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list | pipeline_tag
stringclasses 17
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int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
DevsIA/Devs_IA | []
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} | 0 | null | ---
tags:
- Question(s) Generation
metrics:
- rouge
model-index:
- name: consciousAI/question-generation-auto-t5-v1-base-s-q-c
results: []
---
# Auto Question Generation
The model is intended to be used for Auto Question Generation task i.e. no hint are required as input. The model is expected to produce one or possibly more than one question from the provided context.
[Live Demo: Question Generation](https://huggingface.co/spaces/consciousAI/question_generation)
Including this there are four models trained with different training sets, demo provide comparison to all in one go. However, you can reach individual projects at below links:
[Auto Question Generation v1](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s)
[Auto Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-t5-v1-base-s-q)
[Auto/Hints based Question Generation v1](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q)
[Auto/Hints based Question Generation v2](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c)
This model can be used as below:
```
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer
)
model_checkpoint = "consciousAI/question-generation-auto-t5-v1-base-s-q-c"
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
## Input with prompt
context="question_context: <context>"
encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device)
## You can play with many hyperparams to condition the output, look at demo
output = model.generate(encodings,
#max_length=300,
#min_length=20,
#length_penalty=2.0,
num_beams=4,
#early_stopping=True,
#do_sample=True,
#temperature=1.1
)
## Multiple questions are expected to be delimited by '?' You can write a small wrapper to elegantly format. Look at the demo.
questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output]
```
## Training and evaluation data
Squad & QNLi combo.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.8732 | 1.0 | 942 | 1.4330 | 0.5558 | 0.3857 | 0.5219 | 0.5231 |
| 1.2361 | 2.0 | 1884 | 1.3720 | 0.5649 | 0.4042 | 0.5332 | 0.5338 |
| 0.9515 | 3.0 | 2826 | 1.3703 | 0.5699 | 0.4044 | 0.5373 | 0.5385 |
| 0.7383 | 4.0 | 3768 | 1.4039 | 0.5753 | 0.4159 | 0.5414 | 0.5426 |
| 0.6291 | 5.0 | 4710 | 1.4661 | 0.5809 | 0.4227 | 0.5488 | 0.5498 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0
|
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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"RobertaForQuestionAnswering"
],
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} | 3 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Shri3/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
} | 30 | 2022-10-27T15:07:07Z | ---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("alanakbik/test-push-public")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
``` |
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
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"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: trialzz
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. -->
# trialzz
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1097
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 113 | 2.2090 |
| No log | 2.0 | 226 | 2.1168 |
| No log | 3.0 | 339 | 2.1097 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 59,663,489 | 2022-10-27T16:58:41Z | ---
license: apache-2.0
datasets:
- food101
tags:
- openvino
- int8
---
## [Vision Transformer (ViT)](https://huggingface.co/juliensimon/autotrain-food101-1471154050) quantized and exported to the OpenVINO IR.
## Model Details
**Model Description:** This ViT model fine-tuned on Food-101 was statically quantized and exported to the OpenVINO IR using [optimum](https://huggingface.co/docs/optimum/intel/optimization_ov).
## Usage example
You can use this model with Transformers *pipeline*.
```python
from transformers import pipeline, AutoFeatureExtractor
from optimum.intel.openvino import OVModelForImageClassification
model_id = "echarlaix/vit-food101-int8"
model = OVModelForImageClassification.from_pretrained(model_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
outputs = pipe("http://farm2.staticflickr.com/1375/1394861946_171ea43524_z.jpg")
``` |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 8,214 | 2022-10-27T17:15:40Z | ---
language:
- en
thumbnail: "https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/thumbnail.png"
tags:
- cyberpunk
- anime
- waifu-diffusion
- stable-diffusion
- aiart
- text-to-image
license: creativeml-openrail-m
---
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/5.jpg" width="512" height="512"/></center>
# Cyberpunk Anime Diffusion
An AI model that generates cyberpunk anime characters!~
Based of a finetuned Waifu Diffusion V1.3 Model with Stable Diffusion V1.5 New Vae, training in Dreambooth
by [DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg)
### 🧨 Diffusers
This repo contains both .ckpt and Diffuser model files. It's compatible to be used as any Stable Diffusion model, using standard [Stable Diffusion Pipelines](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can convert this model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](https://huggingface.co/blog/stable_diffusion_jax).
```python example for loading the Diffuser
#!pip install diffusers transformers scipy torch
from diffusers import StableDiffusionPipeline
import torch
model_id = "DGSpitzer/Cyberpunk-Anime-Diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a beautiful perfect face girl in dgs illustration style, Anime fine details portrait of school girl in front of modern tokyo city landscape on the background deep bokeh, anime masterpiece, 8k, sharp high quality anime"
image = pipe(prompt).images[0]
image.save("./cyberpunk_girl.png")
```
# Online Demo
You can try the Online Web UI demo build with [Gradio](https://github.com/gradio-app/gradio), or use Colab Notebook at here:
*My Online Space Demo*
[](https://huggingface.co/spaces/DGSpitzer/DGS-Diffusion-Space)
*Finetuned Diffusion WebUI Demo by anzorq*
[](https://huggingface.co/spaces/anzorq/finetuned_diffusion)
*Colab Notebook*
[](https://colab.research.google.com/github/HelixNGC7293/cyberpunk-anime-diffusion/blob/main/cyberpunk_anime_diffusion.ipynb)[](https://github.com/HelixNGC7293/cyberpunk-anime-diffusion)
*Buy me a coffee if you like this project ;P ♥*
[](https://www.buymeacoffee.com/dgspitzer)
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/1.jpg" width="512" height="512"/></center>
# **👇Model👇**
AI Model Weights available at huggingface: https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/2.jpg" width="512" height="512"/></center>
# Usage
After model loaded, use keyword **dgs** in your prompt, with **illustration style** to get even better results.
For sampler, use **Euler A** for the best result (**DDIM** kinda works too), CFG Scale 7, steps 20 should be fine
**Example 1:**
```
portrait of a girl in dgs illustration style, Anime girl, female soldier working in a cyberpunk city, cleavage, ((perfect femine face)), intricate, 8k, highly detailed, shy, digital painting, intense, sharp focus
```
For cyber robot male character, you can add **muscular male** to improve the output.
**Example 2:**
```
a photo of muscular beard soldier male in dgs illustration style, half-body, holding robot arms, strong chest
```
**Example 3 (with Stable Diffusion WebUI):**
If using [AUTOMATIC1111's Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
You can simply use this as **prompt** with **Euler A** Sampler, CFG Scale 7, steps 20, 704 x 704px output res:
```
an anime girl in dgs illustration style
```
And set the **negative prompt** as this to get cleaner face:
```
out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers
```
This will give you the exactly same style as the sample images above.
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/ReadmeAddon.jpg" width="256" height="353"/></center>
---
**NOTE: usage of this model implies accpetance of stable diffusion's [CreativeML Open RAIL-M license](LICENSE)**
---
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/4.jpg" width="700" height="700"/></center>
<center><img src="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/img/6.jpg" width="700" height="700"/></center>
|
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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}
} | 388,769 | null | Access to model brillo/Fuckyou is restricted and you are not in the authorized list. Visit https://huggingface.co/brillo/Fuckyou to ask for access. |
t5-small | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.12885",
"arxiv:1905.10044",
"arxiv:1910.09700",
"transformers",
"summarization",
"translation",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
]
| translation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,886,928 | 2022-10-27T19:22:09Z | ---
datasets:
- bigscience/xP3
- mc4
license: apache-2.0
language:
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tr
- uk
- und
- ur
- uz
- vi
- xh
- yi
- yo
- zh
- zu
pipeline_tag: text2text-generation
widget:
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?"
example_title: "zh-en sentiment"
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？"
example_title: "zh-zh sentiment"
- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
example_title: "vi-en query"
- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
example_title: "fr-fr query"
- text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
example_title: "te-en qa"
- text: "Why is the sky blue?"
example_title: "en-en qa"
- text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):"
example_title: "es-en fable"
- text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):"
example_title: "hi-en fable"
model-index:
- name: mt0-small
results:
- task:
type: Coreference resolution
dataset:
type: winogrande
name: Winogrande XL (xl)
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 50.51
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (en)
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 51.31
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (fr)
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.22
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (jp)
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.45
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (pt)
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 51.71
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (ru)
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.29
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (zh)
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.17
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r1)
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 34.7
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r2)
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 34.0
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r3)
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 33.83
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (cb)
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 50.0
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (rte)
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 61.01
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ar)
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.43
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (bg)
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.55
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (de)
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.78
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (el)
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.43
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (en)
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 38.47
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (es)
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.75
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (fr)
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.15
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (hi)
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.38
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ru)
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.35
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (sw)
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.18
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (th)
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.55
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (tr)
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.51
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ur)
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.78
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (vi)
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.95
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (zh)
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.07
- task:
type: Sentence completion
dataset:
type: story_cloze
name: StoryCloze (2016)
config: "2016"
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 54.36
- task:
type: Sentence completion
dataset:
type: super_glue
name: SuperGLUE (copa)
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (et)
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ht)
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 60.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (id)
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (it)
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (qu)
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (sw)
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 55.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ta)
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (th)
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 65.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (tr)
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 58.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (vi)
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (zh)
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ar)
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 48.78
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (es)
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 55.2
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (eu)
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 52.95
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (hi)
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.01
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (id)
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.08
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (my)
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 51.82
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ru)
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 49.7
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (sw)
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 54.53
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (te)
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.67
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (zh)
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 57.78
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
- **Languages:** Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
<div class="max-w-full overflow-auto">
<table>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
**Feel free to share your generations in the Community tab!**
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
# Training
## Model
- **Architecture:** Same as [mt5-small](https://huggingface.co/google/mt5-small), also refer to the `config.json` file
- **Finetuning steps:** 25000
- **Finetuning tokens:** 4.62 billion
- **Precision:** bfloat16
## Hardware
- **TPUs:** TPUv4-64
## Software
- **Orchestration:** [T5X](https://github.com/google-research/t5x)
- **Neural networks:** [Jax](https://github.com/google/jax)
# Evaluation
We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
# Citation
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
A-bhimany-u08/bert-base-cased-qqp | [
"pytorch",
"bert",
"text-classification",
"dataset:qqp",
"transformers"
]
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} | 138 | 2022-10-28T01:27:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: languagemodel
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. -->
# languagemodel
This model is a fine-tuned version of [monideep2255/XLRS-torgo](https://huggingface.co/monideep2255/XLRS-torgo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 1.1173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.3015 | 3.12 | 400 | inf | 1.3984 |
| 0.6892 | 6.25 | 800 | inf | 1.1059 |
| 0.5069 | 9.37 | 1200 | inf | 1.0300 |
| 0.3596 | 12.5 | 1600 | inf | 1.0830 |
| 0.2571 | 15.62 | 2000 | inf | 1.1981 |
| 0.198 | 18.75 | 2400 | inf | 1.1009 |
| 0.1523 | 21.87 | 2800 | inf | 1.1803 |
| 0.1112 | 25.0 | 3200 | inf | 1.0429 |
| 0.08 | 28.12 | 3600 | inf | 1.1173 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.13.1
|
AWTStress/stress_score | [
"tf",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback"
]
| text-classification | {
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"DistilBertForSequenceClassification"
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} | 14 | null | ---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- transformers
---
# CoT-MAE MS-Marco Passage Retriever
CoT-MAE is a transformers based Mask Auto-Encoder pretraining architecture designed for Dense Passage Retrieval.
