5000高锰酸钾或活性炭悬液反复洗胃，硫酸镁导泻；用二巯基丙磺酸钠每次5mg/kg，每天2～3次，肌内注射；阿托品0.05mg/kg，肌内注射，每15分钟1次，直至阿托品化后减量。<br/>在这句话中，是否可以找到任何实体名称？ </td> <td>实体名:毒蕈, 实体类型:微生物类<br/>实体名:高锰酸钾, 实体类型:药物<br/>实体名:活性炭, 实体类型:药物<br/>实体名:硫酸镁, 实体类型:药物<br/>实体名:导泻, 实体类型:医疗程序<br/>实体名:二巯基丙磺酸钠, 实体类型:药物<br/>实体名:肌内注射, 实体类型:医疗程序<br/>实体名:阿托品, 实体类型:药物<br/>实体名:肌内注射, 实体类型:医疗程序<br/>实体名:阿托品, 实体类型:药物
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</table>
2. **嵌套NER**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>一位原招商银行信用卡中心负责人告诉记者。请抽取出其中的命名实体。 </td> <td>实体名: 招商银行，实体类型：企业<br/>实体名: 中国，实体类型：地点<br/>实体名: 招商银行信用卡中心， 实体类型：机构<br/>实体名: 银行，实体类型：企业
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<tr><td>这句话中是否有实体？如果有，请列举出来。<br/>依据主要病变部位分为下列数型：脊髓型最常见，为脊髓前角细胞受损所致，表现为分布不对称、不规则的弛缓性肌肉瘫痪，四肢多见，下肢尤甚。</td> <td>实体名:脊髓, 实体类型:身体<br/>实体名:脊髓前角细胞, 实体类型:身体<br/>实体名:脊髓前角细胞受损, 实体类型:临床表现<br/>实体名:分布不对称、不规则的弛缓性肌肉瘫痪, 实体类型:疾病<br/>实体名:肢体, 实体类型:身体<br/>实体名:下肢, 实体类型:身体
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<tr><td>这句话是否包含任何命名实体？如果有，请将其列举出来。<br/>在弥漫性肺损伤和肺泡萎陷时，要获得比较好的氧合，需要在机械通气时保持压力的平台，以使肺泡持续扩张，气体在不同时间常数的肺泡单位内移动，达到压力平台，使得到较好的肺泡通气/灌流，减少肺内分流。</td> <td>实体名:弥漫性肺损伤, 实体类型:疾病<br/>实体名:肺泡萎陷, 实体类型:临床表现<br/>实体名:肺泡, 实体类型:身体<br/>实体名:机械通气, 实体类型:医疗程序<br/>实体名:压力的平台, 实体类型:医疗设备<br/>实体名:肺泡持续扩张, 实体类型:临床表现<br/>实体名:气体不同时间常数, 实体类型:医学检验项目<br/>实体名:肺泡通气/灌流, 实体类型:医疗程序<br/>实体名:肺内分流, 实体类型:临床表现<br/>实体名:肺内, 实体类型:身体
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<tr><td>出血因凝血因子大量被消耗、血小板减少及继发纤溶亢进，发生出血。<br/>请列示这段文本中的所有特定实体。</td> <td>实体名:出血, 实体类型:临床表现<br/>实体名:凝血因子, 实体类型:身体<br/>实体名:凝血因子大量被消耗, 实体类型:临床表现<br/>实体名:血小板, 实体类型:身体<br/>实体名:血小板减少, 实体类型:临床表现<br/>实体名:继发纤溶亢进, 实体类型:疾病<br/>实体名:出血, 实体类型:临床表现
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</table>
3. **泛化NER（根据背景知识泛化的NER）**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>下面句子中的实体有哪些《女人树》，国产电视剧，由导演田迪执导，根据作家子页的原著改编，故事从1947年开始，跨越了解放战争和建国初期两大历史时期，展现了战斗在隐形战线上的人民英雄是如何不惧怕任何危险，不计较个人牺牲，甚至不顾人民内部的误解和生死裁决，都不暴露个人真实身份，至死不渝，与敌人周旋到底的英雄故事。 </td> <td>实体名: 女人树，实体类型：影视作品<br/>实体名: 田迪，实体类型：人物<br/>实体名: 中国，实体类型：国家<br/>实体名: 女人树，实体类型：图书作品<br/>实体名: 子页，实体类型：人物
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<tr><td>俄德两国4架战机全部逼近芬兰空中边界，芬兰空军歼击机迅速起飞干预，驱逐俄德两国歼击机，并将其“护送”。请抽取出其中的命名实体。 </td> <td>实体名: 俄罗斯，实体类型：国家<br/>实体名: 德国，实体类型：国家<br/>实体名: 芬兰，实体类型：地点<br/>实体名: 芬兰空军，实体类型：机构/人物<br/>实体名: 芬兰，实体类型：国家
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</table>
4. **法律案件梳理**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>苏国平签订的劳动合同期限是什么？<br/>经审理查明,原告苏国平于1979年4月顶替父亲进入常州东方印染厂(国有企业)工作,1996年6月与常州东方印染厂与华源集团合资成立的常州华昌染工有限公司签订劳动合同,合同期限为1996年6月1日至1997年5月31日。后双方续签,最后一份劳动合同期限为1998年5月31日至2001年5月31日。1999年8月30日,常州华昌染工有限公司作出华昌(1999)3号《关于苏国平开除出公司的决定》,该决定载明“苏国平原系华昌染工有限公司前道主管……是公司多年培养的技术骨干,于99年8月初为了个人利益置公司利益而不顾,未办任何手续不告而别,直接为其他印染厂提供技术服务,使本公司利益受到严重伤害……经公司经理会研究决定将苏国平开除出公司,并要求其负经济赔偿责任”,该决定还载明“抄送:市仲裁处、市纺工局、市郊区劳服公司、本人”。1999年9月,常州华昌染工有限公司由常州东方印染厂托管,东方印染厂劳资经办人员在1999年11月办理解除劳动合同手续时,在解除原因一栏填写了“除名”。苏国平的社保参保缴费证明显示其1999年8月前在常州华昌染工有限公司参保、1999年10月起改至常州市金田纺织印染厂,至2019年2月累计缴费31年1月。2019年2月20日,常州视音电子科技有限公司为原告申办企业职工基本养老保险待遇申领手续,被告于2月25日作出本案退休待遇审核表。原告不服,诉至本院。庭审中,各方当事人围绕案涉开除决定是否合法有效各执己见。</td> <td>1996年6月1日至1997年5月31日;1998年5月31日至2001年5月31日
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<tr><td>承包方何时进行行贿？<br/>经审理查明:2012年7月,被告人陈x1以不具备承建资质的衡南县向阳镇水口基建队的名义承包了国有事业单位衡南县教师进修学校学员宿舍改造土建项目工程。为获取不正当利益,被告人陈x1于2012年9月底至2013年底分三次送给该校校长、党支部书记程x9好处费共计6万元。具体犯罪事实如下:1、2012年9月份的一天,被告人陈x1为感谢程x9将衡南县进修学校学员宿舍楼改造改建工程交给其做,争取程x9对其做工程给予关照,事后多安排工程给其做,在程x9的办公室送给程x91万元现金,程x9予以收受。2、2012年底的一天,被告人陈x1为承建衡南县进修学校罕舍楼改造的其他后续工程,在衡南县教师进修学校宿舍改造工地上送给程x91万元现金,程x9予以收受,并将后续工程全部交由陈x1承建。3、2013年底的一天,被告人陈x1为感谢程x9在其承建该校工程中给予的关照并争取被告人程x9同意及时尽多拨付工程款,在程x9的办公室送给程4万元现金,程x9予以收受,并尽可能地拨付工程款给陈x1。2014年3月,程x9得知衡南县纪律检查委员会在对其违纪违法行为进行调查后,于3月18日安排其妻子秦某某将从被告人陈x1处收受的6万元贿赂款退还给了陈x1。2014年5月12日,被告人陈x1主动向湖xxxxxxxxx0投案,如实供述了自己的犯罪事实,并上缴了其用于行贿的6万元钱。上述事实有被告人陈x1于衡南县教师进修学校签订的建筑安装工程施工合同、被告人陈x1的常口信息等书证;同案犯程x9的供述及证人秦某某、罗某某等人的证言;被告人陈x1的供述与辩解等证据证实,经庭审质证,被告人陈x1均无异议,足以认定。 </td> <td>2012年9月份<br/>2012年底<br/>2013年底
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<tr><td>案中法院作出过什么民事判决书<br/>经审理查明,原、被告曾7合伙关系,双方因纠纷经本院调解达成(2014)泰兴戴民初字第0746号民事调解书,被告龚x1依调解协议约定,于2015年2月12日将应支付给原告的19万元缴纳至本院。2015年2月12日,被告龚x1向本院提起诉讼,要求案外人窦x6立即给付其货款16万元,并将本案原告列为该案第三人。2015年3月27日,依龚x1的申请,本院作出(2015)泰兴商初字第123号民事裁定书,对原告的19万元(被告缴纳至本院的)进行了保全。2015年4月23日,本院作出(2015)泰兴商初字第123号民事判决书,判决:1、窦x6支付龚x1、兴化市星权化工产品经营部31003.4元;2、驳回龚x1、兴化市星权化工产品经营部的其他诉讼请求。后因龚x1不服,上诉至泰州市中级人民法院,该院于2016年1月5日作出(2015)泰中商四终字第73号民事判决:驳回上诉,维持原判。2016年2月5日,本院解除了对原告财产的保全,原告亦于当日取得了19万元。另查明,涉案的19万元系被告应支付给原告的履行款,由被告缴纳至兴化市财政局在江苏吴江农村商业银行股份有限公司开设的账户内。2015年3月27日,中国人民银行同期贷款利率为年利率5.35%,江苏吴江农村商业银行同期活期存款利率为年利率0.35%。以上事实有原告提供的民事调解书、民事判决书、民事裁定书、被告提供的现金解款单及本院庭审笔录在卷佐证。 </td> <td>（2015）泰兴商初字第123号民事判决书<br/>（2015）泰中商四终字第73号民事判决
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</table>
5. **法律咨询**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>夫妻双方已经离婚，有一个儿子，因为丈夫犯了错，老人明确写下了与丈夫断绝关系，财产由另外两个子女平分，多年来妻子从未过问大家庭一次，老人生病也连一句问候都没有，现在老人走了，妻子有权利争夺财产吗？</td> <td>没有权利。根据你的描述，老人的遗嘱是明确的将遗产留给了其他兄弟姐妹，而你并没有继承权；同时你作为丈夫的前妻和儿子的母亲也没有任何的法定继承人资格来分割遗产。因此，你没有权利去争抢这个遗产份额。
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<tr><td>你好，请问在工地干活左手手腕筋断一根和断血管一根能赔多少钱？ </td> <td>工伤赔偿项目包括：医疗费、住院伙食补助费、停工留薪期工资、护理费、交通费等。具体赔偿金额需要根据实际情况来确定的。建议您先申请劳动能力鉴定，确定伤残等级后按照伤残等级计算赔偿数额。
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<tr><td>无证醉酒驾驶摩托车，喝了一杯白酒，怎么处罚？ </td> <td>饮酒后驾车属于危险驾驶行为。根据《中华人民共和国道路交通安全法》的规定：未取得机动车驾驶证、机动车驾驶证被吊销或者机动车驾驶证被暂扣期间驾驶机动车的处二百元以上二千元以下罚款，可以并处十五日以下拘留。<br/>同时，如果造成事故构成犯罪的，还要承担刑事责任。具体来说，酒后驾驶情节严重的，可能会面临拘役的处罚；而醉驾致人死亡或重伤的，则可能会被判处三年以上七年以下的有期徒刑。因此，建议您不要酒驾，以免给自己和他人带来不必要的伤害和损失。
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</table>
6. **医学咨询**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>年10岁近两天再次出现干呕恶心呕吐腹痛，喝了止泻药但情况没太大变动，应该如何治疗，如何吃晚饭？</td> <td>您描述的症状主要包括胃部不适症状如恶心、呕吐以及肚子痛，建议立即就医确诊病因并采取合适的治疗方法。以下是一些可能帮助您的方法和建议：<br/>* 首先应该考虑是否是因为最近吃了什么不健康的食物或饮料导致消化不良所引起。如果是这样，可以避免这些食物以促进消化吸收。此外，可以考虑喝一杯温水以刺激肠胃运动，或者使用小苏打来缓解酸碱失衡问题（请咨询医生）。<br/>* 如果以上方案无效或是其他原因引起的疼痛等病症持续存在并且影响日常生活时间较长的话最好去医院检查一下是否存在某种疾病感染，进行相应的处理方式会因具体病情而异。同时需要评估治疗费用负担的能力状况，以免造成经济损失。<br/>* 在生病期间饮食选择上需要注意营养均衡的选择多样化多样的食品组合，避免过量或不当搭配的情况产生不良反应。例如食用易腐烂变质食物要加强注意；辛辣刺激性食物需减少甚至禁食等等。
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<tr><td>邻居家的女宝宝才1岁，近几天，孩子的妈妈说孩子耳朵有点疼，察觉到，偶尔还会有黄色的液体流出，同时，食欲也不好，嗓子也不舒服，请问：小孩儿中耳炎耳朵胀痛怎样治效果好。 </td> <td>抗生素药物是目前治疗中耳炎比较常用的，可酌情选。也可以配合一些局部治疗的方法，比如消炎型的滴耳液医治，如孩子耳痛流黄水的症状严重，建议选用一些镇痛药缓解症状，要是伴随发高烧的情况，那么根据孩子的症状使用药物，严重的情况请尽快去医院进行救治，存在耳膜穿孔的情况家长可考虑可孩子手术治疗，期间注意让孩子充分休息，并且多吃许多蔬菜水果。
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<tr><td>早期胃癌手术四十天了，其它都正常，就是夜里有点虚汗，平时吃稀饭和面条以及鱼汤或骨头汤等，现在可以吃点米饭吗？饮食上吃点什么可以增强营养。 </td> <td>病情分析：您好！根据您的描述来看，您目前情况考虑为术后恢复期的问题指导意见：建议您可以适当进食一些易消化的食物，如粥、面片、馄饨等，同时注意补充蛋白质及维生素C的摄取，以促进伤口愈合，提高免疫力，预防感染的发生。祝您早日康复！
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</table>
7. **幻觉问题**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>地球上一共装了多少台行星发动机？</td> <td>实际上，地球并没有安装任何的“行星引擎”。这是一个常见的误解或谣言来源之一。以下是一些详细的解释：<br/>首先需要明确的是，我们通常所说的“行星引擎”指的是一种人造卫星上的推进系统，它通过使用燃料和氧化剂来产生推力以使卫星在轨道上运动并维持其运行状态的一种装置。这些推进系统的类型包括化学火箭、核反应堆等。然而，这种类型的设备并不存在于地球的表面或者大气层中。相反地，它们是由人类设计和建造的用于太空探索和研究的工具。因此，我们不能说地球上安装了任何形式的“行星引擎”。<br/>其次，即使将这个问题看作是指地球上存在某种类似于外星文明的先进技术，我们也无法确定该文明是否真的拥有类似星际飞船这样的交通工具以及它们的运作方式是什么样的。这涉及到许多科学和技术难题，如宇宙物理学、生命起源等等，而这些问题的答案目前还没有被完全解决。 因此，关于外星人存在的证据仍然非常有限且难以证明，而所谓的“行星引擎”也只是虚构的概念而已。<br/>综上所述，没有确切的数据表明地球上有安装在地面上的任何“行星引擎”的存在。这只是一个流传已久但无根据的说法。
</td> </tr>