**CoT-MAE MS-Marco Passage Retriever** is a retriever trained with BM25 hard negatives and CoT-MAE retriever mined MS-Marco hard negatives using [Tevatron](github.com/texttron/tevatron) toolkit. Specifically, we trained a stage-one retriever using BM25 HN, using stage-one retriever to mine HN, then trained a stage-two retriever using both BM25 HN & stage-one retriever mined hn. The release is the stage-two retriever.
Details can be found in our paper and codes.
Paper: [ConTextual Mask Auto-Encoder for Dense Passage Retrieval](https://arxiv.org/abs/2208.07670).
Code: [caskcsg/ir/cotmae](https://github.com/caskcsg/ir/tree/main/cotmae)
## Scores
### MS-Marco Passage full-ranking
| MRR @10 | recall@1 | recall@50 | recall@1k | QueriesRanked |
|----------|----------|-----------|-----------|----------------|
| 0.394431 | 0.265903 | 0.870344 | 0.986676 | 6980 |
## Citations
If you find our work useful, please cite our paper.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2208.07670,
doi = {10.48550/ARXIV.2208.07670},
url = {https://arxiv.org/abs/2208.07670},
author = {Wu, Xing and Ma, Guangyuan and Lin, Meng and Lin, Zijia and Wang, Zhongyuan and Hu, Songlin},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {ConTextual Mask Auto-Encoder for Dense Passage Retrieval},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
Aarav/MeanMadCrazy_HarryPotterBot | []
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} | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DNABert_K6_G_quad_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DNABert_K6_G_quad_3
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0722
- Accuracy: 0.9761
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0912 | 1.0 | 9375 | 0.0883 | 0.9707 |
| 0.0668 | 2.0 | 18750 | 0.0723 | 0.9757 |
| 0.0598 | 3.0 | 28125 | 0.0722 | 0.9761 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AethiQs-Max/AethiQs_GemBERT_bertje_50k | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | {
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}
}
} | 11 | null | ---
license: mit
---
### Edgerunners Style on Stable Diffusion
This is the `<edgerunners-style-av>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Ajteks/Chatbot | []
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- snli
model-index:
- name: bigbird-roberta-large-snli
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. -->
# bigbird-roberta-large-snli
This model was trained from scratch on the snli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2437
- eval_p: 0.9216
- eval_r: 0.9214
- eval_f1: 0.9215
- eval_runtime: 22.8545
- eval_samples_per_second: 429.849
- eval_steps_per_second: 26.866
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
Akash7897/bert-base-cased-wikitext2 | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
]
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}
} | 8 | null | csv files can be downloaded [here](https://www.kaggle.com/datasets/oddrationale/mnist-in-csv/download?datasetVersionNumber=2)
|
Akashpb13/Hausa_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
]
| automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 31 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-chinese-finetuned-own
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-own
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the Myown Car_information dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6957
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 120 | 1.7141 |
| No log | 2.0 | 240 | 1.6677 |
| No log | 3.0 | 360 | 1.7976 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Akbarariza/Anjar | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
**Borderlands Diffusion**
This is a fine-tuned Stable Diffusion model trained on screenshots from the video game series Borderlands.
Use the token **_brld_** in your prompts for the style effect.
#### Prompt and settings for portraits:
**brld harrison ford**
_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417
**brld morgan freeman**
_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
AkshatSurolia/ViT-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 40 | null | ---
license: apache-2.0
---
# DiffAb
This repository contains trained weights of the DiffAb model.
Code: https://github.com/luost26/diffab
Paper: https://www.biorxiv.org/content/10.1101/2022.07.10.499510.abstract
Demo: https://huggingface.co/spaces/luost26/DiffAb
|
AkshaySg/GrammarCorrection | []
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} | 0 | null | ---
license: apache-2.0
widget:
- text: "Ayer dormí la siesta durante 3 horas"
- text: "Recuerda tu cita con el médico el lunes a las 8 de la tarde"
- text: "Recuerda tomar la medicación cada noche"
tags:
- LABEL-0 = NONE
- LABEL-1 = B-DATE
- LABEL-2 = I-DATE
- LABEL-3 = B-TIME
- LABEL-4 = I-TIME
- LABEL-5 = B-DURATION
- LABEL-6 = B-DURATION
- LABEL-7 = B-SET
- LABEL-8 = B-SET
metrics:
- precision
- recall
- f1
- accuracy
datasets:
- Spanish-Timebank
model-index:
- name: roberta-base-biomedical-clinical-es-finetuned-ner-timebank
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-biomedical-clinical-es-finetuned-ner-timebank
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162
- Precision: 0.8070
- Recall: 0.8854
- F1: 0.8444
- Accuracy: 0.9921
## 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: 8e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 82
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0658 | 1.0 | 8 | 0.0613 | 0.3704 | 0.4514 | 0.4069 | 0.9758 |
| 0.038 | 2.0 | 16 | 0.0328 | 0.4528 | 0.6667 | 0.5393 | 0.9789 |
| 0.0067 | 3.0 | 24 | 0.0212 | 0.4515 | 0.7431 | 0.5617 | 0.9776 |
| 0.0225 | 4.0 | 32 | 0.0177 | 0.4913 | 0.7847 | 0.6043 | 0.9814 |
| 0.0119 | 5.0 | 40 | 0.0170 | 0.5531 | 0.7778 | 0.6465 | 0.9848 |
| 0.0109 | 6.0 | 48 | 0.0151 | 0.7027 | 0.8125 | 0.7536 | 0.9893 |
| 0.003 | 7.0 | 56 | 0.0136 | 0.6260 | 0.8194 | 0.7098 | 0.9861 |
| 0.0059 | 8.0 | 64 | 0.0134 | 0.7164 | 0.8333 | 0.7705 | 0.9895 |
| 0.0065 | 9.0 | 72 | 0.0125 | 0.7040 | 0.8507 | 0.7704 | 0.9890 |
| 0.0017 | 10.0 | 80 | 0.0118 | 0.6177 | 0.8472 | 0.7145 | 0.9852 |
| 0.0021 | 11.0 | 88 | 0.0150 | 0.7812 | 0.8681 | 0.8224 | 0.9915 |
| 0.0041 | 12.0 | 96 | 0.0165 | 0.8078 | 0.8611 | 0.8336 | 0.9922 |
| 0.0025 | 13.0 | 104 | 0.0142 | 0.7723 | 0.8715 | 0.8189 | 0.9909 |
| 0.0024 | 14.0 | 112 | 0.0134 | 0.7440 | 0.8681 | 0.8013 | 0.9905 |
| 0.0027 | 15.0 | 120 | 0.0142 | 0.7699 | 0.8715 | 0.8176 | 0.9912 |
| 0.0019 | 16.0 | 128 | 0.0151 | 0.7918 | 0.8715 | 0.8298 | 0.9918 |
| 0.0036 | 17.0 | 136 | 0.0151 | 0.7837 | 0.8681 | 0.8237 | 0.9917 |
| 0.0011 | 18.0 | 144 | 0.0149 | 0.7730 | 0.875 | 0.8208 | 0.9914 |
| 0.0013 | 19.0 | 152 | 0.0152 | 0.7913 | 0.8819 | 0.8342 | 0.9918 |
| 0.0017 | 20.0 | 160 | 0.0159 | 0.8038 | 0.8819 | 0.8411 | 0.9922 |
| 0.0006 | 21.0 | 168 | 0.0164 | 0.8070 | 0.8854 | 0.8444 | 0.9922 |
| 0.002 | 22.0 | 176 | 0.0164 | 0.8121 | 0.8854 | 0.8472 | 0.9922 |
| 0.0021 | 23.0 | 184 | 0.0163 | 0.8121 | 0.8854 | 0.8472 | 0.9922 |
| 0.0013 | 24.0 | 192 | 0.0162 | 0.8070 | 0.8854 | 0.8444 | 0.9921 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
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} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "Positivity towards meat consumption remains strong, despite evidence of negative environmental and ethical outcomes. Although awareness of these repercussions is rising, there is still public resistance to removing meat from our diets. One potential method to alleviate these effects is to produce in vitro meat: meat grown in a laboratory that does not carry the same environmental or ethical concerns. However, there is limited research examining public attitudes towards in vitro meat, thus we know little about the capacity for it be accepted by consumers. This study aimed to examine perceptions of in vitro meat and identify potential barriers that might prevent engagement. Through conducting an online survey with US participants, we identified that although most respondents were willing to try in vitro meat, only one third were definitely or probably willing to eat in vitro meat regularly or as a replacement for farmed meat. Men were more receptive to it than women, as were politically liberal respondents compared with conservative ones. Vegetarians and vegans were more likely to perceive benefits compared to farmed meat, but they were less likely to want to try it than meat eaters. The main concerns were an anticipated high price, limited taste and appeal and a concern that the product was unnatural. It is concluded that people in the USA are likely to try in vitro meat, but few believed that it would replace farmed meat in their diet."
datasets:
- vegancreativecompass/autotrain-data-scitldr-for-vegan-studies
co2_eq_emissions:
emissions: 57.779835625872906
---
# About This Model
This model has been trained to take abstracts of scientific studies about veganism & animal rights and turn them into single-sentence takeaways for vegan businesses and animal activists to apply to their activism. The dataset was curated by scraping TLDRs and abstracts from Semantic Scholar and having vegan activists and marketing professionals from VEG3 review the usefulness of a random sample of the dataset to ensure their relevance to vegan businesses and animal activists.
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1923365100
- CO2 Emissions (in grams): 57.7798
## Validation Metrics
- Loss: 0.711
- Rouge1: 44.317
- Rouge2: 30.335
- RougeL: 41.369
- RougeLsum: 41.198
- Gen Len: 17.855
## Usage
You can use cURL to access this model:
```
curl https://api-inference.huggingface.co/models/VEG3/TLDR-Vegan-Studies \
-X POST \
-d '{"inputs":"ABSTRACT"}' \
-H "Authorization: Bearer YOURAPIKEY"
``` |
Alaeddin/convbert-base-turkish-ner-cased | [
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_s3rl_happy_hardcore
## Model provided by: Broccaloo
Pretrained musika_s3rl_happy_hardcore model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_s3rl_happy_hardcore model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
Aleenbo/Arcane | []
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} | 0 | null | ---
language: te
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning
license: apache-2.0
model-index:
- name: Henil Panchal Facebook XLSR Wav2Vec2 Large 53 Telugu
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER
type: wer
value: 41.90
---
# Wav2Vec2-Large-XLSR-53-Telugu
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Telugu using the ASR IIIT-H dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
**Test Result**: 41.90%
## Training
70% of the O part of ASR IIIT-H Telugu dataset was used for training.
|
Aleksandar/electra-srb-oscar | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: week5-distilbert-base-multilingual-cased-finetuned-eng
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. -->
# week5-distilbert-base-multilingual-cased-finetuned-eng
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0849
- Precision: 0.2332
- Recall: 0.2525
- F1: 0.2425
- Accuracy: 0.9754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1069 | 1.0 | 924 | 0.0904 | 0.2727 | 0.0545 | 0.0909 | 0.9756 |
| 0.0698 | 2.0 | 1848 | 0.0893 | 0.2898 | 0.1838 | 0.2250 | 0.9777 |
| 0.0516 | 3.0 | 2772 | 0.0849 | 0.2332 | 0.2525 | 0.2425 | 0.9754 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Aleksandar1932/gpt2-country | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 12 | 2022-10-29T14:03:11Z | ---
language:
- en
tags:
- convAI
- conversational
---
Korean Drama!