<tr><td>中国最好的烹饪学校是哪里？ </td> <td>没有一个“最好”的烹饪学校，因为每个人对“好”的定义不同。但是以下是一些受欢迎和有影响力的学校：<br/>1. 北京厨艺学院（Beijing Culinary College）- 该校是中国最古老的厨师培训机构之一，成立于1956年。它提供各种课程，包括中餐、西餐、糕点等。该学校的师资力量强大，许多学生毕业后成为知名餐厅的主厨或餐饮企业高管。<br/>2. 上海交通大学附属高级职业技术学院（Shanghai Jiao Tong University Affiliated Vocational Technical School of Advanced Professional Technology and Arts） - 这所院校位于上海，是一所综合性的职业教育机构。该校拥有多个专业领域，其中包括烹饪艺术。该校注重实践教学，为学生提供了丰富的实习机会和实践经验。<br/>3. 广东省粤菜文化研究院（Guangdong Province Guangzhou Chinese Cookery Culture Research Institute）- 这是一所专注于广东菜肴文化的研究机构和培训中心。该校的课程涵盖了从基础到高端的菜肴制作技巧，以及中国传统饮食文化和历史背景等方面的知识。该校还与多家著名酒店合作，为学员提供实际操作的机会。<br/>4. 四川成都川味厨房职业技能培训学校（Chengdu Sichuan Kitchen Skills Training School）- 位于四川成都的一家私立学校，专门教授川菜烹饪技艺。该校的教学方法独特，强调理论与实践相结合，让学生能够快速掌握川菜的制作方法和技巧。此外，该校也开设了其他相关领域的课程，如中式面点、点心等。<br/>以上是一些备受欢迎和有影响的烹饪学校，但选择哪个学校取决于个人兴趣和专业需求。在选择时需要考虑学费、地理位置、师资力量等因素。
</td> </tr>
<tr><td>目前公认的最好的职业是什么？ </td> <td>没有一种职业是“最好”的，因为每个人都有自己的兴趣、技能和目标。然而，以下是一些被认为是最有前途的职业：
<br/>1. 数据科学家/数据分析师 - 随着数字化时代的到来，对数据的需求越来越高，因此数据科学成为了一个非常热门的领域。数据科学家可以帮助企业分析大量数据并做出明智的决策。
<br/>2. 网络安全专家 - 在数字时代，网络攻击变得越来越多样化且复杂。网络安全专业人员需要保护企业和个人免受黑客和其他威胁的攻击。
<br/>3. 医生 - 尽管医疗保健行业面临着许多挑战，但医生仍然是最受欢迎的职业之一。随着人口老龄化和健康意识的提高，人们对医生的需求和期望也在不断增加。
<br/>4. 金融顾问 - 由于金融市场的复杂性和高风险性，金融顾问的需求也日益增长。他们可以为客户提供投资建议和指导，以帮助他们实现财务目标和规划未来。
<br/>5. 开发工程师 - 编程是一项非常有前景的工作，尤其是在技术领域。开发工程师可以在各种行业中工作，包括软件公司、游戏工作室等。
<br/>6. 教师 - 虽然教育行业的竞争激烈，但是教师是一个稳定的职业选择，而且对于那些喜欢与孩子互动的人来说也是一个很好的选择。
<br/>7. 律师 - 作为法律领域的重要角色，律师能够为客户解决问题并提供咨询服务。虽然这个职位的竞争也很大，但它仍然被视为一项很有前途的职业。
<br/>总之，以上这些职业都是目前被广泛认为是最有前途的职业之一。当然，还有很多其他的职业也非常有价值，具体取决于个人的兴趣和能力。
</td> </tr>
</table>
8. **三元组抽取**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>请给出文中的三元组 《善德女王》是由金根洪、朴弘均联合执导，金英贤编剧，高贤贞、李瑶媛、金南佶、严泰雄、朴艺珍、俞承豪主演的一部韩国历史剧。</td> <td><善德女王, 主演, 金南佶>
<br/><善德女王, 导演, 金根洪>
<br/><善德女王, 编剧, 金英贤>
<br/><善德女王, 主演, 李瑶媛>
<br/><善德女王, 主演, 高贤贞>
<br/><善德女王, 主演, 严泰雄>
<br/><善德女王, 主演, 朴艺珍>
<br/><善德女王, 导演, 朴弘均>
<br/><善德女王, 主演, 俞承豪>
</td> </tr>
<tr><td>张开建，男，汉族，山东梁山人，1953年11月出生，1969年12月参加工作，1973年9月加入中国共产党，1979年1月北京大学地质系地震地质专业毕业，大学学历，高级工程师<br/>你能否将文本中所有的实体关系三元组列举出来吗？三元组形式是什么呢？ </td> <td>
<br/><张开建, 民族, 汉族>
<br/><张建, 国籍, 中国>
<br/><张开建, 出生地, 山东梁山>
<br/><张开建, 毕业院校, 北京大学>
<br/><张开建, 出生日期, 1953年11月>
</td> </tr>
<tr><td>请给出下文中的所有关系三元组，三元组形式为<主语，关系，宾语><br/>文本：今天，才知道了国歌的名字叫《义勇军进行曲》，知道了国歌的词曲作者田汉和聂耳，还知道了国歌象征着中华民族永不屈服的抗争精神</td> <td>
<br/><义勇军进行曲, 作词, 田汉>
<br/><义勇军进行曲, 作曲, 聂耳>
</td> </tr>
</table>
9. **不可回答问题**
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>我想知道2045年美国总统是谁？</td> <td>无法确定，因为到那时还没有选举。
</td> </tr>
<tr><td>哪个城市的房子一定会升值？ </td> <td>无法确定，因为房价受多种因素影响。
</td> </tr>
<tr><td>哪只球队在每年的中超联赛中都进球最多？</td> <td>无法回答，因为没有提供具体的年份信息。需要知道哪个赛季的中超联赛才能确定哪一支球队进球最多。
</td> </tr>
</table>
10. **通用能力**
- 生成标题
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>请用一个简单而有力的标题来概括这篇论文的主要观点，使人们更容易理解它的主旨。
<br/>摘要：单纯降低凿岩爆破的直接成本,而忽视爆破效果不良所造成的掘进效率低、矿石大块率、贫化与损失率提高等,将给生产带来较大的经济损失。金岭铁矿加强爆破工程管理、提高职工技术水平和思想素质,保证爆破效果,提高了矿山的经济效益。
<br/>领域：冶金工业及金属 </td> <td>浅谈金岭铁矿的爆破效果分析及其对经济效益的影响——以金岭铁矿为例
</td> </tr>
</table>
- 生成摘要
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>标题：基于混合稀疏基字典学习的微波辐射图像重构方法
<br/>领域：信息与通信工程
<td>为提高雷达目标的检测性能，提出了一种新的基于混合稀疏基(MSG)字典学习算法的微波成像重建模型。该模型利用了信号和噪声之间的相关性来提取出一组特征向量，然后通过在训练样本上建立一个MSG字典对特征向量进行分类，从而实现对目标回波信息的恢复。实验结果表明，所提算法具有较高的鲁棒性、较强的抗噪能力以及较低的计算复杂度，能够有效地改善目标检测效果。
</td> </tr>
</table>
- 其他
<table> <colgroup> <col style="width:50%"> <col style="width:50%"> </colgroup> <tr> <td>问题</td> <td>回复</td> </tr>
<tr><td>给一个房产广告写一篇文案，突出该房产的特点和优势。
</td>
<td>如果您正在寻找一处舒适、安全且具有投资价值的住宅，那么我们诚挚地邀请您来参观我们的楼盘——XX小区！
<br/>首先，让我们来看看这个区域的地理位置。XX小区位于市中心的黄金地段，交通便利，周边商业配套齐全，生活便捷。同时，这里也是教育资源丰富的区域之一，拥有多所优质学校，为孩子的成长提供了良好的保障。
<br/>其次，我们来看看这个小区的建筑风格。XX小区采用现代化的设计理念，建筑外观简洁大方，内部装修豪华精致，让您感受到家的温馨与舒适。此外，小区内还配备了完善的安保系统，24小时保安巡逻，为您和家人的安全保驾护航。
<br/>最后，让我们来看一下这个小区的投资价值。由于地处繁华商圈，加上优质的物业管理和服务，XX小区的房价一直处于高位，升值潜力巨大。而且，随着城市的发展，未来房价还将持续上涨，是您投资的不二之选。
<br/>总之，XX小区是一个集居住、工作和教育于一体的完美选择。无论是自住还是出租，都是您的理想之地。欢迎前来看房，我们将竭诚为您提供最优质的服务！
</td> </tr>
<tr><td>生成一首诗，以“冬雪”为主题。
</td>
<td>冬日白雪皑皑落，
<br/>寒风凛冽吹不停。
<br/>银装素裹山岗色，
<br/>美景如画在眼前。
</td> </tr>
<tr><td>发表一篇关于母爱的文章，探讨母爱的力量和影响，至少500字。</td>
<td>母爱是世间最伟大的力量之一，它像一股温暖的阳光照耀着我们的心灵深处，让我们感受到生命的美好与温暖。母爱是一种无私的奉献、一种无微不至的关怀，它是人类生命中最为珍贵的财富。
<br/>母亲是我们人生中最重要的人之一，她给予我们生命，抚育我们的成长，陪伴我们在人生的道路上前行。母亲的爱如同春雨般滋润着每一个人的心灵，给我们带来无尽的温馨和感动。无论是在生活中还是工作中，我们都需要母亲的支持和理解，因为她们总是默默地为我们付出一切。
<br/>母爱的影响不仅仅体现在情感上，它还能够改变一个人的命运和生活方式。一个有良好家庭教育的孩子往往会更加自信、勇敢、独立，因为他们在成长的过程中得到了父母的关爱和支持；而缺乏母爱的孩子则往往容易变得自卑、内向、孤僻，甚至产生心理问题。因此，母爱对于孩子的成长和发展具有重要的作用。
<br/>同时，母爱也是推动社会进步的重要因素。在一个家庭中，如果父母都能够以身作则地传递爱心和责任感，那么整个社会的道德水平也会得到提高。这种爱的传承不仅可以帮助孩子们树立正确的人生观和价值观，还可以促进家庭的和谐稳定和社会的稳定发展。
<br/>总之，母爱是人类最美好的礼物之一，它不仅仅是一种情感的表达，更是一种责任的体现。作为子女，我们要学会感恩母亲的恩情，珍惜这份宝贵的亲情；作为家长，我们也要时刻关注子女的身心健康，为他们创造良好的生活和学习环境。只有这样，才能让母爱在我们的生命中发挥出更大的价值和意义。
</td> </tr>
</table>
经过我们的微调，目前TechGPT-2.0对其不知道的问题会直接进行不可回答的回复，对与人类价值观相悖的问题会进行否定回答并给出合理建议。
另外，还需注意，上述结果并非设置为相同的输出长度。在默认输出长度设置下，模型输出的结果偏保守，即很多正确的结果在强制大模型输出更长结果的条件下可以得到，但在默认输出长度设置条件下，这些正确的结果并没有被模型输出；有时也可能因为强制模型输出的长度过长，导致模型过分填充一些无用字符。
## 总结
**TechGPT-2.0** 在许多方面进行了改进，使模型能够拥有更强的知识图谱构建能力尤其是在**嵌套NER**以及**医学、法律**领域能力的提升，并且模型对**幻觉等边界问题以及长文本问题**的回答也得到了改善。
目前，我们对TechGPT的研究也在持续进行中，如外挂知识库、大模型与知识图谱融合、长文本问题优化等方向。
## 主要贡献者
排名不分先后
<table>
<tr>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/86044648?v=4" alt="Contributor 1" height="150">
<br>
<b>wangjiaqi @ Northeastern University</b>
<br>
<a href='https://github.com/wangjiaqi886'>TechGPT-2.0 项目的核心开发成员</a>
</td>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/109963333?v=4" alt="Contributor 2" height="150">
<br>
<b>Chang Yuying @ Northeastern University</b>
<br>
<a href='https://github.com/changyuying'>TechGPT-2.0 项目的核心开发成员</a>
</td>
<td align='center'>
<img src="https://avatars.githubusercontent.com/u/49021250?v=4" alt="Contributor 3" height="150">
<br>
<b>SIGMOID @ Northeastern University</b>
<br>
<a href='https://github.com/lizhongv'>TechGPT-2.0 项目的核心开发成员</a>
</td>
</tr>
</table>
## 免责声明
该项目仅供学习交流使用，禁止用于商业用途。在使用过程中，使用者需认真阅读并遵守以下声明:
1. 本项目仅为大模型测试功能而生，使用者需自行承担风险和责任，如因使用不当而导致的任何损失或伤害，本项目概不负责。
2. 本项目中出现的第三方链接或库仅为提供便利而存在，其内容和观点与本项目无关。使用者在使用时需自行辨别，本项目不承担任何连带责任；
3. 使用者在测试和使用模型时，应遵守相关法律法规，如因使用不当而造成损失的，本项目不承担责任，使用者应自行承担；若项目出现任何错误，请向我方反馈，以助于我们及时修复；
4. 本模型中出现的任何违反法律法规或公序良俗的回答，均不代表本项目观点和立场，我们将不断完善模型回答以使其更符合社会伦理和道德规范。
使用本项目即表示您已经仔细阅读、理解并同意遵守以上免责声明。本项目保留在不预先通知任何人的情况下修改本声明的权利。
## 引用
如果使用本项目的代码、数据或模型，请引用本项目。
```
@misc{TechGPT,
author = {Feiliang Ren, Jiaqi Wang, Yuying Chang, Zhong Li},
title = {TechGPT 2.0: Technology-Oriented Generative Pretrained Transformer 2.0},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/neukg/TechGPT}},
}
```
## 致谢
**我们对 [Chinese-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) 和 [Atom](https://github.com/FlagAlpha/Llama2-Chinese) 的相关项目和研究开发人员表示衷心的感谢！**
```
@article{Chinese-LLaMA-Alpaca,
title={Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca},
author={Cui, Yiming and Yang, Ziqing and Yao, Xin},
journal={arXiv preprint arXiv:2304.08177},
url={https://arxiv.org/abs/2304.08177},
year={2023}
}
```
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EWCH/whisper-large-v3-sl | EWCH | 2023-11-29T15:19:46Z | 0 | 0 | null | [
"tensorboard",
"asr-sl",
"generated_from_trainer",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:19:46Z | 2023-11-26T15:28:04.000Z | null | null | ---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- asr-sl
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: ASR for Slovenian Large
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. -->
# ASR for Slovenian Large
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1684 | 1.0 | 613 | 0.3087 |
| 0.0627 | 2.0 | 1226 | 0.2722 |
| 0.0187 | 3.0 | 1839 | 0.2479 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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mesolitica/malaysian-llama2-7b-32k-instructions-v2 | mesolitica | 2023-11-29T09:21:34Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ms",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:21:34Z | 2023-11-26T15:36:05.000Z | null | null | ---
language:
- ms
---
# Full Parameter Finetuning 7B 32768 context length Mistral on Malaysian instructions dataset
We use exact Llama2 chat template.