[Hometown Cha-Cha-Cha](https://en.wikipedia.org/wiki/Hometown_Cha-Cha-Cha) |
Aleksandar1932/gpt2-hip-hop | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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} | 8 | null | ---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-WIKI_Growth_Form_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT-WIKI_Growth_Form_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2853
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.259 | 1.0 | 2320 | 0.2713 |
| 0.195 | 2.0 | 4640 | 0.2513 |
| 0.149 | 3.0 | 6960 | 0.2853 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AlexMaclean/sentence-compression-roberta | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
]
| token-classification | {
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"RobertaForTokenClassification"
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} | 13 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/socpens/1667063063525/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1404907635934216205/unH2FvUy_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">scorpy</div>
<div style="text-align: center; font-size: 14px;">@socpens</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from scorpy.
| Data | scorpy |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 758 |
| Short tweets | 423 |
| Tweets kept | 2055 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xewzfqo/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @socpens's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1u64kl11) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1u64kl11/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/socpens')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AlexN/xls-r-300m-pt | [
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"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
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}
} | 15 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511060623072927747/xvz5xYEj_400x400.jpg')">
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style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
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style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
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<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">wayneradiotv</div>
<div style="text-align: center; font-size: 14px;">@wayneradiotv</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from wayneradiotv.
| Data | wayneradiotv |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 1142 |
| Short tweets | 365 |
| Tweets kept | 1720 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3nfxw79q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wayneradiotv's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dhlzg3t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dhlzg3t/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wayneradiotv')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AlexaMerens/Owl | [
"license:cc"
]
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} | 0 | null |
---
language:
- pt
thumbnail: "Portuguese BERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt
- rufimelo/PortugueseLegalSentences-v1
widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
sentences:
- "O juíz leu as provas."
- "O juíz leu o recurso."
- "O juíz atirou uma pedra."
example_title: "Example 1"
model-index:
- name: BERTimbau
results:
- task:
name: STS
type: STS
metrics:
- name: Pearson Correlation - assin Dataset
type: Pearson Correlation
value: 0.78814
- name: Pearson Correlation - assin2 Dataset
type: Pearson Correlation
value: 0.81380
- name: Pearson Correlation - stsb_multi_mt pt Dataset
type: Pearson Correlation
value: 0.75777
---
# rufimelo/Legal-BERTimbau-base-TSDAE-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-BERTimbau-base-TSDAE-sts is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
model = SentenceTransformer('rufimelo/Legal-BERTimbau-base-TSDAE-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-base-TSDAE-sts')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-base-TSDAE-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results STS
| Model| Assin | Assin2|stsb_multi_mt pt| avg|
| ---------------------------------------- | ---------- | ---------- |---------- |---------- |
| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
| Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
| Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
| Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
| Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
| Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
| Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831 |**0.84575**|0.80682|
## Training
rufimelo/Legal-BERTimbau-base-TSDAE-sts is based on rufimelo/Legal-BERTimbau-base-TSDAE which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
rufimelo/Legal-BERTimbau-base-TSDAE was trained with TSDAE: 50000 cleaned documents (https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v1)
'lr': 1e-5
It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets. 'lr': 1e-5
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
## Citing & Authors
If you use this work, please cite:
```bibtex
@inproceedings{souza2020bertimbau,
author = {F{\'a}bio Souza and
Rodrigo Nogueira and
Roberto Lotufo},
title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
year = {2020}
}
@inproceedings{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
year={2016}
}
@inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}
@InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}
``` |
Alexander-Learn/bert-finetuned-squad | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: long-t5-tglobal-base
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. -->
# long-t5-tglobal-base
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- TensorFlow 2.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
AlexeyYazev/my-awesome-model | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: long-t5-local-base
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. -->
# long-t5-local-base
This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- TensorFlow 2.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
Alfia/anekdotes | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: long-t5-local-large
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. -->
# long-t5-local-large
This model is a fine-tuned version of [google/long-t5-local-large](https://huggingface.co/google/long-t5-local-large) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.24.0.dev0
- TensorFlow 2.9.0
- Datasets 2.2.2
- Tokenizers 0.11.6
|
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} | 0 | null | ---
license: mit
---
### Edgerunners Style v2 on Stable Diffusion
This is the `<edgerunners-style-av-v2>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Andrey1989/bert-multilingual-finetuned-ner | []
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} | 0 | null | ---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
- visual emb-gam
---
# Model description
This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`facebook/dino-vitb16` DINO checkpoint](https://huggingface.co/facebook/dino-vitb16).
## Intended uses & limitations
This model is not intended to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------|-----------------------------------------------------------|
| Cs | 10 |
| class_weight | |
| cv | StratifiedKFold(n_splits=5, random_state=1, shuffle=True) |
| dual | False |
| fit_intercept | True |
| intercept_scaling | 1.0 |
| l1_ratios | |
| max_iter | 100 |
| multi_class | auto |
| n_jobs | |
| penalty | l2 |
| random_state | 1 |
| refit | False |
| scoring | |
| solver | lbfgs |
| tol | 0.0001 |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-58b9b78a-229c-4a6a-b67b-244945cdc29d div.sk-text-repr-fallback {display: none;}</style><div id="sk-58b9b78a-229c-4a6a-b67b-244945cdc29d" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d612eebc-39a3-42fc-99e0-37e6f258ac21" type="checkbox" checked><label for="d612eebc-39a3-42fc-99e0-37e6f258ac21" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
| accuracy | 0.97707 |
| f1 score | 0.97707 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from PIL import Image
from skops import hub_utils
import torch
from transformers import AutoFeatureExtractor, AutoModel
import pickle
import os
# load embedding model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16')
model = AutoModel.from_pretrained('facebook/dino-vitb16').eval().to(device)
# load logistic regression
os.mkdir('emb-gam-dino')
hub_utils.download(repo_id='Ramos-Ramos/emb-gam-dino', dst='emb-gam-dino')
with open('emb-gam-dino/model.pkl', 'rb') as file:
logistic_regression = pickle.load(file)
# load image
img = Image.open('examples/english_springer.png')
# preprocess image
inputs = {k: v.to(device) for k, v in feature_extractor(img, return_tensors='pt').items()}
# extract patch embeddings
with torch.no_grad():
patch_embeddings = model(**inputs).last_hidden_state[0, 1:].cpu()
# classify
pred = logistic_regression.predict(patch_embeddings.sum(dim=0, keepdim=True))
# get patch contributions
patch_contributions = logistic_regression.coef_ @ patch_embeddings.T.numpy()
```
</details>
# Model Card Authors
This model card is written by following authors:
Patrick Ramos
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
**BibTeX:**
```
@article{singh2022emb,
title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models},
author={Singh, Chandan and Gao, Jianfeng},
journal={arXiv preprint arXiv:2209.11799},
year={2022}
}
```
# Additional Content
## confusion_matrix
# Demo
Check out our HuggingFace Space [here](https://huggingface.co/spaces/Ramos-Ramos/emb-gam-dino)! It does Imagenette classification and visualizes patch contributions per label. |
Andrey1989/mbart-finetuned-en-to-kk | []
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} | 0 | null | ---
license: mit
---
This model is ANCE-Tele trained on MS MARCO, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|MS MARCO (Dev)|MRR@10|R@1K|
|:---|:---|:---|
|ANCE-Tele|39.1|98.4|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
``` |
Andrey1989/mbert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
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} | 12 | null | ---
license: mit
---
### Frank Frazetta on Stable Diffusion
This is the `frank franzetta` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Andrey78/my_nlp_test_model | []
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} | 0 | null | ---
license: mit
---
This model is the **passage** encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.
ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.
|NQ (Test)|R@5|R@20|R@20|
|:---|:---|:---|:---|
|ANCE-Tele|76.9|83.4|87.3|
```
@inproceedings{sun2022ancetele,
title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
booktitle={Proceedings of EMNLP 2022},
year={2022}
}
``` |
Andrija/RobertaFastBPE | []
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} | 0 | null | ---
tags:
- autotrain
- translation
language:
- en
- it
datasets:
- Tritkoman/autotrain-data-gatvotva
co2_eq_emissions:
emissions: 14.908336657166226
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1931765297
- CO2 Emissions (in grams): 14.9083
## Validation Metrics
- Loss: 2.666
- SacreBLEU: 17.990
- Gen len: 64.922 |
Andrija/SRoBERTa-L-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 6 | 2022-10-30T07:42:26Z | from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
|
Andrija/SRoBERTa-L | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
]
| fill-mask | {
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"RobertaForMaskedLM"
],
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} | 58 | 2022-10-30T07:54:25Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tluo_xml_roberta_base_amazon_review_sentiment_v3
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. -->
# tluo_xml_roberta_base_amazon_review_sentiment_v3
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9456
- Accuracy: 0.6023
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.056 | 0.33 | 5000 | 0.9885 | 0.5642 |
| 0.944 | 0.67 | 10000 | 0.9574 | 0.5913 |
| 0.9505 | 1.0 | 15000 | 0.9674 | 0.579 |
| 0.8902 | 1.33 | 20000 | 0.9660 | 0.5945 |
| 0.8851 | 1.67 | 25000 | 0.9470 | 0.5888 |
| 0.8714 | 2.0 | 30000 | 0.9456 | 0.6023 |
| 0.7967 | 2.33 | 35000 | 0.9662 | 0.5978 |
| 0.767 | 2.67 | 40000 | 0.9738 | 0.5987 |
| 0.7595 | 3.0 | 45000 | 0.9740 | 0.5988 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.1
|
Andrija/SRoBERTa-XL-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-detests-29-10-2022
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-detests-29-10-2022
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1346
- Accuracy: 0.7921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2105 | 0.33 | 50 | 0.5718 | 0.8265 |
| 0.2156 | 0.65 | 100 | 0.5998 | 0.8232 |
| 0.215 | 0.98 | 150 | 0.5778 | 0.8232 |
| 0.1353 | 1.31 | 200 | 0.6240 | 0.8069 |
| 0.0664 | 1.63 | 250 | 0.7277 | 0.7938 |
| 0.2339 | 1.96 | 300 | 0.8471 | 0.7758 |
| 0.1518 | 2.29 | 350 | 0.9487 | 0.7938 |
| 0.0766 | 2.61 | 400 | 0.9715 | 0.8069 |
| 0.0524 | 2.94 | 450 | 1.0911 | 0.7610 |
| 0.0836 | 3.27 | 500 | 1.0099 | 0.8101 |
| 0.0935 | 3.59 | 550 | 0.9368 | 0.8020 |
| 0.1065 | 3.92 | 600 | 0.9528 | 0.8282 |
| 0.0139 | 4.25 | 650 | 1.0382 | 0.7971 |
| 0.0642 | 4.58 | 700 | 1.1667 | 0.7774 |
| 0.1584 | 4.9 | 750 | 1.1346 | 0.7921 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Andrija/SRoBERTaFastBPE-2 | [
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
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} | 7 | null | ---
language:
- esp
tags:
- cecilio
- angulo
license: apache-2.0
--- |
Ankit-11/distilbert-base-uncased-finetuned-toxic | []
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fumi13/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Ann2020/model-finetuned-ner | []
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} | 0 | null | ---
license: creativeml-openrail-m
---
# Xynthii Diffusion v1
* **Dataset**: 159 images of Xynthii (credits to Hanna)
* **Prior-preservation** loss[1]
* **Train text encoder**
* **1000 steps**
* **Model**: "runwayml/stable-diffusion-v1-5"
Environmental impact:
* **Hardware Type**: 3090 24GB
* **Hours used**: 1
* **Cloud Provider**: runpod community cloud
* **Compute Region**: US-Pennsylvania
* **Carbon Emitted** [2]: 0.075 kg CO2 eq.