WandB, https://wandb.ai/mesolitica/fpf-llama2-7b-hf-instructions-32k-function-call?workspace=user-husein-mesolitica
## how-to
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
def parse_llama_chat(messages, function_call = None):
system = messages[0]['content']
user_query = messages[-1]['content']
users, assistants = [], []
for q in messages[1:-1]:
if q['role'] == 'user':
users.append(q['content'])
elif q['role'] == 'assistant':
assistants.append(q['content'])
texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n']
if function_call:
fs = []
for f in function_call:
f = json.dumps(f, indent=4)
fs.append(f)
fs = '\n\n'.join(fs)
texts.append(f'\n[FUNCTIONCALL]\n{fs}\n')
for u, a in zip(users, assistants):
texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ')
texts.append(f'{user_query.strip()} [/INST]')
prompt = ''.join(texts).strip()
return prompt
TORCH_DTYPE = 'bfloat16'
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
)
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-llama2-7b-32k-instructions')
model = AutoModelForCausalLM.from_pretrained(
'mesolitica/malaysian-llama2-7b-32k-instructions',
use_flash_attention_2 = True,
quantization_config = nf4_config
)
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'kwsp tu apa'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
'<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
kwsp tu apa [/INST] Kumpulan Wang Simpanan Pekerja (KWSP) ialah sebuah badan berkanun yang ditubuhkan di Malaysia yang menguruskan tabung simpanan tetap pekerja bagi tujuan persaraan dan perancangan masa depan. Diasaskan pada tahun 1951, KWSP bertanggungjawab untuk mengumpul dan menguruskan sumber daripada majikan dan pekerja, dan juga menyediakan pelbagai faedah kepada ahli seperti dividen dan akses kepada pengeluaran simpanan pada usia persaraan. KWSP juga memainkan peranan penting dalam menyediakan perlindungan sosial dan pembangunan ekonomi di Malaysia, dengan mempromosikan simpanan pengguna dan meningkatkan kadar celik kewangan dalam kalangan rakyat. </s>'
```
```python
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
awat malaysia ada jabatan koko, malaysia bukan buat keluaq koko banyak pun [/INST] Penanaman koko di Malaysia bermula pada tahun 1950-an, dengan pengeluaran koko kering pertama dihasilkan pada tahun 1958. Pada masa itu, negara ini menjadi pembekal koko terbesar di dunia, dan industri ini terus berkembang dan mewujudkan ribuan pekerjaan dalam pemprosesan dan pengeluaran koko.
Walau bagaimanapun, penurunan harga koko di pasaran antarabangsa pada tahun 1980-an menyebabkan pengeluaran koko Malaysia menurun, dan negara ini telah mengubah tumpuan daripada penanaman koko kepada komoditi lain seperti minyak kelapa sawit dan getah.
Walaupun Malaysia bukan pengeluar koko yang besar, industri koko tempatan masih penting dari segi ekonomi dan sosial. Koko ialah komoditi yang diperdagangkan di bursa tempatan, dan pengeluar koko tempatan menghasilkan koko kering yang dieksport ke seluruh dunia.
Jabatan Koko Malaysia ialah sebuah agensi kerajaan yang bertanggungjawab untuk membangunkan industri koko dan mempromosikan pengeluaran koko. Agensi ini bekerjasama dengan industri untuk meningkatkan produktiviti dan memastikan kualiti produk koko Malaysia. Ia juga menggalakkan usaha pembangunan pekebun kecil untuk mempromosikan industri koko dan mewujudkan peluang pekerjaan dan ekonomi di kawasan pedalaman. </s>
```
```python
f = { "name": "get_exchange_rate", "description": "Get the exchange rate between two currencies", "parameters": { "type": "object", "properties": { "base_currency": { "type": "string", "description": "The currency to convert from" }, "target_currency": { "type": "string", "description": "The currency to convert to" } }, "required": [ "base_currency", "target_currency" ] } }
messages = [
{'role': 'system', 'content': 'anda adalah AI yang sangat membantu'},
{'role': 'user', 'content': 'tolong tukar 10 ringgit ke usd'}
]
prompt = parse_llama_chat(messages, function_call = [f])
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=128,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
<s> [INST] <<SYS>>
anda adalah AI yang sangat membantu
<</SYS>>
[FUNCTIONCALL]
{
"name": "get_exchange_rate",
"description": "Get the exchange rate between two currencies",
"parameters": {
"type": "object",
"properties": {
"base_currency": {
"type": "string",
"description": "The currency to convert from"
},
"target_currency": {
"type": "string",
"description": "The currency to convert to"
}
},
"required": [
"base_currency",
"target_currency"
]
}
}
tolong tukar 10 ringgit ke usd [/INST] <functioncall> {"name": "get_exchange_rate", "arguments": '{"base_currency": "ringgit", "target_currency": "usd"}'} </s>
```
```python
# do follow up from output above,
messages = [
{'role': 'system', 'content': 'anda adalah AI yang sangat membantu'},
{'role': 'user', 'content': 'tolong tukar 10 ringgit ke usd'},
{'role': 'assistant', 'content': tokenizer.decode(r[0]).split('[/INST]')[1].strip().replace('</s>', '')},
{'role': 'user', 'content': 'wow hebatnya'},
]
prompt = parse_llama_chat(messages, function_call = [f])
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=128,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```text
<s> [INST] <<SYS>>
anda adalah AI yang sangat membantu
<</SYS>>
[FUNCTIONCALL]
{
"name": "get_exchange_rate",
"description": "Get the exchange rate between two currencies",
"parameters": {
"type": "object",
"properties": {
"base_currency": {
"type": "string",
"description": "The currency to convert from"
},
"target_currency": {
"type": "string",
"description": "The currency to convert to"
}
},
"required": [
"base_currency",
"target_currency"
]
}
}
tolong tukar 10 ringgit ke usd [/INST] <functioncall> {"name": "get_exchange_rate", "arguments": '{"base_currency": "ringgit", "target_currency": "usd"}'} </s><s> [INST] wow hebatnya [/INST] Terima kasih! Adakah terdapat kadar pertukaran lain yang boleh saya tukar? </s>
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | mesolitica/malaysian-llama2-7b-32k-instructions-v2 | [
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AdrianHR/whisper-small-sv | AdrianHR | 2023-11-29T19:29:13Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:29:13Z | 2023-11-26T15:39:06.000Z | null | null | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-sv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-sv
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3178
- Wer: 112.0916
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3164 | 0.65 | 500 | 0.3268 | 71.7055 |
| 0.1574 | 1.29 | 1000 | 0.2951 | 126.5010 |
| 0.1168 | 1.94 | 1500 | 0.2882 | 62.9857 |
| 0.0678 | 2.59 | 2000 | 0.2879 | 63.6175 |
| 0.0341 | 3.23 | 2500 | 0.2999 | 67.0081 |
| 0.0306 | 3.88 | 3000 | 0.2958 | 78.9842 |
| 0.0112 | 4.53 | 3500 | 0.3186 | 104.4231 |
| 0.0082 | 5.17 | 4000 | 0.3178 | 112.0916 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | AdrianHR/whisper-small-sv | [
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] |
BetterThanNothing/cicada | BetterThanNothing | 2023-11-29T11:47:23Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T11:47:23Z | 2023-11-26T16:04:03.000Z | null | null | ---
license: creativeml-openrail-m
---
# Cicada
great for anime style,capable for handDrawn results and screencap styles, dynamic with contrasting colours.
the name comes from cicada3301.
recommended to use high cfg >8 ,and high steps are optional, the shorter and simple the prompt
the better the image is going to be.
use high-res fix for sharper image.