[1] the same images (and prompt) were used for both instance and class
[2] Power consumption x Time x Carbon produced based on location of power grid based on figures for AWS US-east
# Xynthii Diffusion epoch48
* **Dataset**: 159 images of Xynthii (credits to Hanna)
* **Prior-preservation** loss[1]
* **Train text encoder**
* **48 epochs (7632 steps)**
* **Model**: "runwayml/stable-diffusion-v1-5"
Environmental impact:
* **Hardware Type**: 3090 24GB
* **Hours used**: 3
* **Cloud Provider**: runpod community cloud
* **Compute Region**: US-Pennsylvania
* **Carbon Emitted** [2]: 0.225 kg CO2 eq.
[1] the same images (and prompt) were used for both instance and class
[2] Power consumption x Time x Carbon produced based on location of power grid based on figures for AWS US-east
# Example images from training dataset
# Example generations epoch 48
`a classical preraphaelite painting of taylor swift as a xynthii by john william waterhouse and William-Adolphe Bouguereau`
`rainbow xynthii`
# Example generations v1
All are k_lms, 50 steps, 7.5 cfg scale
`red xynthii`
`xynthii named Hanna`
`xynthii`
|
Anonymous/ReasonBERT-TAPAS | [
"pytorch",
"tapas",
"feature-extraction",
"transformers"
]
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} | 7 | null | ---
license: creativeml-openrail-m
---
## Neatmike Model
A model based on images of neatmike (messymike)
## Usage
```
neatmike person
``` |
AnonymousSub/AR_EManuals-RoBERTa | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9359059291156012
- name: Recall
type: recall
value: 0.9510265903736116
- name: F1
type: f1
value: 0.9434056761268781
- name: Accuracy
type: accuracy
value: 0.9861511744275033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Precision: 0.9359
- Recall: 0.9510
- F1: 0.9434
- Accuracy: 0.9862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0856 | 1.0 | 1756 | 0.0676 | 0.9190 | 0.9354 | 0.9271 | 0.9825 |
| 0.0344 | 2.0 | 3512 | 0.0619 | 0.9263 | 0.9470 | 0.9365 | 0.9861 |
| 0.0184 | 3.0 | 5268 | 0.0599 | 0.9359 | 0.9510 | 0.9434 | 0.9862 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/AR_SDR_HF_model_base | [
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"feature-extraction",
"transformers"
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} | 1 | null | ---
language:
- en
tags:
- ner
metrics:
- seqeval
---
## Overview
This model consists of a bert-base-cased model fine-tuned for Named Entity Recognition (NER) with 18 NE tags on the Universal Dependencies English dataset.
\
https://universaldependencies.org/en/index.html
\
The recognized NE tags are:
| CARDINAL | cardinal value |
|-----------------------|------------------------|
| DATE | date value |
| EVENT | event name |
| FAC | building name |
| GPE | geo-political entity |
| LANGUAGE | language name |
| LAW | law name |
| LOC | location name |
| MONEY | money name |
| NORP | affiliation |
| ORDINAL | ordinal value |
| ORG | organization name |
| PERCENT | percent value |
| PERSON | person name |
| PRODUCT | product name |
| QUANTITY | quantity value |
| TIME | time value |
| WORK_OF_ART | name of work of art |
A fine-tuned bert-base-uncased model is also available. |
AnonymousSub/AR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 1 | null | ---
language:
- en
tags:
- ner
metrics:
- seqeval
---
## Overview
This model consists of a bert-base-uncased model fine-tuned for Named Entity Recognition (NER) with 18 NE tags on the Universal Dependencies English dataset.
\
https://universaldependencies.org/en/index.html
\
The recognized NE tags are:
| CARDINAL | cardinal value |
|-----------------------|------------------------|
| DATE | date value |
| EVENT | event name |
| FAC | building name |
| GPE | geo-political entity |
| LANGUAGE | language name |
| LAW | law name |
| LOC | location name |
| MONEY | money name |
| NORP | affiliation |
| ORDINAL | ordinal value |
| ORG | organization name |
| PERCENT | percent value |
| PERSON | person name |
| PRODUCT | product name |
| QUANTITY | quantity value |
| TIME | time value |
| WORK_OF_ART | name of work of art |
\
A fine-tuned bert-base-cased model is also available. |
AnonymousSub/AR_rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 6 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-chinese-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-chinese-finetuned-squad
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 11.3796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7051 | 1.0 | 6911 | 11.3796 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/AR_rule_based_roberta_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 4 | null | ---
license: mit
language:
- pt
tags:
- generated_from_trainer
datasets:
- Luciano/lener_br_text_to_lm
model-index:
- name: bert-base-multilingual-cased-finetuned-lener_br
results: []
widget:
- text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes."
- text: "O autor sustenta que a lei é formal e materialmente inconstitucional, em violação aos arts. 15, XIV e XV, 19, caput, 53, 71, §1º, I , e 100, VI e X, da Lei Orgânica do DF, uma vez que, ( i ) originou-se de iniciativa parlamentar quando necessáriainiciativa privativa do Chefe do Poder Executivo, suscitando, inclusive, violação ao postulado constitucional da `` reserva de administração '', a qual impede a ingerência normativa do Poder [MASK] em matérias de competência executiva ; ( ii ) autoriza a delegação de poder de polícia , atividade típica e exclusiva de Estado , na medida em que permite ao Distrito Federal firmar convênios com o Conselho Regional de Engenharia e Agronomia do Distrito Federal - CREA-DF e com o Conselho de Arquitetura e Urbanismo do Distrito Federal - CAU-DF para, por meio do seu corpo técnico e credenciados, atuarem na análise de processos de concessão de Alvará de Construção e de Carta de Habite-se."
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-lener_br
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [Luciano/lener_br_text_to_lm](https://huggingface.co/datasets/Luciano/lener_br_text_to_lm) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8132 (To update)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters (To update)
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results (To update)
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3167 | 1.0 | 2079 | 1.1163 |
| 1.1683 | 2.0 | 4158 | 1.0594 |
| 1.0648 | 3.0 | 6237 | 1.0501 |
| 1.0228 | 4.0 | 8316 | 0.9693 |
| 0.9662 | 5.0 | 10395 | 0.9847 |
| 0.9422 | 6.0 | 12474 | 0.9556 |
| 0.8696 | 7.0 | 14553 | 0.8978 |
| 0.7856 | 8.0 | 16632 | nan |
| 0.7849 | 9.0 | 18711 | 0.9192 |
| 0.7559 | 10.0 | 20790 | 0.8536 |
| 0.7564 | 11.0 | 22869 | 0.9230 |
| 0.7641 | 12.0 | 24948 | 0.8852 |
| 0.7007 | 13.0 | 27027 | 0.8616 |
| 0.7139 | 14.0 | 29106 | 0.8419 |
| 0.6543 | 15.0 | 31185 | 0.8460 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AnonymousSub/AR_specter | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 2 | null | Access to model Madiator2011/Lyoko-Diffusion-v1.1 is restricted and you are not in the authorized list. Visit https://huggingface.co/Madiator2011/Lyoko-Diffusion-v1.1 to ask for access. |
AnonymousSub/EManuals_BERT_squad2.0 | [
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"transformers",
"autotrain_compatible"
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} | 1 | null | ---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- brazilian_court_decisions
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: train
args: lener_br
metrics:
- name: Precision
type: precision
value: 0
- name: Recall
type: recall
value: 0
- name: F1
type: f1
value: 0
- name: Accuracy
type: accuracy
value: 0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan (To update)
- Precision: 0.9122 (To update)
- Recall: 0.9163 (To update)
- F1: 0.9142 (To update)
- Accuracy: 0.9826 (To update)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters (To update)
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results (To update)
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.068 | 1.0 | 3914 | nan | 0.6196 | 0.8604 | 0.7204 | 0.9568 |
| 0.0767 | 2.0 | 7828 | nan | 0.8270 | 0.8710 | 0.8484 | 0.9693 |
| 0.0257 | 3.0 | 11742 | nan | 0.7243 | 0.9005 | 0.8029 | 0.9639 |
| 0.0193 | 4.0 | 15656 | nan | 0.9010 | 0.8984 | 0.8997 | 0.9821 |
| 0.0156 | 5.0 | 19570 | nan | 0.7150 | 0.9121 | 0.8016 | 0.9641 |
| 0.0165 | 6.0 | 23484 | nan | 0.7640 | 0.8796 | 0.8177 | 0.9691 |
| 0.0225 | 7.0 | 27398 | nan | 0.8851 | 0.9098 | 0.8973 | 0.9803 |
| 0.016 | 8.0 | 31312 | nan | 0.9081 | 0.9015 | 0.9048 | 0.9792 |
| 0.0078 | 9.0 | 35226 | nan | 0.8941 | 0.8863 | 0.8902 | 0.9788 |
| 0.0061 | 10.0 | 39140 | nan | 0.9026 | 0.9002 | 0.9014 | 0.9804 |
| 0.0057 | 11.0 | 43054 | nan | 0.8793 | 0.9018 | 0.8904 | 0.9769 |
| 0.0044 | 12.0 | 46968 | nan | 0.8790 | 0.9033 | 0.8910 | 0.9785 |
| 0.0043 | 13.0 | 50882 | nan | 0.9122 | 0.9163 | 0.9142 | 0.9826 |
| 0.0003 | 14.0 | 54796 | nan | 0.9032 | 0.9070 | 0.9051 | 0.9807 |
| 0.0025 | 15.0 | 58710 | nan | 0.8903 | 0.9085 | 0.8993 | 0.9798 |
### Framework versions (To update)
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/SR_SDR_HF_model_base | [
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} | 1 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
### Classic Animation Diffusion
This is the fine-tuned Stable Diffusion model trained on screenshots from a popular animation studio.
Use the tokens **_classic disney style_** in your prompts for the effect.
**If you enjoy my work, please consider supporting me**
[](https://patreon.com/user?u=79196446)
**Characters rendered with the model:**
**Animals rendered with the model:**
**Cars and Landscapes rendered with the model:**
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "nitrosocke/classic-anim-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "classic disney style magical princess with golden hair"
image = pipe(prompt).images[0]
image.save("./magical_princess.png")
```
#### Prompt and settings for Helen Mirren:
**classic disney style helen mirren as a queen**
_Steps: 30, Sampler: Euler a, CFG scale: 7, Seed: 3496225274, Size: 512x704_
#### Prompt and settings for the Ford Model T:
**classic disney style Ford Model T - Negative prompt: person**
_Steps: 20, Sampler: DPM2 Karras, CFG scale: 7, Seed: 4817981, Size: 704x512_
This model was trained using the diffusers based dreambooth training by ShivamShrirao using prior-preservation loss and the _train-text-encoder_ flag in 9.000 steps.
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
AnonymousSub/SR_cline | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
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} | 6 | null | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-hkuoct-exu-256-200ep
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `imagefolder` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/geevegeorge/ddpm-hkuoct-exu-256-200ep/tensorboard?#scalars)
|
AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 3 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2500 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 2500,
"warmup_steps": 250,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 4 | 2022-10-30T18:29:56Z | ---
license: mit
---
### emily carroll style on Stable Diffusion via Dreambooth
#### model by hiero
This your the Stable Diffusion model fine-tuned the emily carroll style concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **a detailed digital matte illustration by sks**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
|
AnonymousSub/cline_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 8 | null | ---
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/dallinmackay/Tron-Legacy-diffusion/resolve/main/trnlgcy-preview.jpg"
tags:
- stable-diffusion
- text-to-image
---
### Tron Legacy Diffusion
This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from the film **_Tron: Legacy (2010)_**. Use the token **_trnlgcy_** in your prompts to use the style.
_Download the ckpt file from "files and versions" tab into the stable diffusion models folder of your web-ui of choice._
--
**Characters rendered with this model:**
_prompt and settings used: **[person] in the style of trnlgcy** | **Steps: 25, Sampler: Euler a, CFG scale: 7.5**_
--
**Landscapes/scenes rendered with this model:**
_prompt and settings used: **city landscape in the style of trnlgcy** | **Steps: 25, Sampler: Euler a, CFG scale: 7.5**_
--
This model was trained with Dreambooth training by TheLastBen, using 30 images at 3000 steps.
--
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
--
[](https://www.patreon.com/dallinmackay) |
AnonymousSub/declutr-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 5 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 940.00 +/- 81.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnonymousSub/rule_based_bert_mean_diff_epochs_1_shard_10 | [
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"bert",
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"transformers"
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} | 4 | null | ---
license: creativeml-openrail-m
---
git lfs install
git clone https://huggingface.co/clohre1999/Lohre1 |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1 | [
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"bert",
"feature-extraction",
"transformers"
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}
}
} | 8 | null | ---
tags:
- conversational
---
# Pearl DialoGPT Model
|
AnonymousSub/rule_based_only_classfn_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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}
}
} | 7 | null | data: https://github.com/BigSalmon2/InformalToFormalDataset
Text Generation Informal Formal
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln88Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above):
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer]
***
microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer]
***
```
```
original: microsoft word's [MASK] pricing invites competition.
Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition.
***
original: the library’s quiet atmosphere encourages visitors to [blank] in their work.
Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work.
```
Backwards
```
Essay Intro (National Parks):
text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ).
***
Essay Intro (D.C. Statehood):
washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ).
```
```
topic: the Golden State Warriors.
characterization 1: the reigning kings of the NBA.
characterization 2: possessed of a remarkable cohesion.
characterization 3: helmed by superstar Stephen Curry.
characterization 4: perched atop the league’s hierarchy.
characterization 5: boasting a litany of hall-of-famers.
***
topic: emojis.
characterization 1: shorthand for a digital generation.
characterization 2: more versatile than words.
characterization 3: the latest frontier in language.
characterization 4: a form of self-expression.
characterization 5: quintessentially millennial.
characterization 6: reflective of a tech-centric world.
***
topic:
```
```
regular: illinois went against the census' population-loss prediction by getting more residents.
VBG: defying the census' prediction of population loss, illinois experienced growth.
***
regular: microsoft word’s high pricing increases the likelihood of competition.
VBG: extortionately priced, microsoft word is inviting competition.
***
regular:
```
```
source: badminton should be more popular in the US.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more
text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing.
***
source: movies in theaters should be free.
QUERY: Based on the given topic, can you develop a story outline?
target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money
text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay.
***
source:
```
```
in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure.
***
the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule.
***
the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement.
***
```
```
it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise.
question: what does “do likewise” mean in the above context?
(a) make the same journey
(b) share in the promise of the american dream
(c) start anew in the land of opportunity
(d) make landfall on the united states
***
in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure.
question: what does “this orientation” mean in the above context?
(a) visible business practices
(b) candor with the public
(c) open, honest communication
(d) culture of accountability
```
```
example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot.
text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities.
***
example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear.
text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student.
```
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult
(a) in reverential tones
(b) with great affection
(c) in adulatory fashion
(d) in glowing terms
```
```
clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ).
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
*Note* Of all the masking techniques, this one works the best.
```
<Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle>
***
<Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle>
```
```
essence: when someone's views are keeping within reasonable.
refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ).
***
essence: when things are worked through in a petty way.
refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling.
```
```
description: when someone thinks that their view is the only right one.
synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous.
***
description: when you put something off.
synonyms: shelve, defer, table, postpone.
```
```
organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea.
rewrite phrases: meritocratic, viability, vision
rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability.
```
```
music before bedtime [makes for being able to relax] -> is a recipe for relaxation.
```
```
[people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway.
```
```
in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal.
***
politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ).
```
```
Q: What is whistleblower protection?
A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer.
Q: Why are whistleblower protections important?
A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution.
Q: Why would an employer engage in retribution?
A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing.
```
```
original: the meritocratic nature of crowdfunding [MASK] into their vision's viability.
infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability.
``` |
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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"translation_en_to_ro": {
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}
}
} | 10 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: og-deberta-extra-o
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. -->
# og-deberta-extra-o
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5184
- Precision: 0.5981
- Recall: 0.6667
- F1: 0.6305
- Accuracy: 0.9226
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 55 | 0.4813 | 0.2863 | 0.3467 | 0.3136 | 0.8720 |
| No log | 2.0 | 110 | 0.3469 | 0.4456 | 0.4587 | 0.4520 | 0.9010 |
| No log | 3.0 | 165 | 0.3166 | 0.5206 | 0.5387 | 0.5295 | 0.9147 |
| No log | 4.0 | 220 | 0.3338 | 0.4899 | 0.584 | 0.5328 | 0.9087 |
| No log | 5.0 | 275 | 0.3166 | 0.5625 | 0.648 | 0.6022 | 0.9198 |
| No log | 6.0 | 330 | 0.3464 | 0.5707 | 0.6027 | 0.5863 | 0.9207 |
| No log | 7.0 | 385 | 0.3548 | 0.5489 | 0.6133 | 0.5793 | 0.9207 |
| No log | 8.0 | 440 | 0.4005 | 0.6125 | 0.6027 | 0.6075 | 0.9210 |
| No log | 9.0 | 495 | 0.4185 | 0.5763 | 0.6347 | 0.6041 | 0.9171 |
| 0.2019 | 10.0 | 550 | 0.4174 | 0.5596 | 0.6507 | 0.6017 | 0.9179 |
| 0.2019 | 11.0 | 605 | 0.4558 | 0.5603 | 0.632 | 0.5940 | 0.9179 |
| 0.2019 | 12.0 | 660 | 0.4615 | 0.5632 | 0.6533 | 0.6049 | 0.9166 |
| 0.2019 | 13.0 | 715 | 0.4899 | 0.5815 | 0.6187 | 0.5995 | 0.9208 |
| 0.2019 | 14.0 | 770 | 0.4800 | 0.5581 | 0.64 | 0.5963 | 0.9186 |
| 0.2019 | 15.0 | 825 | 0.4752 | 0.5905 | 0.6613 | 0.6239 | 0.9212 |
| 0.2019 | 16.0 | 880 | 0.5014 | 0.5773 | 0.6373 | 0.6058 | 0.9174 |
| 0.2019 | 17.0 | 935 | 0.5095 | 0.5917 | 0.6453 | 0.6173 | 0.9195 |
| 0.2019 | 18.0 | 990 | 0.5249 | 0.5807 | 0.6427 | 0.6101 | 0.9203 |
| 0.0077 | 19.0 | 1045 | 0.5086 | 0.5761 | 0.656 | 0.6135 | 0.9222 |
| 0.0077 | 20.0 | 1100 | 0.5108 | 0.5962 | 0.6693 | 0.6307 | 0.9219 |
| 0.0077 | 21.0 | 1155 | 0.5144 | 0.5977 | 0.6853 | 0.6385 | 0.9231 |
| 0.0077 | 22.0 | 1210 | 0.5176 | 0.5990 | 0.6613 | 0.6286 | 0.9229 |
| 0.0077 | 23.0 | 1265 | 0.5171 | 0.6039 | 0.6667 | 0.6337 | 0.9226 |
| 0.0077 | 24.0 | 1320 | 0.5184 | 0.6043 | 0.672 | 0.6364 | 0.9226 |
| 0.0077 | 25.0 | 1375 | 0.5184 | 0.5981 | 0.6667 | 0.6305 | 0.9226 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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} | 28 | 2022-10-31T04:31:30Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: summary71/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AnonymousSub/rule_based_roberta_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | {
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} | 4 | 2022-11-01T20:15:58Z | ---
language: vi
datasets:
- oscar
---
# NlpHUST/roberta-base-vn
## Model description
This is a Vietnamese RoBERTa base model pretrained on Vietnamese Oscar dataset.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("NlpHUST/roberta-base-vn")
model = AutoModelForMaskedLM.from_pretrained("NlpHUST/roberta-base-vn")
You can fine-tune this model on downstream tasks.
|
AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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}
} | 6 | 2022-10-31T05:23:10Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-tweet-gpt2
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. -->
# finetuning-sentiment-model-tweet-gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3646
- Accuracy: 0.6908
- F1: 0.6908
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_roberta_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
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} | 2 | 2022-10-31T06:37:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-WIKI_Climber_Finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SciBERT-WIKI_Climber_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0794 | 1.0 | 2364 | 0.0723 |
| 0.0565 | 2.0 | 4728 | 0.0649 |
| 0.0399 | 3.0 | 7092 | 0.0700 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 4 | 2022-10-31T06:49:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_squad2.0 | [
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"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 4 | null | ---
language:
- ja
license: mit
tags:
- bart
- pytorch
datasets:
- wikipedia
---
# bart-base-japanese
This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University.
Both the encoder and decoder outputs are identical to the original Fairseq model.
### How to use the model
The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-base-japanese/blob/main/tokenization_bart_japanese.py).
Tokenizer requirements:
* [Juman++](https://github.com/ku-nlp/jumanpp)
* [zenhan](https://pypi.org/project/zenhan/)
* [pyknp](https://pypi.org/project/pyknp/)
* [sentencepiece](https://pypi.org/project/sentencepiece/)
#### Simple FillMaskPipeline
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = "Formzu/bart-base-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
out = fill_mask(masked_text)
print(out)
# [{'score': 0.19255658984184265, 'token': 1718, 'token_str': 'よく', 'sequence': '天気 が よく から 散歩 し ましょう 。'},
# {'score': 0.14426815509796143, 'token': 5478, 'token_str': '良く', 'sequence': '天気 が 良く から 散歩 し ましょう 。'},
# {'score': 0.05554169788956642, 'token': 6561, 'token_str': '悪い', 'sequence': '天気 が 悪い から 散歩 し ましょう 。'},
# {'score': 0.05524599179625511, 'token': 3553, 'token_str': '良い', 'sequence': '天気 が 良い から 散歩 し ましょう 。'},
# {'score': 0.03720080852508545, 'token': 1370, 'token_str': '良', 'sequence': '天気 が 良 から 散歩 し ましょう 。'}]
```
#### Text Generation
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "Formzu/bart-base-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
inp = tokenizer(masked_text, return_tensors='pt').to(device)
out = model.generate(**inp, num_beams=1, min_length=0, max_length=20, early_stopping=True, no_repeat_ngram_size=2)
res = "".join(tokenizer.decode(out.squeeze(0).tolist(), skip_special_tokens=True).split(" "))
print(res)
# 天気がよくなってから散歩しましょう。天気のよく合っているところにいる
```
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
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"RobertaForSequenceClassification"
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} | 25 | 2022-10-31T06:53:19Z | ---
language:
- ja
license: mit
tags:
- bart
- pytorch
datasets:
- wikipedia
---
# bart-large-japanese
This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University.