# example prompts:
`A gorgeous nordic teen girl, pale porcelain skin, face with round puffy cheeks, Nordic braid, platinum hair, (nano-suit), dynamic pose,
8k, high quality, intricate, dramatic lighting, (masterpiece, DOF, vivid), black, white, (orange:0.9), deep blue, light bronze, amber`
`masterpiece, picture of a witch, oil painting, hard brush, highly detailed, 1girl, fiery witch, witch hat, flowing red magic, attractvie
, haunting, beautiful, dynamic Poster by Greg Rutkowski, portrait,
Marilyn Monroe, action pose, red hair, pale skin, highlights in eyes, detailed pupils, luminous effects, highest detailed, floating particles`
`Photorealism a portait of a beautiful dark haired french girl, attractive collarbone and shoulders, holding a mug of hot tea. In the style of ilya kuvshinov, elegant,
highly detailed, photorealistic, digital painting, high resolution, artstation, concept art, smooth, often for highly detailed representation,
, or visual illusion.`
`detailed eyes, Perfect features, (masterpiece),
(best quality), moist skin, shiny skin, glossy skin, (good quality), intricate details, earrings,
Ray Tracing, (See-through), (Bokeh), (Depth of field),`
`(masterpiece, best quality:1.4), (ultra realistic:1.4), (8k uhd, very high quality:1.4), (extremely detailed face:1.4),
(dark skin and muscular mother holding her dark skin female child:1.4),
(dark caramel skin:1.4), (absurdly long ruby red hair:1.4), (smiling and happy:1.4), (bright blue eyes:1.4), (city background:1.4)`
`masterpiece,best quality,highres,cinematic lighting,dramatic angle,1girl,brown hair,blue eyes,ahoge,hairband,looking back at viewer,turning head,
,purple dress,drinking with straw,handbag,dating,crowd,sitting,chair and desk,sunshade,depth of field,streetscape,close-up,portrait,happy,`
# a different type of prompt
`Title: "Whimsical Watercolors: The Serene Elegance of a Woman in a Pastel Dreamscape",
ArtStyle: Watercolor Painting Style Drawing,
Texture: Soft and fluid,characteristic of watercolor techniques,with washes of color and gentle gradations,
Color Palette: Delicate and dreamy pastels,with an emphasis on light blues,pinks,lavenders,and soft greens,
SceneDescription:,
Subject: A woman portrayed as an embodiment of grace and tranquility within a watercolor landscape,
Appearance: Softly defined features,with a harmonious blend of colors creating a gentle,ethereal look,
Expression: A calm,reflective smile,suggesting a deep inner peace and contentment,
Attire: A flowing dress with subtle,watercolor hues,appearing as if the colors are melting into the surrounding landscape,
Action: A relaxed,graceful pose,perhaps sitting by a watercolor-rendered river or walking through a field of softly painted flowers,
Setting: A picturesque,watercolor environment,
Elements: Dreamlike interpretations of nature,such as a blurry field of flowers,a gentle stream,or a distant,misty forest,
Details: Soft splashes of color and undefined shapes that merge into one another,typical of watercolor landscapes,
Lighting: Soft,diffused light that enhances the watercolor effect,creating a warm,gentle ambiance,
Additional Details:,
Symbolic Elements: Subtle inclusions like a sketchbook,a feather,or a softly glowing lantern,adding layers of meaning to the scene,
Atmospheric Quality: Hints of color bleeding or watercolor drips,emphasizing the artistic medium,
Mood: A serene and enchanting portrayal,capturing the quiet beauty and soothing essence of a woman in a whimsical,pastel watercolor world,`
`Portrait of a very sinister looking Batman with a scary pose, cinematic 4k epic detailed bokeh hbo dark
moodyPortrait of a very sinister looking Batman with a scary pose, cinematic 4k epic detailed bokeh hbo dark moody`
`,1girl, blond hair,long hair ,blue eyes, Beautiful blue eyes,Violet Evergarden,
smirk,
pirate,pirate woman, eye pad, eyepatch,black eyepatch,0Skull mark on hat,saber,hold up a saber,Black Gloves,
mini-skirt,
on board a ship,pirate ship,Black flag with skull,pirate flag,`
any basic prompt can work, no triggered words are required;
works good with loras
merged models:
nostalgia mix.
falt2d animerge.
forgottenmix.
exo.
aux.
pika's new generation.
toonyoujp.
# results
| null | null | null | null | null | null | null | null | null | null | null | null | BetterThanNothing/cicada | [
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mahh1/rudolph_2.7B_service_tuned | mahh1 | 2023-11-29T19:37:26Z | 0 | 0 | null | [
"pytorch",
"ru",
"license:mit",
"region:us"
] | 2023-11-29T19:37:26Z | 2023-11-26T16:39:34.000Z | null | null | ---
license: mit
language:
- ru
---
### Tuned model VQA task to service sector | null | null | null | null | null | null | null | null | null | null | null | null | mahh1/rudolph_2.7B_service_tuned | [
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ernlavr/llama2-7bn-4bit-xsum | ernlavr | 2023-11-30T00:05:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:05:45Z | 2023-11-26T18:35:37.000Z | null | null | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama2-7bn-4bit-xsum
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. -->
# llama2-7bn-4bit-xsum
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 177
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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openerotica/cockatrice-7b-v0.1-gguf | openerotica | 2023-11-30T01:18:51Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | 2023-11-30T01:18:51Z | 2023-11-26T18:40:34.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | openerotica/cockatrice-7b-v0.1-gguf | [
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Enferlain/218xl | Enferlain | 2023-11-29T16:28:58Z | 0 | 2 | null | [
"safetensors",
"art",
"anime",
"stable-diffusion",
"SDXL",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T16:28:58Z | 2023-11-26T18:54:20.000Z | null | null | ---
license: creativeml-openrail-m
language:
- en
pipeline_tag: text-to-image
tags:
- art
- anime
- stable-diffusion
- SDXL
library_name: safetensors
---
<style>
.title-container {
display: flex;
justify-content: center;
align-items: center;
height: 100vh; /* Adjust this value to position the title vertically */
}
.title {
font-size: 2.5em;
text-align: center;
color: #d6767f;
font-family: 'Roboto', sans-serif;
text-transform: uppercase;
letter-spacing: 0.11em;
padding: 0.218em 0;
background: transparent;
}
.title span {
background: -webkit-linear-gradient(69deg, #130c28, #2a4aee);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.custom-table {
table-layout: fixed;
width: 100%;
border-collapse: collapse;
margin-top: 2em;
}
.custom-table td {
width: 50%;
vertical-align: top;
padding: 10px;
box-shadow: 0px 0px 0px 0px rgba(0, 0, 0, 0.15);
}
.custom-image-container {
position: relative;
width: 100%;
margin-bottom: 0em;
overflow: hidden;
border-radius: 10px;
transition: transform .7s;
/* Smooth transition for the container */
}
.custom-image-container:hover {
transform: scale(1.05);
/* Scale the container on hover */
}
.custom-image-container:hover .custom-image {
opacity: 0
}
.custom-image-container:hover
.hover-image {
opacity: 1;
}
.custom-image {
width: 100%;
height: auto;
object-fit: cover;
border-radius: 10px;
transition: transform .7s, opacity .5s;
margin-bottom: 0em;
}
.hover-image {
position: absolute;
top: 0;
left: 0;
opacity: 0;
transition: opacity .5s;
}
.nsfw-filter {
filter: blur(8px); /* Apply a blur effect */
transition: filter 0.3s ease; /* Smooth transition for the blur effect */
}
.custom-image-container:hover .nsfw-filter {
filter: none; /* Remove the blur effect on hover */
}
.overlay {
position: absolute;
bottom: 0;
left: 0;
right: 0;
color: white;
width: 100%;
height: 40%;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
font-size: 1vw;
font-style: bold;
text-align: center;
opacity: 0;
/* Keep the text fully opaque */
background: linear-gradient(0deg, rgba(0, 0, 0, 0.6) 60%, rgba(0, 0, 0, 0) 100%);
transition: opacity .5s;
}
.custom-image-container:hover .overlay {
opacity: 1;
/* Make the overlay always visible */
}
.overlay-text {
background: linear-gradient(45deg, #130c28, #ca76e2);
-webkit-background-clip: text;
color: transparent;
/* Fallback for browsers that do not support this effect */
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);
/* Enhanced text shadow for better legibility */
.overlay-subtext {
font-size: 0.75em;
margin-top: 0.5em;
font-style: italic;
}
.overlay,
.overlay-subtext {
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5);
}
</style>
<h1 class="title">
<span>∞ 218xl ∞</span>
</h1>
<table class="custom-table">
<tr>
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb1.png" alt="sample1">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xl1.png" alt="sampleb1">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Merry Christmas <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by ziyun"</div></div>
</div>
</div>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb2.png" alt="sample2">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xll6.png" alt="sampleb2">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Sunflower <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by poire"</div></div>
</div>
</td>
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb3.png" alt="sample3">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xll5.png" alt="sampleb3">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Light meal <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by ziyun"</div></div>
</div>
</div>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb4.png" alt="sample4">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xl27.png" alt="sampleb4">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Rice Shower <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by ziyun"</div></div>
</td>
<td>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb5.png" alt="sample5">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xl37.png" alt="sampleb5">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Remilia Scarlet <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by ziyun"</div></div>
</div>
</div>
<div class="custom-image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xlb6.png" alt="sample6">
<img class="hover-image" src="https://huggingface.co/Enferlain/218xl/resolve/main/images/218xll2.png" alt="sampleb6">
<div class="overlay" style="font-size: 1vw; font-style: bold;"> Y-yes? <div class="overlay-subtext" style="font-size: 0.75em; font-style: italic;">"by ask"</div></div>
</div>
</td>
</tr>
</table>
<p align="center", style="font-size: 1.2rem; font-style:bold"><strong><a href="https://huggingface.co/Enferlain/218xl/resolve/main/218xl.safetensors?download=true"</a></strong> Download</p>
# ∞ 218 SDXL aka 'counterproductive illustration' omE
I only recently started dabbling in SDXL models, but I already put a lot of testing into it. Mostly comparisons, testing loras, trying to fix some shortcomings in models I liked.
I decided on a model called ReproductionXL because it looked the most similar to what I was used to, but better.
After that I merged CounterfeitXL because Counterfeit aesthetics are still one of if not the best.
Then I merged that result with Anime Illust Diffusion XL as I found the idea of being able to use different artstyles with tags fun -- and it is a lot of fun.
This took around 2 days of manual scoring and guidance through a Bayesian process, usually 500 samples per merge, which I did about 7 times total to arrive here.
Also merged a LoRA trained on 18000 images at .4 weight into the final result, aimed at fixing sdxl models' lack of knowledge about the 4 letter word.
And so there you have it, that's the current point in time.
Onwards I'm looking to see how I can use Lynn's Animagine XL 2 and upgrade the main ingredients with their latest updates.
I'm looking to add multiple characters (animagine should help with that) and more styles that aren't currently present within the model.
<hr>
<details id="Dropdown">
<summary style="font-size: 1.10em"><strong> Changelogs </strong> (click to open the dropdown)</summary>
### 11 29 2023
- Yoinked layout
- Added images
- Uploaded 218xl
</details>
<hr>
# ∞ Usage
So this is a bit different compared to usual models. You can still prompt as normal, but there is a fun quirk thanks to Anime Illust Diffusion.
There is a [wildcard text file](https://huggingface.co/Enferlain/218xl/blob/main/artistsAI.txt) in the repo that contains all the tags that call for their respective artstyles, try and play around with that.
You can call it with ```__artistsAI__```
There is also a [wildcard](https://huggingface.co/Enferlain/218xl/blob/main/artists218.txt) with artists that I personally liked in this model, ```__artists218__```, but this is a work in progress.
Artist xyzs thanks to [b74](https://huggingface.co/b74444) from the Touhou [discord](https://discord.com/invite/touhouai)
[One](https://huggingface.co/b74444/style1/blob/main/list1.png)
[Two](https://huggingface.co/b74444/style1/blob/main/list2.png)
#### Embeddings Downloads
- [**aidxlv05_neg**](https://civitai.com/models/144327/negative-embedding-for-sdxl-based-anime-models), [Direct download](https://civitai.com/api/download/models/195614)
- [**unaestheticXL_Sky3.1**](https://civitai.com/models/119032?modelVersionId=175819), [Direct download](https://civitai.com/api/download/models/175819?type=Model&format=SafeTensor)
#### VAE
- [**sdxl_vae**](https://huggingface.co/stabilityai/sdxl-vae/blob/main/sdxl_vae.safetensors) (official sdxl vae)
# ∞ Credits
- Checkpoints
- [**ReproductionXL v8.7**](https://civitai.com/models/118729?modelVersionId=199943)
- [**CounterfeitXL v1.0**](https://civitai.com/models/118406?modelVersionId=146761)
- [**Anime Illust Diffusion v0.52**](https://civitai.com/models/124189/anime-illust-diffusion-xl?modelVersionId=238771)
- Lynn for layout and future juice
- [**Animagine XL 2**](https://huggingface.co/Linaqruf/animagine-xl-2.0)
- Ashtaka Aha Oof for taking my spam on discord EZ Clap
- [**Ahnn**](https://huggingface.co/AshtakaOOf/Ahnn)
- Tools
- [**Bayesian merger by s1dlx**](https://github.com/s1dlx/sd-webui-bayesian-merger/)
- [**LoRA_Easy_Training_Scripts by Derrian**](https://github.com/derrian-distro/LoRA_Easy_Training_Scripts)
## ∞ Legal deez
```
You are free to use this model locally but
1. You aren't allowed to redistribute on another platform. (like CivitAI or Tensor.Art)
2. I am not responsible for how this model is used to generate images.
``` | null | safetensors | text-to-image | null | null | null | null | null | null | null | null | null | Enferlain/218xl | [
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mlc-ai/mlc-chat-OpenHermes-2.5-Mistral-7B-q4f16_1 | mlc-ai | 2023-11-29T22:02:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:02:52Z | 2023-11-26T21:28:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mlc-ai/mlc-chat-OpenHermes-2.5-Mistral-7B-q4f16_1 | [
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mlc-ai/mlc-chat-OpenHermes-2.5-Mistral-7B-q4f32_1 | mlc-ai | 2023-11-29T22:01:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:01:38Z | 2023-11-26T21:40:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mlc-ai/mlc-chat-OpenHermes-2.5-Mistral-7B-q4f32_1 | [
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jerawincel/roberta-base-finetuned | jerawincel | 2023-11-29T10:12:58Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:12:58Z | 2023-11-26T22:54:42.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | jerawincel/roberta-base-finetuned | [
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Mariaaaaa/vicuna-7b-fine-tuning_SST2_20_16 | Mariaaaaa | 2023-11-29T19:41:52Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T19:41:52Z | 2023-11-26T23:02:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mariaaaaa/vicuna-7b-fine-tuning_SST2_20_16 | [
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MidhunKanadan/llama-2-7b-cypher-finetuned | MidhunKanadan | 2023-11-29T11:00:25Z | 0 | 0 | null | [
"peft",
"pytorch",
"region:us"
] | 2023-11-29T11:00:25Z | 2023-11-26T23:17:28.000Z | null | null | ---
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
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codegood/HF_AWS_Mistral_SC | codegood | 2023-11-30T00:45:32Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:amazon/MistralLite",
"region:us"
] | 2023-11-30T00:45:32Z | 2023-11-27T02:26:40.000Z | null | null | ---
library_name: peft
base_model: amazon/MistralLite
---
# 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]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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
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### 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
## 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 0.6.2
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aicam2/llama2-7b-aicam2 | aicam2 | 2023-11-29T03:20:57Z | 0 | 0 | null | [
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2023-11-29T03:20:57Z | 2023-11-27T03:36:24.000Z | null | null | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: llama2-7b-aicam2
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. -->
# llama2-7b-aicam2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the generator 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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openbmb/RLHF-V_v0 | openbmb | 2023-11-29T13:16:10Z | 0 | 1 | null | [
"transformers",
"pytorch",
"beit3_llava",
"text-generation",
"en",
"dataset:Yirany/UniMM-Chat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:16:10Z | 2023-11-27T03:42:11.000Z | null | null | ---
license: apache-2.0
datasets:
- Yirany/UniMM-Chat
language:
- en
library_name: transformers
---
readme.md | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | openbmb/RLHF-V_v0 | [
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RR6/my_awesome_eli5_mlm_model | RR6 | 2023-11-29T05:47:31Z | 0 | 0 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:47:31Z | 2023-11-27T04:24:16.000Z | null | null | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_keras_callback
model-index:
- name: RR6/my_awesome_eli5_mlm_model
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. -->
# RR6/my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8772
- Validation Loss: 1.7423
- Epoch: 2
## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0277 | 1.7980 | 0 |
| 1.9373 | 1.7732 | 1 |
| 1.8772 | 1.7423 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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mahsamassoud/ddpm-butterflies-128 | mahsamassoud | 2023-11-29T21:31:47Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T21:31:47Z | 2023-11-27T04:29:53.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mahsamassoud/ddpm-butterflies-128 | [
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chaoscodes/starcoder | chaoscodes | 2023-11-29T16:13:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:13:55Z | 2023-11-27T05:28:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chaoscodes/starcoder | [
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SiRoZaRuPa/1B-1126-clean | SiRoZaRuPa | 2023-11-29T14:43:59Z | 0 | 0 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:43:59Z | 2023-11-27T05:59:13.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
- generated_from_trainer
datasets:
- audiofolder
model-index:
- name: 1B-1126-clean
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. -->
# 1B-1126-clean
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0303
- Cer: 0.0274
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1600
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3423 | 1.6 | 500 | 4.8660 | 0.9620 |
| 0.2937 | 3.2 | 1000 | 4.0211 | 0.9203 |
| 0.2154 | 4.8 | 1500 | 4.7682 | 0.9868 |
| 0.1618 | 6.4 | 2000 | 5.4428 | 0.9905 |
| 0.1395 | 8.0 | 2500 | 5.9375 | 0.9894 |
| 0.1179 | 9.6 | 3000 | 5.5084 | 0.9553 |
| 0.1073 | 11.2 | 3500 | 5.6132 | 0.9750 |
| 0.0907 | 12.8 | 4000 | 5.4367 | 0.9862 |
| 0.0836 | 14.4 | 4500 | 5.5723 | 0.9540 |
| 0.0845 | 16.0 | 5000 | 5.3288 | 0.9726 |
| 0.0757 | 17.6 | 5500 | 4.6148 | 0.9574 |
| 0.0726 | 19.2 | 6000 | 3.7586 | 0.8945 |
| 0.069 | 20.8 | 6500 | 3.1709 | 0.8384 |
| 0.0637 | 22.4 | 7000 | 1.9729 | 0.5768 |
| 0.0648 | 24.0 | 7500 | 0.2142 | 0.0773 |
| 0.0649 | 25.6 | 8000 | 0.2289 | 0.0823 |
| 0.062 | 27.2 | 8500 | 0.1730 | 0.0627 |
| 0.0584 | 28.8 | 9000 | 0.1367 | 0.0569 |
| 0.0543 | 30.4 | 9500 | 0.0743 | 0.0408 |
| 0.0585 | 32.0 | 10000 | 0.0809 | 0.0421 |
| 0.0517 | 33.6 | 10500 | 0.0589 | 0.0363 |
| 0.0508 | 35.2 | 11000 | 0.0742 | 0.0396 |
| 0.0502 | 36.8 | 11500 | 0.0618 | 0.0371 |
| 0.0463 | 38.4 | 12000 | 0.0625 | 0.0353 |
| 0.0478 | 40.0 | 12500 | 0.0568 | 0.0343 |
| 0.0457 | 41.6 | 13000 | 0.0546 | 0.0347 |
| 0.0453 | 43.2 | 13500 | 0.0442 | 0.0317 |
| 0.0429 | 44.8 | 14000 | 0.0533 | 0.0341 |
| 0.041 | 46.4 | 14500 | 0.0453 | 0.0327 |
| 0.0442 | 48.0 | 15000 | 0.0500 | 0.0335 |
| 0.0382 | 49.6 | 15500 | 0.0466 | 0.0329 |
| 0.0419 | 51.2 | 16000 | 0.0412 | 0.0322 |
| 0.0379 | 52.8 | 16500 | 0.0367 | 0.0303 |
| 0.0389 | 54.4 | 17000 | 0.0360 | 0.0302 |
| 0.0394 | 56.0 | 17500 | 0.0404 | 0.0308 |
| 0.0365 | 57.6 | 18000 | 0.0431 | 0.0309 |
| 0.0355 | 59.2 | 18500 | 0.0365 | 0.0299 |
| 0.0332 | 60.8 | 19000 | 0.0408 | 0.0314 |
| 0.0326 | 62.4 | 19500 | 0.0336 | 0.0297 |
| 0.032 | 64.0 | 20000 | 0.0349 | 0.0302 |
| 0.0318 | 65.6 | 20500 | 0.0342 | 0.0294 |
| 0.0328 | 67.2 | 21000 | 0.0331 | 0.0297 |
| 0.0303 | 68.8 | 21500 | 0.0349 | 0.0293 |
| 0.0294 | 70.4 | 22000 | 0.0331 | 0.0295 |
| 0.0309 | 72.0 | 22500 | 0.0307 | 0.0289 |
| 0.0279 | 73.6 | 23000 | 0.0317 | 0.0288 |
| 0.0259 | 75.2 | 23500 | 0.0313 | 0.0283 |
| 0.0282 | 76.8 | 24000 | 0.0325 | 0.0292 |
| 0.026 | 78.4 | 24500 | 0.0305 | 0.0286 |
| 0.0262 | 80.0 | 25000 | 0.0326 | 0.0285 |
| 0.0255 | 81.6 | 25500 | 0.0321 | 0.0284 |
| 0.026 | 83.2 | 26000 | 0.0347 | 0.0290 |
| 0.0248 | 84.8 | 26500 | 0.0293 | 0.0280 |
| 0.0243 | 86.4 | 27000 | 0.0324 | 0.0284 |
| 0.0246 | 88.0 | 27500 | 0.0308 | 0.0285 |
| 0.023 | 89.6 | 28000 | 0.0302 | 0.0283 |
| 0.0222 | 91.2 | 28500 | 0.0290 | 0.0283 |
| 0.0231 | 92.8 | 29000 | 0.0293 | 0.0282 |
| 0.0227 | 94.4 | 29500 | 0.0298 | 0.0282 |
| 0.0216 | 96.0 | 30000 | 0.0308 | 0.0282 |
| 0.0199 | 97.6 | 30500 | 0.0285 | 0.0278 |
| 0.0211 | 99.2 | 31000 | 0.0290 | 0.0277 |
| 0.0192 | 100.8 | 31500 | 0.0301 | 0.0280 |
| 0.0196 | 102.4 | 32000 | 0.0302 | 0.0277 |
| 0.0203 | 104.0 | 32500 | 0.0307 | 0.0278 |
| 0.0191 | 105.6 | 33000 | 0.0294 | 0.0279 |
| 0.0191 | 107.2 | 33500 | 0.0309 | 0.0278 |
| 0.0185 | 108.8 | 34000 | 0.0338 | 0.0281 |
| 0.0174 | 110.4 | 34500 | 0.0321 | 0.0281 |
| 0.0174 | 112.0 | 35000 | 0.0325 | 0.0280 |
| 0.0167 | 113.6 | 35500 | 0.0324 | 0.0279 |
| 0.0174 | 115.2 | 36000 | 0.0301 | 0.0278 |
| 0.0168 | 116.8 | 36500 | 0.0317 | 0.0277 |
| 0.0161 | 118.4 | 37000 | 0.0312 | 0.0278 |
| 0.0168 | 120.0 | 37500 | 0.0303 | 0.0275 |
| 0.0163 | 121.6 | 38000 | 0.0302 | 0.0277 |
| 0.0161 | 123.2 | 38500 | 0.0305 | 0.0278 |
| 0.0159 | 124.8 | 39000 | 0.0319 | 0.0278 |
| 0.0163 | 126.4 | 39500 | 0.0302 | 0.0275 |
| 0.0171 | 128.0 | 40000 | 0.0315 | 0.0276 |
| 0.0167 | 129.6 | 40500 | 0.0306 | 0.0275 |
| 0.0164 | 131.2 | 41000 | 0.0302 | 0.0275 |
| 0.0157 | 132.8 | 41500 | 0.0306 | 0.0276 |
| 0.0155 | 134.4 | 42000 | 0.0304 | 0.0276 |
| 0.0154 | 136.0 | 42500 | 0.0305 | 0.0275 |
| 0.0171 | 137.6 | 43000 | 0.0299 | 0.0275 |
| 0.0154 | 139.2 | 43500 | 0.0300 | 0.0275 |
| 0.0155 | 140.8 | 44000 | 0.0300 | 0.0273 |
| 0.0159 | 142.4 | 44500 | 0.0304 | 0.0274 |
| 0.0157 | 144.0 | 45000 | 0.0304 | 0.0274 |
| 0.0149 | 145.6 | 45500 | 0.0303 | 0.0273 |
| 0.0161 | 147.2 | 46000 | 0.0303 | 0.0274 |
| 0.0164 | 148.8 | 46500 | 0.0303 | 0.0274 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | SiRoZaRuPa/1B-1126-clean | [
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Icchan/hololive | Icchan | 2023-11-29T17:59:42Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T17:59:42Z | 2023-11-27T06:27:49.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Icchan/hololive | [
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nmnghia02/output | nmnghia02 | 2023-11-29T04:55:35Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:55:35Z | 2023-11-27T07:49:18.000Z | null | null | Entry not found | null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | nmnghia02/output | [
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wanderer2k1/mammoth-7b-intruct-vietnamese | wanderer2k1 | 2023-11-29T18:41:56Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T18:41:56Z | 2023-11-27T09:13:04.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | wanderer2k1/mammoth-7b-intruct-vietnamese | [
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xavierbarbier/deberta-v3-base-kaggle-llm-science-exam | xavierbarbier | 2023-11-29T10:56:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:56:57Z | 2023-11-27T11:18:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | xavierbarbier/deberta-v3-base-kaggle-llm-science-exam | [
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0.4404810965061188,
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0.1408473551273346,
0.8338427543640137,
0.3126058280467987,
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0.269897997379303,
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0.3101596236228943,
-1.0262155532836914
] |
MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 | MoritzLaurer | 2023-11-29T19:21:02Z | 0 | 0 | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:21:02Z | 2023-11-27T12:35:25.000Z | null | null | ---
language:
- en
tags:
- text-classification
- zero-shot-classification
pipeline_tag: zero-shot-classification
library_name: transformers
license: mit
---
# Model description: deberta-v3-large-zeroshot-v1.1-all-33
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
(`entailment` vs. `not_entailment`).