Both the encoder and decoder outputs are identical to the original Fairseq model.
### How to use the model
The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-large-japanese/blob/main/tokenization_bart_japanese.py).
Tokenizer requirements:
* [Juman++](https://github.com/ku-nlp/jumanpp)
* [zenhan](https://pypi.org/project/zenhan/)
* [pyknp](https://pypi.org/project/pyknp/)
* [sentencepiece](https://pypi.org/project/sentencepiece/)
#### Simple FillMaskPipeline
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
out = fill_mask(masked_text)
print(out)
# [{'score': 0.03228279948234558, 'token': 2566, 'token_str': 'いい', 'sequence': '天気 が いい から 散歩 し ましょう 。'},
# {'score': 0.023878807201981544, 'token': 27365, 'token_str': '晴れ', 'sequence': '天気 が 晴れ から 散歩 し ましょう 。'},
# {'score': 0.020059829577803612, 'token': 267, 'token_str': '南', 'sequence': '天気 が 南 から 散歩 し ましょう 。'},
# {'score': 0.013921134173870087, 'token': 17, 'token_str': 'な', 'sequence': '天気 が な から 散歩 し ましょう 。'},
# {'score': 0.013069136068224907, 'token': 1718, 'token_str': 'よく', 'sequence': '天気 が よく から 散歩 し ましょう 。'}]
```
#### Text Generation
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
inp = tokenizer(masked_text, return_tensors='pt').to(device)
out = model.generate(**inp, num_beams=1, min_length=0, max_length=20, early_stopping=True, no_repeat_ngram_size=2)
res = "".join(tokenizer.decode(out.squeeze(0).tolist(), skip_special_tokens=True).split(" "))
print(res)
# 天気がいいから散歩しましょう。天気のいいへやから、ここから
```
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_squad2.0 | [
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"transformers",
"autotrain_compatible"
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} | 4 | 2022-10-31T07:47:55Z | ---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
datasets:
- KsponSpeech
metrics:
- wer # Example: wer. Use metric id from https://hf.co/metrics
---
# ko-spelling-wav2vec2-conformer-del-1s
## Table of Contents
- [ko-spelling-wav2vec2-conformer-del-1s](#ko-spelling-wav2vec2-conformer-del-1s)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description:**
해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다. <br />
Wav2Vec2ConformerForCTC를 이용하여 KsponSpeech에 대한 Fine-Tuning 모델입니다. <br />
- Dataset use [AIHub KsponSpeech](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123) <br />
Datasets는 해당 Data를 전처리하여 임의로 만들어 사용하였습니다. <br />
del-1s의 의미는 1초 이하의 데이터 필터링을 의미합니다. <br />
해당 모델은 **철자전사** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 각 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother, @ddobokki, @jp42maru)
- **Language(s):** Korean
- **License:** apache-2.0
- **Parent Model:** See the [wav2vec2-conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer) for more information about the pre-trained base model. (해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다.)
## Evaluation
Just using `load_metric("wer")` and `load_metric("wer")` in huggingface `datasets` library <br />
## How to Get Started With the Model
KenLM과 혼용된 Wav2Vec2ProcessorWithLM 예제를 보시려면 [42maru-kenlm 예제](https://huggingface.co/42MARU/ko-ctc-kenlm-spelling-only-wiki)를 참고하세요
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s")
beamsearch_decoder = build_ctcdecoder(
labels=list(tokenizer.encoder.keys()),
kenlm_model_path=None,
)
processor = Wav2Vec2ProcessorWithLM(
feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=beamsearch_decoder
)
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 123 테스트입니다.
```
*Beam-100 Result (WER)*:
| "clean" | "other" |
| ------- | ------- |
| 22.01 | 27.34 | |
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"RobertaForSequenceClassification"
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} | 24 | null | ---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- audio
- automatic-speech-recognition
datasets:
- KsponSpeech
metrics:
- wer # Example: wer. Use metric id from https://hf.co/metrics
---
# ko-42maru-wav2vec2-conformer-del-1s
## Table of Contents
- [ko-42maru-wav2vec2-conformer-del-1s](#ko-42maru-wav2vec2-conformer-del-1s)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description:**
해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다. <br />
Wav2Vec2ConformerForCTC를 이용하여 KsponSpeech에 대한 Fine-Tuning 모델입니다. <br />
- Dataset use [AIHub KsponSpeech](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123) <br />
Datasets는 해당 Data를 전처리하여 임의로 만들어 사용하였습니다. <br />
del-1s의 의미는 1초 이하의 데이터 필터링을 의미합니다. <br />
해당 모델은 **음성전사를 자체 커스텀한 42maru** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 한글 표기법을 따름) <br />
- **Developed by:** TADev (@lIlBrother, @ddobokki, @jp42maru)
- **Language(s):** Korean
- **License:** apache-2.0
- **Parent Model:** See the [wav2vec2-conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer) for more information about the pre-trained base model. (해당 모델은 wav2vec2-conformer base architecture에 scratch pre-training 되었습니다.)
## Evaluation
Just using `load_metric("wer")` and `load_metric("wer")` in huggingface `datasets` library <br />
## How to Get Started With the Model
KenLM과 혼용된 Wav2Vec2ProcessorWithLM 예제를 보시려면 [42maru-kenlm 예제](https://huggingface.co/42MARU/ko-ctc-kenlm-42maru-only-wiki)를 참고하세요
```python
import librosa
from pyctcdecode import build_ctcdecoder
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForCTC,
AutoTokenizer,
Wav2Vec2ProcessorWithLM,
)
from transformers.pipelines import AutomaticSpeechRecognitionPipeline
audio_path = ""
# 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다.
model = AutoModelForCTC.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-42maru-wav2vec2-conformer-del-1s")
beamsearch_decoder = build_ctcdecoder(
labels=list(tokenizer.encoder.keys()),
kenlm_model_path=None,
)
processor = Wav2Vec2ProcessorWithLM(
feature_extractor=feature_extractor, tokenizer=tokenizer, decoder=beamsearch_decoder
)
# 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입.
asr_pipeline = AutomaticSpeechRecognitionPipeline(
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
decoder=processor.decoder,
device=-1,
)
# 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다.
raw_data, _ = librosa.load(audio_path, sr=16000)
kwargs = {"decoder_kwargs": {"beam_width": 100}}
pred = asr_pipeline(inputs=raw_data, **kwargs)["text"]
# 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다.
result = unicodedata.normalize("NFC", pred)
print(result)
# 안녕하세요 하나둘셋 테스트입니다.
```
*Beam-100 Result (WER)*:
| "clean" | "other" |
| ------- | ------- |
| 21.52 | 25.72 | |
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | {
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"RobertaForSequenceClassification"
],
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}
} | 25 | 2022-10-31T07:59:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2119 | 1.0 | 5533 | 1.1532 |
| 0.9427 | 2.0 | 11066 | 1.1100 |
| 0.7477 | 3.0 | 16599 | 1.1543 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"RobertaModel"
],
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}
} | 5 | 2022-10-31T08:44:05Z | ---
tags:
- generated_from_trainer
model-index:
- name: SST2_BERT_5E
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. -->
# SST2_BERT_5E
This model was trained from scratch 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | {
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"RobertaModel"
],
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}
} | 6 | 2022-10-31T08:53:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-lr-3e-05-wd-001
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.919
- name: F1
type: f1
value: 0.9191101562818575
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-lr-3e-05-wd-001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2415
- Accuracy: 0.919
- F1: 0.9191
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9356 | 1.0 | 125 | 0.3832 | 0.8895 | 0.8855 |
| 0.2866 | 2.0 | 250 | 0.2415 | 0.919 | 0.9191 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
| feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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} | 6 | 2022-10-31T09:00:15Z | ---
language: en
datasets:
- Dizex/FoodBase
widget:
- text: "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"
example_title: "Food example 1"
- text: "Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water."
example_title: "Food example 2"
tags:
- FoodBase
- NER
license: mit
---
# FoodBaseBERT
## Model description
**FoodBaseBERT** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities. It has been trained to recognize one entity: food (FOOD).
Specifically, this model is a *bert-base-cased* model that was fine-tuned on the [FoodBase NER](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291) dataset.
## Intended uses
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Dizex/FoodBaseBERT")
model = AutoModelForTokenClassification.from_pretrained("Dizex/FoodBaseBERT")
pipe = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"
ner_entity_results = pipe(example)
print(ner_entity_results)
```
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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}
} | 30 | 2022-10-31T09:00:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-lr-2e-05-wd-0001
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.896
- name: F1
type: f1
value: 0.8928636709995518
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-lr-2e-05-wd-0001
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3670
- Accuracy: 0.896
- F1: 0.8929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0933 | 1.0 | 125 | 0.5691 | 0.8335 | 0.8121 |
| 0.4488 | 2.0 | 250 | 0.3670 | 0.896 | 0.8929 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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"BertModel"
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}
} | 1 | 2022-10-31T09:01:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion-lr-0.0006-wd-0003
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8875
- name: F1
type: f1
value: 0.8888949280126286
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-lr-0.0006-wd-0003
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4198
- Accuracy: 0.8875
- F1: 0.8889
## 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: 0.0006
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1911 | 1.0 | 125 | 0.6098 | 0.808 | 0.7921 |
| 0.4819 | 2.0 | 250 | 0.4198 | 0.8875 | 0.8889 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/specter-bert-model_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | 2022-10-31T09:34:29Z | ---
license: mit
---
# BART models fine tuned for keyphrase generation
## About
This repository contains 5 models that were trained and evaluated on the three datasets KPBiomed, KP20k and KPTimes.
Details about the models and the KPBiomed dataset can be found in the original paper: Maël Houbre, Florian Boudin and Béatrice Daille. 2022. A Large-Scale Dataset for Biomedical Keyphrase Generation. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI 2022).
## How to use
As this repository contains several models, using the huggingface API directly will not work.
To use one of the 4 models, you need to first download the desired zip file and unzip it.