This task format is based on the Natural Language Inference task (NLI).
The task is so universal that any classification task can be reformulated into this task.
A detailed description of how the model was trained and how it can be used is available in this paper: [link to be added]
## Training data
The model was trained on a mixture of __33 datasets and 387 classes__ that have been reformatted into this universal format.
1. Five NLI datasets with ~885k texts: "mnli", "anli", "fever", "wanli", "ling"
2. 28 classification tasks reformatted into the universal NLI format. ~51k cleaned texts were used to avoid overfitting:
'amazonpolarity', 'imdb', 'appreviews', 'yelpreviews', 'rottentomatoes',
'emotiondair', 'emocontext', 'empathetic',
'financialphrasebank', 'banking77', 'massive',
'wikitoxic_toxicaggregated', 'wikitoxic_obscene', 'wikitoxic_threat', 'wikitoxic_insult', 'wikitoxic_identityhate',
'hateoffensive', 'hatexplain', 'biasframes_offensive', 'biasframes_sex', 'biasframes_intent',
'agnews', 'yahootopics',
'trueteacher', 'spam', 'wellformedquery',
'manifesto', 'capsotu'.
See details on each dataset here: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
Note that compared to other NLI models, this model predicts two classes (`entailment` vs. `not_entailment`)
as opposed to three classes (entailment/neutral/contradiction)
The model was only trained on English data. For __multilingual use-cases__,
I recommend machine translating texts to English with libraries like [EasyNMT](https://github.com/UKPLab/EasyNMT).
English-only models tend to perform better than multilingual models and
validation with English data can be easier if you don't speak all languages in your corpus.
### How to use the model
#### Simple zero-shot classification pipeline
```python
#!pip install transformers[sentencepiece]
from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "This example is about {}"
classes_verbalized = ["politics", "economy", "entertainment", "environment"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
print(output)
```
### Details on data and training
The code for preparing the data and training & evaluating the model is fully open-source here: https://github.com/MoritzLaurer/zeroshot-classifier/tree/main
Hyperparameters and other details are available in this Weights & Biases repo: https://wandb.ai/moritzlaurer/deberta-v3-large-zeroshot-v1-1-all-33/table?workspace=user-
## Metrics
Balanced accuracy is reported for all datasets.
`deberta-v3-large-zeroshot-v1.1-all-33` was trained on all datasets, with only maximum 500 texts per class to avoid overfitting.
The metrics on these datasets are therefore not strictly zeroshot, as the model has seen some data for each task during training.
`deberta-v3-large-zeroshot-v1.1-heldout` indicates zeroshot performance on the respective dataset.
To calculate these zeroshot metrics, the pipeline was run 28 times, each time with one dataset held out from training to simulate a zeroshot setup.
| | deberta-v3-large-mnli-fever-anli-ling-wanli-binary | deberta-v3-large-zeroshot-v1.1-heldout | deberta-v3-large-zeroshot-v1.1-all-33 |
|:---------------------------|----------------------------:|-----------------------------------------:|----------------------------------------:|
| datasets mean (w/o nli) | 64.1 | 73.4 | 85.2 |
| amazonpolarity (2) | 94.7 | 96.6 | 96.8 |
| imdb (2) | 90.3 | 95.2 | 95.5 |
| appreviews (2) | 93.6 | 94.3 | 94.7 |
| yelpreviews (2) | 98.5 | 98.4 | 98.9 |
| rottentomatoes (2) | 83.9 | 90.5 | 90.8 |
| emotiondair (6) | 49.2 | 42.1 | 72.1 |
| emocontext (4) | 57 | 69.3 | 82.4 |
| empathetic (32) | 42 | 34.4 | 58 |
| financialphrasebank (3) | 77.4 | 77.5 | 91.9 |
| banking77 (72) | 29.1 | 52.8 | 72.2 |
| massive (59) | 47.3 | 64.7 | 77.3 |
| wikitoxic_toxicaggreg (2) | 81.6 | 86.6 | 91 |
| wikitoxic_obscene (2) | 85.9 | 91.9 | 93.1 |
| wikitoxic_threat (2) | 77.9 | 93.7 | 97.6 |
| wikitoxic_insult (2) | 77.8 | 91.1 | 92.3 |
| wikitoxic_identityhate (2) | 86.4 | 89.8 | 95.7 |
| hateoffensive (3) | 62.8 | 66.5 | 88.4 |
| hatexplain (3) | 46.9 | 61 | 76.9 |
| biasframes_offensive (2) | 62.5 | 86.6 | 89 |
| biasframes_sex (2) | 87.6 | 89.6 | 92.6 |
| biasframes_intent (2) | 54.8 | 88.6 | 89.9 |
| agnews (4) | 81.9 | 82.8 | 90.9 |
| yahootopics (10) | 37.7 | 65.6 | 74.3 |
| trueteacher (2) | 51.2 | 54.9 | 86.6 |
| spam (2) | 52.6 | 51.8 | 97.1 |
| wellformedquery (2) | 49.9 | 40.4 | 82.7 |
| manifesto (56) | 10.6 | 29.4 | 44.1 |
| capsotu (21) | 23.2 | 69.4 | 74 |
| mnli_m (2) | 93.1 | nan | 93.1 |
| mnli_mm (2) | 93.2 | nan | 93.2 |
| fevernli (2) | 89.3 | nan | 89.5 |
| anli_r1 (2) | 87.9 | nan | 87.3 |
| anli_r2 (2) | 76.3 | nan | 78 |
| anli_r3 (2) | 73.6 | nan | 74.1 |
| wanli (2) | 82.8 | nan | 82.7 |
| lingnli (2) | 90.2 | nan | 89.6 |
## Limitations and bias
The model can only do text classification tasks.
Please consult the original DeBERTa paper and the papers for the different datasets for potential biases.
## License
The base model (DeBERTa-v3) is published under the MIT license.
The datasets the model was fine-tuned on are published under a diverse set of licenses.
The following table provides an overview of the non-NLI datasets used for fine-tuning,
information on licenses, the underlying papers etc.: https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv
## Citation
If you use this model academically, please cite:
```
@article{laurer_less_2023,
title = {Less {Annotating}, {More} {Classifying}: {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT}-{NLI}},
issn = {1047-1987, 1476-4989},
shorttitle = {Less {Annotating}, {More} {Classifying}},
url = {https://www.cambridge.org/core/product/identifier/S1047198723000207/type/journal_article},
doi = {10.1017/pan.2023.20},
language = {en},
urldate = {2023-06-20},
journal = {Political Analysis},
author = {Laurer, Moritz and Van Atteveldt, Wouter and Casas, Andreu and Welbers, Kasper},
month = jun,
year = {2023},
pages = {1--33},
}
```
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers can have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.
### Hypotheses used for classification
The hypotheses in the tables below were used to fine-tune the model.
Inspecting them can help users get a feeling for which type of hypotheses and tasks the model was trained on.
You can formulate your own hypotheses by changing the `hypothesis_template` of the zeroshot pipeline. For example:
```python
from transformers import pipeline
text = "Angela Merkel is a politician in Germany and leader of the CDU"
hypothesis_template = "Merkel is the leader of the party: {}"
classes_verbalized = ["CDU", "SPD", "Greens"]
zeroshot_classifier = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33")
output = zeroshot_classifier(text, classes_verbalised, hypothesis_template=hypothesis_template, multi_label=False)
print(output)
```
Note that a few rows in the `massive` and `banking77` datasets contain `nan` because some classes were so ambiguous/unclear that I excluded them from the data.
#### wellformedquery
| label | hypothesis |
|:----------------|:-----------------------------------------------|
| not_well_formed | This example is not a well formed Google query |
| well_formed | This example is a well formed Google query. |
#### biasframes_sex
| label | hypothesis |
|:--------|:-----------------------------------------------------------|
| not_sex | This example does not contain allusions to sexual content. |
| sex | This example contains allusions to sexual content. |
#### biasframes_intent
| label | hypothesis |
|:-----------|:-----------------------------------------------------------------|
| intent | The intent of this example is to be offensive/disrespectful. |
| not_intent | The intent of this example is not to be offensive/disrespectful. |
#### biasframes_offensive
| label | hypothesis |
|:--------------|:-------------------------------------------------------------------------|
| not_offensive | This example could not be considered offensive, disrespectful, or toxic. |
| offensive | This example could be considered offensive, disrespectful, or toxic. |
#### financialphrasebank
| label | hypothesis |
|:---------|:--------------------------------------------------------------------------|
| negative | The sentiment in this example is negative from an investor's perspective. |
| neutral | The sentiment in this example is neutral from an investor's perspective. |
| positive | The sentiment in this example is positive from an investor's perspective. |
#### rottentomatoes
| label | hypothesis |
|:---------|:-----------------------------------------------------------------------|
| negative | The sentiment in this example rotten tomatoes movie review is negative |
| positive | The sentiment in this example rotten tomatoes movie review is positive |
#### amazonpolarity
| label | hypothesis |
|:---------|:----------------------------------------------------------------|
| negative | The sentiment in this example amazon product review is negative |
| positive | The sentiment in this example amazon product review is positive |
#### imdb
| label | hypothesis |
|:---------|:------------------------------------------------------------|
| negative | The sentiment in this example imdb movie review is negative |
| positive | The sentiment in this example imdb movie review is positive |
#### appreviews
| label | hypothesis |
|:---------|:------------------------------------------------------|
| negative | The sentiment in this example app review is negative. |
| positive | The sentiment in this example app review is positive. |
#### yelpreviews
| label | hypothesis |
|:---------|:-------------------------------------------------------|
| negative | The sentiment in this example yelp review is negative. |
| positive | The sentiment in this example yelp review is positive. |
#### wikitoxic_toxicaggregated
| label | hypothesis |
|:--------------------|:----------------------------------------------------------------|
| not_toxicaggregated | This example wikipedia comment does not contain toxic language. |
| toxicaggregated | This example wikipedia comment contains toxic language. |
#### wikitoxic_obscene
| label | hypothesis |
|:------------|:------------------------------------------------------------------|
| not_obscene | This example wikipedia comment does not contain obscene language. |
| obscene | This example wikipedia comment contains obscene language. |
#### wikitoxic_threat
| label | hypothesis |
|:-----------|:----------------------------------------------------------|
| not_threat | This example wikipedia comment does not contain a threat. |
| threat | This example wikipedia comment contains a threat. |
#### wikitoxic_insult
| label | hypothesis |
|:-----------|:-----------------------------------------------------------|
| insult | This example wikipedia comment contains an insult. |
| not_insult | This example wikipedia comment does not contain an insult. |
#### wikitoxic_identityhate
| label | hypothesis |
|:-----------------|:---------------------------------------------------------------|
| identityhate | This example wikipedia comment contains identity hate. |
| not_identityhate | This example wikipedia comment does not contain identity hate. |
#### hateoffensive
| label | hypothesis |
|:------------|:------------------------------------------------------------------------|
| hate_speech | This example tweet contains hate speech. |
| neither | This example tweet contains neither offensive language nor hate speech. |
| offensive | This example tweet contains offensive language without hate speech. |
#### hatexplain
| label | hypothesis |
|:------------|:-------------------------------------------------------------------------------------------|
| hate_speech | This example text from twitter or gab contains hate speech. |
| neither | This example text from twitter or gab contains neither offensive language nor hate speech. |
| offensive | This example text from twitter or gab contains offensive language without hate speech. |
#### spam
| label | hypothesis |
|:---------|:------------------------------|
| not_spam | This example sms is not spam. |
| spam | This example sms is spam. |
#### emotiondair
| label | hypothesis |
|:---------|:---------------------------------------------------|
| anger | This example tweet expresses the emotion: anger |
| fear | This example tweet expresses the emotion: fear |
| joy | This example tweet expresses the emotion: joy |
| love | This example tweet expresses the emotion: love |
| sadness | This example tweet expresses the emotion: sadness |
| surprise | This example tweet expresses the emotion: surprise |
#### emocontext
| label | hypothesis |
|:--------|:--------------------------------------------------------------------------------------|
| angry | This example tweet expresses the emotion: anger |
| happy | This example tweet expresses the emotion: happiness |
| others | This example tweet does not express any of the emotions: anger, sadness, or happiness |
| sad | This example tweet expresses the emotion: sadness |
#### empathetic
| label | hypothesis |
|:-------------|:-----------------------------------------------------------|
| afraid | The main emotion of this example dialogue is: afraid |
| angry | The main emotion of this example dialogue is: angry |
| annoyed | The main emotion of this example dialogue is: annoyed |
| anticipating | The main emotion of this example dialogue is: anticipating |
| anxious | The main emotion of this example dialogue is: anxious |
| apprehensive | The main emotion of this example dialogue is: apprehensive |
| ashamed | The main emotion of this example dialogue is: ashamed |
| caring | The main emotion of this example dialogue is: caring |
| confident | The main emotion of this example dialogue is: confident |
| content | The main emotion of this example dialogue is: content |
| devastated | The main emotion of this example dialogue is: devastated |
| disappointed | The main emotion of this example dialogue is: disappointed |
| disgusted | The main emotion of this example dialogue is: disgusted |
| embarrassed | The main emotion of this example dialogue is: embarrassed |
| excited | The main emotion of this example dialogue is: excited |
| faithful | The main emotion of this example dialogue is: faithful |
| furious | The main emotion of this example dialogue is: furious |
| grateful | The main emotion of this example dialogue is: grateful |
| guilty | The main emotion of this example dialogue is: guilty |
| hopeful | The main emotion of this example dialogue is: hopeful |
| impressed | The main emotion of this example dialogue is: impressed |
| jealous | The main emotion of this example dialogue is: jealous |
| joyful | The main emotion of this example dialogue is: joyful |
| lonely | The main emotion of this example dialogue is: lonely |
| nostalgic | The main emotion of this example dialogue is: nostalgic |
| prepared | The main emotion of this example dialogue is: prepared |
| proud | The main emotion of this example dialogue is: proud |
| sad | The main emotion of this example dialogue is: sad |
| sentimental | The main emotion of this example dialogue is: sentimental |
| surprised | The main emotion of this example dialogue is: surprised |
| terrified | The main emotion of this example dialogue is: terrified |
| trusting | The main emotion of this example dialogue is: trusting |
#### agnews
| label | hypothesis |
|:---------|:-------------------------------------------------------|
| Business | This example news text is about business news |
| Sci/Tech | This example news text is about science and technology |
| Sports | This example news text is about sports |
| World | This example news text is about world news |
#### yahootopics
| label | hypothesis |
|:-----------------------|:---------------------------------------------------------------------------------------------------|
| Business & Finance | This example question from the Yahoo Q&A forum is categorized in the topic: Business & Finance |
| Computers & Internet | This example question from the Yahoo Q&A forum is categorized in the topic: Computers & Internet |
| Education & Reference | This example question from the Yahoo Q&A forum is categorized in the topic: Education & Reference |
| Entertainment & Music | This example question from the Yahoo Q&A forum is categorized in the topic: Entertainment & Music |
| Family & Relationships | This example question from the Yahoo Q&A forum is categorized in the topic: Family & Relationships |
| Health | This example question from the Yahoo Q&A forum is categorized in the topic: Health |
| Politics & Government | This example question from the Yahoo Q&A forum is categorized in the topic: Politics & Government |
| Science & Mathematics | This example question from the Yahoo Q&A forum is categorized in the topic: Science & Mathematics |
| Society & Culture | This example question from the Yahoo Q&A forum is categorized in the topic: Society & Culture |
| Sports | This example question from the Yahoo Q&A forum is categorized in the topic: Sports |
#### massive
| label | hypothesis |
|:-------------------------|:------------------------------------------------------------------------------------------|
| alarm_query | The example utterance is a query about alarms. |
| alarm_remove | The intent of this example utterance is to remove an alarm. |
| alarm_set | The intent of the example utterance is to set an alarm. |
| audio_volume_down | The intent of the example utterance is to lower the volume. |
| audio_volume_mute | The intent of this example utterance is to mute the volume. |
| audio_volume_other | The example utterance is related to audio volume. |
| audio_volume_up | The intent of this example utterance is turning the audio volume up. |
| calendar_query | The example utterance is a query about a calendar. |
| calendar_remove | The intent of the example utterance is to remove something from a calendar. |
| calendar_set | The intent of this example utterance is to set something in a calendar. |
| cooking_query | The example utterance is a query about cooking. |
| cooking_recipe | This example utterance is about cooking recipies. |
| datetime_convert | The example utterance is related to date time changes or conversion. |
| datetime_query | The intent of this example utterance is a datetime query. |
| email_addcontact | The intent of this example utterance is adding an email address to contacts. |
| email_query | The example utterance is a query about emails. |
| email_querycontact | The intent of this example utterance is to query contact details. |
| email_sendemail | The intent of the example utterance is to send an email. |
| general_greet | This example utterance is a general greet. |
| general_joke | The intent of the example utterance is to hear a joke. |
| general_quirky | nan |
| iot_cleaning | The intent of the example utterance is for an IoT device to start cleaning. |
| iot_coffee | The intent of this example utterance is for an IoT device to make coffee. |
| iot_hue_lightchange | The intent of this example utterance is changing the light. |
| iot_hue_lightdim | The intent of the example utterance is to dim the lights. |
| iot_hue_lightoff | The example utterance is related to turning the lights off. |
| iot_hue_lighton | The example utterance is related to turning the lights on. |
| iot_hue_lightup | The intent of this example utterance is to brighten lights. |
| iot_wemo_off | The intent of this example utterance is turning an IoT device off. |
| iot_wemo_on | The intent of the example utterance is to turn an IoT device on. |
| lists_createoradd | The example utterance is related to creating or adding to lists. |
| lists_query | The example utterance is a query about a list. |
| lists_remove | The intent of this example utterance is to remove a list or remove something from a list. |
| music_dislikeness | The intent of this example utterance is signalling music dislike. |
| music_likeness | The example utterance is related to liking music. |
| music_query | The example utterance is a query about music. |
| music_settings | The intent of the example utterance is to change music settings. |
| news_query | The example utterance is a query about the news. |
| play_audiobook | The example utterance is related to playing audiobooks. |
| play_game | The intent of this example utterance is to start playing a game. |
| play_music | The intent of this example utterance is for an IoT device to play music. |
| play_podcasts | The example utterance is related to playing podcasts. |
| play_radio | The intent of the example utterance is to play something on the radio. |
| qa_currency | This example utteranceis about currencies. |
| qa_definition | The example utterance is a query about a definition. |
| qa_factoid | The example utterance is a factoid question. |
| qa_maths | The example utterance is a question about maths. |
| qa_stock | This example utterance is about stocks. |
| recommendation_events | This example utterance is about event recommendations. |
| recommendation_locations | The intent of this example utterance is receiving recommendations for good locations. |
| recommendation_movies | This example utterance is about movie recommendations. |
| social_post | The example utterance is about social media posts. |
| social_query | The example utterance is a query about a social network. |
| takeaway_order | The intent of this example utterance is to order takeaway food. |
| takeaway_query | This example utterance is about takeaway food. |
| transport_query | The example utterance is a query about transport or travels. |
| transport_taxi | The intent of this example utterance is to get a taxi. |
| transport_ticket | This example utterance is about transport tickets. |
| transport_traffic | This example utterance is about transport or traffic. |
| weather_query | This example utterance is a query about the wheather. |
#### banking77
| label | hypothesis |
|:-------------------------------------------------|:----------------------------------------------------------------------------------------------------------|
| Refund_not_showing_up | This customer example message is about a refund not showing up. |
| activate_my_card | This banking customer example message is about activating a card. |
| age_limit | This banking customer example message is related to age limits. |
| apple_pay_or_google_pay | This banking customer example message is about apple pay or google pay |
| atm_support | This banking customer example message requests ATM support. |
| automatic_top_up | This banking customer example message is about automatic top up. |
| balance_not_updated_after_bank_transfer | This banking customer example message is about a balance not updated after a transfer. |
| balance_not_updated_after_cheque_or_cash_deposit | This banking customer example message is about a balance not updated after a cheque or cash deposit. |
| beneficiary_not_allowed | This banking customer example message is related to a beneficiary not being allowed or a failed transfer. |
| cancel_transfer | This banking customer example message is related to the cancellation of a transfer. |
| card_about_to_expire | This banking customer example message is related to the expiration of a card. |
| card_acceptance | This banking customer example message is related to the scope of acceptance of a card. |
| card_arrival | This banking customer example message is about the arrival of a card. |
| card_delivery_estimate | This banking customer example message is about a card delivery estimate or timing. |
| card_linking | nan |
| card_not_working | This banking customer example message is about a card not working. |
| card_payment_fee_charged | This banking customer example message is about a card payment fee. |
| card_payment_not_recognised | This banking customer example message is about a payment the customer does not recognise. |
| card_payment_wrong_exchange_rate | This banking customer example message is about a wrong exchange rate. |
| card_swallowed | This banking customer example message is about a card swallowed by a machine. |
| cash_withdrawal_charge | This banking customer example message is about a cash withdrawal charge. |
| cash_withdrawal_not_recognised | This banking customer example message is about an unrecognised cash withdrawal. |
| change_pin | This banking customer example message is about changing a pin code. |
| compromised_card | This banking customer example message is about a compromised card. |
| contactless_not_working | This banking customer example message is about contactless not working |
| country_support | This banking customer example message is about country-specific support. |
| declined_card_payment | This banking customer example message is about a declined card payment. |
| declined_cash_withdrawal | This banking customer example message is about a declined cash withdrawal. |
| declined_transfer | This banking customer example message is about a declined transfer. |
| direct_debit_payment_not_recognised | This banking customer example message is about an unrecognised direct debit payment. |
| disposable_card_limits | This banking customer example message is about the limits of disposable cards. |
| edit_personal_details | This banking customer example message is about editing personal details. |
| exchange_charge | This banking customer example message is about exchange rate charges. |
| exchange_rate | This banking customer example message is about exchange rates. |
| exchange_via_app | nan |
| extra_charge_on_statement | This banking customer example message is about an extra charge. |
| failed_transfer | This banking customer example message is about a failed transfer. |
| fiat_currency_support | This banking customer example message is about fiat currency support |
| get_disposable_virtual_card | This banking customer example message is about getting a disposable virtual card. |
| get_physical_card | nan |
| getting_spare_card | This banking customer example message is about getting a spare card. |
| getting_virtual_card | This banking customer example message is about getting a virtual card. |
| lost_or_stolen_card | This banking customer example message is about a lost or stolen card. |
| lost_or_stolen_phone | This banking customer example message is about a lost or stolen phone. |
| order_physical_card | This banking customer example message is about ordering a card. |
| passcode_forgotten | This banking customer example message is about a forgotten passcode. |
| pending_card_payment | This banking customer example message is about a pending card payment. |
| pending_cash_withdrawal | This banking customer example message is about a pending cash withdrawal. |
| pending_top_up | This banking customer example message is about a pending top up. |
| pending_transfer | This banking customer example message is about a pending transfer. |
| pin_blocked | This banking customer example message is about a blocked pin. |
| receiving_money | This banking customer example message is about receiving money. |
| request_refund | This banking customer example message is about a refund request. |
| reverted_card_payment? | This banking customer example message is about reverting a card payment. |
| supported_cards_and_currencies | nan |
| terminate_account | This banking customer example message is about terminating an account. |
| top_up_by_bank_transfer_charge | nan |
| top_up_by_card_charge | This banking customer example message is about the charge for topping up by card. |
| top_up_by_cash_or_cheque | This banking customer example message is about topping up by cash or cheque. |
| top_up_failed | This banking customer example message is about top up issues or failures. |
| top_up_limits | This banking customer example message is about top up limitations. |
| top_up_reverted | This banking customer example message is about issues with topping up. |
| topping_up_by_card | This banking customer example message is about topping up by card. |
| transaction_charged_twice | This banking customer example message is about a transaction charged twice. |
| transfer_fee_charged | This banking customer example message is about an issue with a transfer fee charge. |
| transfer_into_account | This banking customer example message is about transfers into the customer's own account. |
| transfer_not_received_by_recipient | This banking customer example message is about a transfer that has not arrived yet. |
| transfer_timing | This banking customer example message is about transfer timing. |
| unable_to_verify_identity | This banking customer example message is about an issue with identity verification. |
| verify_my_identity | This banking customer example message is about identity verification. |
| verify_source_of_funds | This banking customer example message is about the source of funds. |
| verify_top_up | This banking customer example message is about verification and top ups |
| virtual_card_not_working | This banking customer example message is about a virtual card not working |
| visa_or_mastercard | This banking customer example message is about types of bank cards. |
| why_verify_identity | This banking customer example message questions why identity verification is necessary. |
| wrong_amount_of_cash_received | This banking customer example message is about a wrong amount of cash received. |
| wrong_exchange_rate_for_cash_withdrawal | This banking customer example message is about a wrong exchange rate for a cash withdrawal. |
#### trueteacher
| label | hypothesis |
|:-----------------------|:---------------------------------------------------------------------|
| factually_consistent | The example summary is factually consistent with the full article. |
| factually_inconsistent | The example summary is factually inconsistent with the full article. |
#### capsotu
| label | hypothesis |
|:----------------------|:----------------------------------------------------------------------------------------------------------|
| Agriculture | This example text from a US presidential speech is about agriculture |
| Civil Rights | This example text from a US presidential speech is about civil rights or minorities or civil liberties |
| Culture | This example text from a US presidential speech is about cultural policy |
| Defense | This example text from a US presidential speech is about defense or military |
| Domestic Commerce | This example text from a US presidential speech is about banking or finance or commerce |
| Education | This example text from a US presidential speech is about education |
| Energy | This example text from a US presidential speech is about energy or electricity or fossil fuels |
| Environment | This example text from a US presidential speech is about the environment or water or waste or pollution |
| Foreign Trade | This example text from a US presidential speech is about foreign trade |
| Government Operations | This example text from a US presidential speech is about government operations or administration |
| Health | This example text from a US presidential speech is about health |