For example, if we take the biobart-medium model and unzip it in our source directory. We will be able to load the model with the API as below.
from transformers import BartTokenizerFast, BartForConditionalGeneration
tokenizer = BartTokenizerFast.from_pretrained("biobart-medium")
model = BartForConditionalGeneration.from_pretrained("biobart-medium")
model.to("cuda")
We will then be able to generate keyphrases with the model using Hugging Face's generate function
inputs = tokenizer(input_text, padding="max_length", max_length= 512, truncation=True, return_tensors='pt')
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
outputs = model.generate(inputs=input_ids,attention_mask=attention_mask,
num_beams=20,
num_return_sequences=20
)
keyphrase_sequence = tokenizer.batch_decode(outputs,skip_special_tokens=False)
|
AnonymousSub/unsup-consert-emanuals | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
]
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} | 2 | 2022-10-31T10:12:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-ashishkhandelwal
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
args: default
metrics:
- name: Rouge1
type: rouge
value: 14.7089
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-ashishkhandelwal
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7453
- Rouge1: 14.7089
- Rouge2: 4.6798
- Rougel: 11.2024
- Rougelsum: 13.0025
- Gen Len: 18.9968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9805 | 1.0 | 2811 | 2.7453 | 14.7089 | 4.6798 | 11.2024 | 13.0025 | 18.9968 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Anonymreign/savagebeta | []
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} | 0 | 2022-10-31T10:32:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: VANBase-finetuned-brs-finetuned-brs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5882352941176471
- name: F1
type: f1
value: 0.6956521739130435
---
<!-- 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. -->
# VANBase-finetuned-brs-finetuned-brs
This model is a fine-tuned version of [Visual-Attention-Network/van-base](https://huggingface.co/Visual-Attention-Network/van-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7056
- Accuracy: 0.5882
- F1: 0.6957
- Precision (ppv): 0.6154
- Recall (sensitivity): 0.8
- Specificity: 0.2857
- Npv: 0.5
- Auc: 0.5429
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6589 | 6.25 | 100 | 0.6655 | 0.5882 | 0.6316 | 0.6667 | 0.6 | 0.5714 | 0.5 | 0.5857 |
| 0.6262 | 12.49 | 200 | 0.6917 | 0.5294 | 0.6364 | 0.5833 | 0.7 | 0.2857 | 0.4 | 0.4929 |
| 0.4706 | 18.74 | 300 | 0.6776 | 0.5882 | 0.6957 | 0.6154 | 0.8 | 0.2857 | 0.5 | 0.5429 |
| 0.5202 | 24.98 | 400 | 0.7018 | 0.5294 | 0.6 | 0.6 | 0.6 | 0.4286 | 0.4286 | 0.5143 |
| 0.4628 | 31.25 | 500 | 0.6903 | 0.6471 | 0.75 | 0.6429 | 0.9 | 0.2857 | 0.6667 | 0.5929 |
| 0.3525 | 37.49 | 600 | 0.7241 | 0.5294 | 0.6667 | 0.5714 | 0.8 | 0.1429 | 0.3333 | 0.4714 |
| 0.2877 | 43.74 | 700 | 0.8262 | 0.5882 | 0.7407 | 0.5882 | 1.0 | 0.0 | nan | 0.5 |
| 0.2921 | 49.98 | 800 | 0.8058 | 0.4706 | 0.64 | 0.5333 | 0.8 | 0.0 | 0.0 | 0.4 |
| 0.3834 | 56.25 | 900 | 0.7864 | 0.5882 | 0.7407 | 0.5882 | 1.0 | 0.0 | nan | 0.5 |
| 0.2267 | 62.49 | 1000 | 0.5520 | 0.7647 | 0.8182 | 0.75 | 0.9 | 0.5714 | 0.8 | 0.7357 |
| 0.3798 | 68.74 | 1100 | 0.8722 | 0.4706 | 0.64 | 0.5333 | 0.8 | 0.0 | 0.0 | 0.4 |
| 0.2633 | 74.98 | 1200 | 0.7260 | 0.6471 | 0.7273 | 0.6667 | 0.8 | 0.4286 | 0.6 | 0.6143 |
| 0.3439 | 81.25 | 1300 | 1.0187 | 0.4118 | 0.5455 | 0.5 | 0.6 | 0.1429 | 0.2 | 0.3714 |
| 0.2532 | 87.49 | 1400 | 0.8812 | 0.5882 | 0.7407 | 0.5882 | 1.0 | 0.0 | nan | 0.5 |
| 0.0841 | 93.74 | 1500 | 0.8717 | 0.5294 | 0.6923 | 0.5625 | 0.9 | 0.0 | 0.0 | 0.45 |
| 0.3409 | 99.98 | 1600 | 0.7056 | 0.5882 | 0.6957 | 0.6154 | 0.8 | 0.2857 | 0.5 | 0.5429 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnthonyNelson/DialoGPT-small-ricksanchez | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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} | 12 | 2022-10-31T10:38:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: reviews-generator2
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. -->
# reviews-generator2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5013
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8034 | 0.08 | 500 | 3.5646 |
| 3.749 | 0.16 | 1000 | 3.5013 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.1+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Anthos23/distilbert-base-uncased-finetuned-sst2 | [
"tf",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"license:apache-2.0"
]
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} | 21 | 2022-10-31T10:53:09Z | # Simple CNN for knotted x unknotted recognition
The model is trained on downsampled SPOUT knotted x Rossmann unknotted dataset to fix the knotted x unknotted sequence length distribution
The architecture is [PENGUINN](https://www.frontiersin.org/articles/10.3389/fgene.2020.568546/full) with modified input |
Anthos23/my-awesome-model | [
"pytorch",
"tf",
"roberta",
"text-classification",
"transformers"
]
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} | 30 | null | Access to model do0110/MLStudy0110 is restricted and you are not in the authorized list. Visit https://huggingface.co/do0110/MLStudy0110 to ask for access. |
Anthos23/sentiment-roberta-large-english-finetuned-sentiment-analysis | []
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} | 0 | 2022-10-31T11:05:24Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: week5-eng-distilbert-base-multilingual-cased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# week5-eng-distilbert-base-multilingual-cased-finetuned
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0855
- Precision: 0.2813
- Recall: 0.2949
- F1: 0.2880
- Accuracy: 0.9763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1071 | 1.0 | 924 | 0.0911 | 0.2472 | 0.0444 | 0.0753 | 0.9755 |
| 0.07 | 2.0 | 1848 | 0.0891 | 0.3206 | 0.1697 | 0.2219 | 0.9775 |
| 0.0522 | 3.0 | 2772 | 0.0855 | 0.2813 | 0.2949 | 0.2880 | 0.9763 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Anthos23/test_trainer | []
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} | 0 | 2022-10-31T11:07:07Z |
---
language:
- pt
thumbnail: "Portuguese BERT for the Legal Domain"
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- transformers
datasets:
- assin
- assin2
- stsb_multi_mt
- rufimelo/PortugueseLegalSentences-v2
widget:
- source_sentence: "O advogado apresentou as provas ao juíz."
sentences:
- "O juíz leu as provas."
- "O juíz leu o recurso."
- "O juíz atirou uma pedra."
example_title: "Example 1"
model-index:
- name: BERTimbau
results:
- task:
name: STS
type: STS
metrics:
- name: Pearson Correlation - assin Dataset
type: Pearson Correlation
value: 0.80703
- name: Pearson Correlation - assin2 Dataset
type: Pearson Correlation
value: 0.82270
- name: Pearson Correlation - stsb_multi_mt pt Dataset
type: pearsonr
value: 0.77638
---
# rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3 is based on Legal-BERTimbau-large which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large.
It is adapted to the Portuguese legal domain and trained for STS on portuguese datasets.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Isto é um exemplo", "Isto é um outro exemplo"]
model = SentenceTransformer('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3')
model = AutoModel.from_pretrained('rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results STS
| Model| Assin | Assin2|stsb_multi_mt pt| avg|
| ---------------------------------------- | ---------- | ---------- |---------- |---------- |
| Legal-BERTimbau-sts-base| 0.71457| 0.73545 | 0.72383|0.72462|
| Legal-BERTimbau-sts-base-ma| 0.74874 | 0.79532|0.82254 |0.78886|
| Legal-BERTimbau-sts-base-ma-v2| 0.75481 | 0.80262|0.82178|0.79307|
| Legal-BERTimbau-base-TSDAE-sts|0.78814 |0.81380 |0.75777|0.78657|
| Legal-BERTimbau-sts-large| 0.76629| 0.82357 | 0.79120|0.79369|
| Legal-BERTimbau-sts-large-v2| 0.76299 | 0.81121|0.81726 |0.79715|
| Legal-BERTimbau-sts-large-ma| 0.76195| 0.81622 | 0.82608|0.80142|
| Legal-BERTimbau-sts-large-ma-v2| 0.7836| 0.8462| 0.8261| 0.81863|
| Legal-BERTimbau-sts-large-ma-v3| 0.7749| **0.8470**| 0.8364| **0.81943**|
| Legal-BERTimbau-large-v2-sts| 0.71665| 0.80106| 0.73724| 0.75165|
| Legal-BERTimbau-large-TSDAE-sts| 0.72376| 0.79261| 0.73635| 0.75090|
| Legal-BERTimbau-large-TSDAE-sts-v2| 0.81326| 0.83130| 0.786314| 0.81029|
| Legal-BERTimbau-large-TSDAE-sts-v3|0.80703 |0.82270 |0.77638 |0.80204 |
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| BERTimbau base Fine-tuned for STS|**0.78455** | 0.80626|0.82841|0.80640|
| BERTimbau large Fine-tuned for STS|0.78193 | 0.81758|0.83784|0.81245|
| ---------------------------------------- | ---------- |---------- |---------- |---------- |
| paraphrase-multilingual-mpnet-base-v2| 0.71457| 0.79831 |0.83999 |0.78429|
| paraphrase-multilingual-mpnet-base-v2 Fine-tuned with assin(s)| 0.77641|0.79831 |**0.84575**|0.80682|
## Training
rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3 is based on rufimelo/Legal-BERTimbau-large-TSDAE-sts-v3 which derives from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) large.