| Housing | This example text from a US presidential speech is about community development or housing issues |
| Immigration | This example text from a US presidential speech is about migration |
| International Affairs | This example text from a US presidential speech is about international affairs or foreign aid |
| Labor | This example text from a US presidential speech is about employment or labour |
| Law and Crime | This example text from a US presidential speech is about law, crime or family issues |
| Macroeconomics | This example text from a US presidential speech is about macroeconomics |
| Public Lands | This example text from a US presidential speech is about public lands or water management |
| Social Welfare | This example text from a US presidential speech is about social welfare |
| Technology | This example text from a US presidential speech is about space or science or technology or communications |
| Transportation | This example text from a US presidential speech is about transportation |
#### manifesto
| label | hypothesis |
|:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Agriculture and Farmers: Positive | This example text from a political party manifesto is positive towards policies for agriculture and farmers |
| Anti-Growth Economy: Positive | This example text from a political party manifesto is in favour of anti-growth politics |
| Anti-Imperialism | This example text from a political party manifesto is anti-imperialistic, for example against controlling other countries and for greater self-government of colonies |
| Centralisation | This example text from a political party manifesto is in favour of political centralisation |
| Civic Mindedness: Positive | This example text from a political party manifesto is positive towards national solidarity, civil society or appeals for public spiritedness or against anti-social attitudes |
| Constitutionalism: Negative | This example text from a political party manifesto is positive towards constitutionalism |
| Constitutionalism: Positive | This example text from a political party manifesto is positive towards constitutionalism and the status quo of the constitution |
| Controlled Economy | This example text from a political party manifesto is supportive of direct government control of the economy, e.g. price control or minimum wages |
| Corporatism/Mixed Economy | This example text from a political party manifesto is positive towards cooperation of government, employers, and trade unions simultaneously |
| Culture: Positive | This example text from a political party manifesto is in favour of cultural policies or leisure facilities, for example museus, libraries or public sport clubs |
| Decentralization | This example text from a political party manifesto is for decentralisation or federalism |
| Democracy | This example text from a political party manifesto favourably mentions democracy or democratic procedures or institutions |
| Economic Goals | This example text from a political party manifesto is a broad/general statement on economic goals without specifics |
| Economic Growth: Positive | This example text from a political party manifesto is supportive of economic growth, for example facilitation of more production or government aid for growth |
| Economic Orthodoxy | This example text from a political party manifesto is for economic orthodoxy, for example reduction of budget deficits, thrift or a strong currency |
| Economic Planning | This example text from a political party manifesto is positive towards government economic planning, e.g. policy plans or strategies |
| Education Expansion | This example text from a political party manifesto is about the need to expand/improve policy on education |
| Education Limitation | This example text from a political party manifesto is sceptical towards state expenditure on education, for example in favour of study fees or private schools |
| Environmental Protection | This example text from a political party manifesto is in favour of environmental protection, e.g. fighting climate change or 'green' policies or preservation of natural resources or animal rights |
| Equality: Positive | This example text from a political party manifesto is positive towards equality or social justice, e.g. protection of underprivileged groups or fair distribution of resources |
| European Community/Union: Negative | This example text from a political party manifesto negatively mentions the EU or European Community |
| European Community/Union: Positive | This example text from a political party manifesto is positive towards the EU or European Community, for example EU expansion and integration |
| Foreign Special Relationships: Negative | This example text from a political party manifesto is negative towards particular countries |
| Foreign Special Relationships: Positive | This example text from a political party manifesto is positive towards particular countries |
| Free Market Economy | This example text from a political party manifesto is in favour of a free market economy and capitalism |
| Freedom and Human Rights | This example text from a political party manifesto is in favour of freedom and human rights, for example freedom of speech, assembly or against state coercion or for individualism |
| Governmental and Administrative Efficiency | This example text from a political party manifesto is in favour of efficiency in government/administration, for example by restructuring civil service or improving bureaucracy |
| Incentives: Positive | This example text from a political party manifesto is favourable towards supply side economic policies supporting businesses, for example for incentives like subsidies or tax breaks |
| Internationalism: Negative | This example text from a political party manifesto is sceptical of internationalism, for example negative towards international cooperation, in favour of national sovereignty and unilaterialism |
| Internationalism: Positive | This example text from a political party manifesto is in favour of international cooperation with other countries, for example mentions the need for aid to developing countries, or global governance |
| Keynesian Demand Management | This example text from a political party manifesto is for keynesian demand management and demand side economic policies |
| Labour Groups: Negative | This example text from a political party manifesto is negative towards labour groups and unions |
| Labour Groups: Positive | This example text from a political party manifesto is positive towards labour groups, for example for good working conditions, fair wages or unions |
| Law and Order: Positive | This example text from a political party manifesto is positive towards law and order and strict law enforcement |
| Market Regulation | This example text from a political party manifesto is supports market regulation for a fair and open market, for example for consumer protection or for increased competition or for social market economy |
| Marxist Analysis | This example text from a political party manifesto is positive towards Marxist-Leninist ideas or uses specific Marxist terminology |
| Middle Class and Professional Groups | This example text from a political party manifesto favourably references the middle class, e.g. white colar groups or the service sector |
| Military: Negative | This example text from a political party manifesto is negative towards the military, for example for decreasing military spending or disarmament |
| Military: Positive | This example text from a political party manifesto is positive towards the military, for example for military spending or rearmament or military treaty obligations |
| Multiculturalism: Negative | This example text from a political party manifesto is sceptical towards multiculturalism, or for cultural integration or appeals to cultural homogeneity in society |
| Multiculturalism: Positive | This example text from a political party manifesto favourably mentions cultural diversity, for example for freedom of religion or linguistic heritages |
| National Way of Life: Negative | This example text from a political party manifesto unfavourably mentions a country's nation and history, for example sceptical towards patriotism or national pride |
| National Way of Life: Positive | This example text from a political party manifesto is positive towards the national way of life and history, for example pride of citizenship or appeals to patriotism |
| Nationalisation | This example text from a political party manifesto is positive towards government ownership of industries or land or for economic nationalisation |
| Non-economic Demographic Groups | This example text from a political party manifesto favourably mentions non-economic demographic groups like women, students or specific age groups |
| Peace | This example text from a political party manifesto is positive towards peace and peaceful means of solving crises, for example in favour of negotiations and ending wars |
| Political Authority | This example text from a political party manifesto mentions the speaker's competence to govern or other party's lack of such competence, or favourably mentions a strong/stable government |
| Political Corruption | This example text from a political party manifesto is negative towards political corruption or abuse of political/bureaucratic power |
| Protectionism: Negative | This example text from a political party manifesto is negative towards protectionism, in favour of free trade |
| Protectionism: Positive | This example text from a political party manifesto is in favour of protectionism, for example tariffs, export subsidies |
| Technology and Infrastructure: Positive | This example text from a political party manifesto is about technology and infrastructure, e.g. the importance of modernisation of industry, or supportive of public spending on infrastructure/tech |
| Traditional Morality: Negative | This example text from a political party manifesto is negative towards traditional morality, for example against religious moral values, for divorce or abortion, for modern families or separation of church and state |
| Traditional Morality: Positive | This example text from a political party manifesto is favourable towards traditional or religious values, for example for censorship of immoral behavour, for traditional family values or religious institutions |
| Underprivileged Minority Groups | This example text from a political party manifesto favourably mentions underprivileged minorities, for example handicapped, homosexuals or immigrants |
| Welfare State Expansion | This example text from a political party manifesto is positive towards the welfare state, e.g. health care, pensions or social housing |
| Welfare State Limitation | This example text from a political party manifesto is for limiting the welfare state, for example public funding for social services or social security, e.g. private care before state care | | null | transformers | zero-shot-classification | null | null | null | null | null | null | null | null | null | MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 | [
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Eliahu/vit-base-patch16-224-in21k-finetuned-lora-food101 | Eliahu | 2023-11-29T15:57:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:57:55Z | 2023-11-27T14:13:39.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Eliahu/vit-base-patch16-224-in21k-finetuned-lora-food101 | [
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NotAHorse123/ellmo2 | NotAHorse123 | 2023-11-29T08:49:42Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:49:42Z | 2023-11-27T15:47:33.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | NotAHorse123/ellmo2 | [
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gggggggg123/telegram-slav-gosh | gggggggg123 | 2023-11-30T01:19:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:19:23Z | 2023-11-27T16:11:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | gggggggg123/telegram-slav-gosh | [
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lombardata/dino-base-2023_11_27-with_custom_head | lombardata | 2023-11-30T01:07:17Z | 0 | 0 | null | [
"transformers",
"pytorch",
"dinov2",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:07:17Z | 2023-11-27T18:44:30.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | lombardata/dino-base-2023_11_27-with_custom_head | [
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Smoob/Glurgle | Smoob | 2023-11-29T19:11:40Z | 0 | 0 | null | [
"transformers",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:11:40Z | 2023-11-27T18:50:45.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Smoob/Glurgle | [
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Gastpm/Donut_CORD3 | Gastpm | 2023-11-30T01:23:39Z | 0 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:23:39Z | 2023-11-27T19:21:26.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | Gastpm/Donut_CORD3 | [
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osanseviero/osangu-212 | osanseviero | 2023-11-29T11:21:39Z | 0 | 0 | null | [
"image-to-video",
"license:other",
"has_space",
"region:us"
] | 2023-11-29T11:21:39Z | 2023-11-27T20:40:41.000Z | null | null |
---
pipeline_tag: image-to-video
license: other
license_name: stable-video-diffusion-nc-community
license_link: LICENSE
---
# LoRA DreamBooth - osanseviero/osangu-212
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0 trained on @fffiloni's SD-XL trainer.
The weights were trained on the concept prompt:
```
TOK
```
Use this keyword to trigger your custom model in your prompts.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Usage
Make sure to upgrade diffusers to >= 0.19.0:
```
pip install diffusers --upgrade
```
In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```
To just use the base model, you can run:
```python
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
device = "cuda" if torch.cuda.is_available() else "cpu"
vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae, torch_dtype=torch.float16, variant="fp16",
use_safetensors=True
)
pipe.to(device)
# This is where you load your trained weights
specific_safetensors = "pytorch_lora_weights.safetensors"
lora_scale = 0.9
pipe.load_lora_weights(
'osanseviero/osangu-212',
weight_name = specific_safetensors,
# use_auth_token = True
)
prompt = "A majestic TOK jumping from a big stone at night"
image = pipe(
prompt=prompt,
num_inference_steps=50,
cross_attention_kwargs={"scale": lora_scale}
).images[0]
```
| null | null | null | null | null | null | null | null | null | null | null | null | osanseviero/osangu-212 | [
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akadriu/whisper-distill-large-v2-sq | akadriu | 2023-11-30T00:08:11Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:08:11Z | 2023-11-27T20:47:51.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | akadriu/whisper-distill-large-v2-sq | [
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sophiayk/whisper-small-ko | sophiayk | 2023-11-29T05:35:40Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:35:40Z | 2023-11-27T23:13:52.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | sophiayk/whisper-small-ko | [
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nlile/PE-7b-full | nlile | 2023-11-29T01:39:42Z | 0 | 0 | null | [
"transformers",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T01:39:42Z | 2023-11-28T01:18:21.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | nlile/PE-7b-full | [
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