rufimelo/Legal-BERTimbau-large-TSDAE was trained with TSDAE: 200000 cleaned documents (https://huggingface.co/datasets/rufimelo/PortugueseLegalSentences-v1)
'lr': 1e-5
It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2) and [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) datasets. 'lr': 1e-5
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
If you use this work, please cite BERTimbau's work:
```bibtex
@inproceedings{souza2020bertimbau,
author = {F{\'a}bio Souza and
Rodrigo Nogueira and
Roberto Lotufo},
title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
year = {2020}
}
``` |
AntonClaesson/movie-plot-generator | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
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"GPT2LMHeadModel"
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} | 9 | 2022-10-31T11:13:45Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1980 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1980,
"warmup_steps": 198,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Anubhav23/indianlegal | []
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} | 0 | 2022-10-31T11:33:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9330916282135794
- name: Recall
type: recall
value: 0.9528778189161898
- name: F1
type: f1
value: 0.9428809325562032
- name: Accuracy
type: accuracy
value: 0.9872255254017779
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0594
- Precision: 0.9331
- Recall: 0.9529
- F1: 0.9429
- Accuracy: 0.9872
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0872 | 1.0 | 1756 | 0.0631 | 0.9128 | 0.9359 | 0.9242 | 0.9827 |
| 0.0338 | 2.0 | 3512 | 0.0578 | 0.9322 | 0.9510 | 0.9415 | 0.9867 |
| 0.0174 | 3.0 | 5268 | 0.0594 | 0.9331 | 0.9529 | 0.9429 | 0.9872 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Anupam/QuestionClassifier | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: bert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9799
- Overall Precision: 0.4362
- Overall Recall: 0.5285
- Overall F1: 0.4779
- Overall Accuracy: 0.9515
- Datasetname F1: 0.3262
- Hyperparametername F1: 0.7339
- Hyperparametervalue F1: 0.7619
- Methodname F1: 0.5193
- Metricname F1: 0.6525
- Metricvalue F1: 0.6452
- Taskname F1: 0.2704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
| No log | 1.0 | 132 | 0.5733 | 0.2215 | 0.3313 | 0.2655 | 0.9350 | 0.0 | 0.1273 | 0.325 | 0.4369 | 0.2759 | 0.3478 | 0.0639 |
| No log | 2.0 | 264 | 0.4130 | 0.2929 | 0.5996 | 0.3936 | 0.9348 | 0.2930 | 0.3316 | 0.6133 | 0.5104 | 0.5622 | 0.5263 | 0.2452 |
| No log | 3.0 | 396 | 0.4654 | 0.3302 | 0.5793 | 0.4207 | 0.9431 | 0.2732 | 0.5 | 0.7463 | 0.5189 | 0.5930 | 0.5405 | 0.2228 |
| 0.5901 | 4.0 | 528 | 0.4540 | 0.3604 | 0.5772 | 0.4438 | 0.9457 | 0.2644 | 0.5271 | 0.7027 | 0.5346 | 0.6386 | 0.4878 | 0.2804 |
| 0.5901 | 5.0 | 660 | 0.5776 | 0.3920 | 0.5346 | 0.4523 | 0.9483 | 0.2529 | 0.6241 | 0.7761 | 0.5056 | 0.6369 | 0.6875 | 0.2601 |
| 0.5901 | 6.0 | 792 | 0.4903 | 0.3934 | 0.6037 | 0.4763 | 0.9493 | 0.3651 | 0.6230 | 0.6667 | 0.5775 | 0.6136 | 0.4848 | 0.3084 |
| 0.5901 | 7.0 | 924 | 0.6343 | 0.3991 | 0.5549 | 0.4643 | 0.9492 | 0.3286 | 0.6047 | 0.6301 | 0.5705 | 0.6216 | 0.5 | 0.2508 |
| 0.1282 | 8.0 | 1056 | 0.6686 | 0.4185 | 0.5691 | 0.4823 | 0.9507 | 0.3546 | 0.6565 | 0.7353 | 0.5786 | 0.6174 | 0.4615 | 0.2540 |
| 0.1282 | 9.0 | 1188 | 0.6801 | 0.4144 | 0.5610 | 0.4767 | 0.9507 | 0.3729 | 0.5616 | 0.7576 | 0.5569 | 0.5839 | 0.5263 | 0.2821 |
| 0.1282 | 10.0 | 1320 | 0.5843 | 0.4022 | 0.5976 | 0.4808 | 0.9493 | 0.3262 | 0.6667 | 0.6957 | 0.5417 | 0.6267 | 0.6286 | 0.2969 |
| 0.1282 | 11.0 | 1452 | 0.7041 | 0.4187 | 0.5813 | 0.4868 | 0.9508 | 0.3158 | 0.608 | 0.6857 | 0.5583 | 0.6174 | 0.7429 | 0.3145 |
| 0.0579 | 12.0 | 1584 | 0.7037 | 0.4143 | 0.5894 | 0.4866 | 0.9509 | 0.3310 | 0.6032 | 0.7164 | 0.5426 | 0.6497 | 0.6486 | 0.3207 |
| 0.0579 | 13.0 | 1716 | 0.6623 | 0.3961 | 0.5732 | 0.4684 | 0.9512 | 0.3490 | 0.6847 | 0.6462 | 0.5411 | 0.5987 | 0.6286 | 0.3038 |
| 0.0579 | 14.0 | 1848 | 0.7953 | 0.4443 | 0.5752 | 0.5013 | 0.9517 | 0.3704 | 0.6457 | 0.6667 | 0.5705 | 0.625 | 0.6667 | 0.3030 |
| 0.0579 | 15.0 | 1980 | 0.7321 | 0.4050 | 0.5976 | 0.4828 | 0.9482 | 0.3023 | 0.7156 | 0.7042 | 0.5815 | 0.6309 | 0.6667 | 0.2935 |
| 0.0281 | 16.0 | 2112 | 0.7175 | 0.4149 | 0.6240 | 0.4984 | 0.9503 | 0.3740 | 0.5816 | 0.6667 | 0.5756 | 0.6335 | 0.6667 | 0.3182 |
| 0.0281 | 17.0 | 2244 | 0.7463 | 0.4242 | 0.5915 | 0.4941 | 0.9507 | 0.3741 | 0.7130 | 0.7429 | 0.5678 | 0.6144 | 0.6486 | 0.2824 |
| 0.0281 | 18.0 | 2376 | 0.8040 | 0.4172 | 0.5732 | 0.4829 | 0.9531 | 0.3485 | 0.6838 | 0.7647 | 0.4946 | 0.6279 | 0.6286 | 0.3233 |
| 0.0156 | 19.0 | 2508 | 0.8922 | 0.4365 | 0.5589 | 0.4902 | 0.9525 | 0.4 | 0.6607 | 0.7500 | 0.5455 | 0.6013 | 0.6316 | 0.2803 |
| 0.0156 | 20.0 | 2640 | 0.8597 | 0.4072 | 0.5488 | 0.4675 | 0.9516 | 0.3333 | 0.6435 | 0.7213 | 0.5086 | 0.6536 | 0.5882 | 0.2975 |
| 0.0156 | 21.0 | 2772 | 0.7927 | 0.3986 | 0.5752 | 0.4709 | 0.9518 | 0.3759 | 0.6107 | 0.6857 | 0.5048 | 0.6582 | 0.7273 | 0.2802 |
| 0.0156 | 22.0 | 2904 | 0.9151 | 0.4234 | 0.5732 | 0.4870 | 0.9493 | 0.3590 | 0.7143 | 0.8060 | 0.5491 | 0.6174 | 0.7273 | 0.2305 |
| 0.0102 | 23.0 | 3036 | 0.9133 | 0.4231 | 0.5813 | 0.4897 | 0.9502 | 0.3185 | 0.6357 | 0.7941 | 0.55 | 0.6795 | 0.6061 | 0.2754 |
| 0.0102 | 24.0 | 3168 | 0.9799 | 0.4362 | 0.5285 | 0.4779 | 0.9515 | 0.3262 | 0.7339 | 0.7619 | 0.5193 | 0.6525 | 0.6452 | 0.2704 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
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} | 12 | 2022-10-31T11:48:46Z | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions:
emissions: 2.4435817864698777
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1902664733
- CO2 Emissions (in grams): 2.4436
## Validation Metrics
- Loss: 0.282
- Rouge1: 63.261
- Rouge2: 29.581
- RougeL: 63.239
- RougeLsum: 63.219
- Gen Len: 9.777
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/aalbertini90/autotrain-improve-figcaption-v2-1902664733
``` |
ArBert/albert-base-v2-finetuned-ner-agglo | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
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"AlbertForTokenClassification"
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"model_type": "albert",
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} | 8 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
This is a fork of the Microworlds model provided in [Public Prompts](https://publicprompts.art/microworlds-dreambooth-model/) for easier download and integration into services as allowed by the CreativeML-Openrails-M license
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
ArBert/albert-base-v2-finetuned-ner-gmm | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | {
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
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},
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}
} | 8 | null | ---
language: zh
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- zh
- Chinese
inference: true
widget:
- text: "孤帆远影碧空尽，惟见长江天际流,油画"
example_title: 孤帆远影碧空尽，惟见长江天际流,油画
- text: "日出在印象的港口来回, 唯美, 插画"
example_title: 日出在印象的港口来回, 唯美, 插画
- text: "科幻, 外星文明, 建筑, 机械感, 4k壁纸"
example_title: 科幻, 外星文明, 建筑, 机械感, 4k壁纸
- text: "东临碣石, 以观沧海, 波涛汹涌, 插画"
example_title: 东临碣石, 以观沧海, 波涛汹涌, 插画
- text: "飞流直下三千尺, 疑是银河落九天, 瀑布, 插画"
example_title: 飞流直下三千尺, 疑是银河落九天, 瀑布, 插画
- text: "女孩背影, 日落, 唯美插画"
example_title: 女孩背影, 日落, 唯美插画
extra_gated_prompt: |-
One more step before getting this model.
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. IDEA-CCNL claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
# Taiyi-Stable-Diffusion-1B-Chinese-v0.1
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
- API：[Fengshen-OpenAPI](https://fengshenbang-lm.com/open-api)
## 简介 Brief Introduction
首个开源的中文Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。
The first open source Chinese Stable diffusion, which was trained on 20M filtered Chinese image-text pairs.
## 在线体验 Gradio Web UI
可以在[Taiyi-Stable-Diffusion-Chinese](https://huggingface.co/spaces/IDEA-CCNL/Taiyi-Stable-Diffusion-Chinese)体验我们的模型。
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run Taiyi-Stable-Diffusion-1B-Chinese-v0.1:
[Taiyi-Stable-Diffusion-Chinese](https://huggingface.co/spaces/IDEA-CCNL/Taiyi-Stable-Diffusion-Chinese)
## 简介 Brief Introduction
首个开源的中英双语Stable Diffusion模型，基于0.2亿筛选过的中文图文对训练。
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 特殊 Special | 多模态 Multimodal | 太乙 Taiyi | Stable Diffusion | 1B | Chinese |
## 模型信息 Model Information
我们将[Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)数据集(100M)和[Zero](https://zero.so.com/)数据集(23M)用作预训练的数据集，先用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)对这两个数据集的图文对相似性进行打分，取CLIP Score大于0.2的图文对作为我们的训练集。 我们使用[IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)作为初始化的text encoder，冻住[stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([论文](https://arxiv.org/abs/2112.10752))模型的其他部分，只训练text encoder，以便保留原始模型的生成能力且实现中文概念的对齐。该模型目前在0.2亿图文对上训练了一个epoch。 我们在 32 x A100 训练了大约100小时。该版本只是一个初步的版本，我们将持续优化并开源后续模型，欢迎交流。
We use [Noah-Wukong](https://wukong-dataset.github.io/wukong-dataset/)(100M) 和 [Zero](https://zero.so.com/)(23M) as our dataset, and take the image and text pairs with CLIP Score (based on [IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese)) greater than 0.2 as our Training set. We use [IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese](https://huggingface.co/IDEA-CCNL/Taiyi-CLIP-RoBERTa-102M-ViT-L-Chinese) as our init text encoder. To keep the powerful generative capability of stable diffusion and align Chinese concepts with the images, We only train the text encoder and freeze other part of the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)([paper](https://arxiv.org/abs/2112.10752)) model. It takes 100 hours to train this model based on 32 x A100. This model is a preliminary version and we will update this model continuously and open sourse. Welcome to exchange！
### Result
Basic Prompt
| 铁马冰河入梦来，3D绘画。 | 飞流直下三千尺，油画。 | 女孩背影，日落，唯美插画。 |
| ---- | ---- | ---- |
|  |  |  |
Advanced Prompt
| 铁马冰河入梦来，概念画，科幻，玄幻，3D | 中国海边城市，科幻，未来感，唯美，插画。 | 那人却在灯火阑珊处，色彩艳丽，古风，资深插画师作品，桌面高清壁纸。 |
| ---- | ---- | ---- |
|  |  |  |
## 使用 Usage
### 全精度 Full precision
```py
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1").to("cuda")
prompt = '飞流直下三千尺，油画'
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("飞流.png")
```
### 半精度 Half precision FP16 (CUDA)
添加 `torch_dtype=torch.float16` 和 `device_map="auto"` 可以快速加载 FP16 的权重，以加快推理速度。
更多信息见 [the optimization docs](https://huggingface.co/docs/diffusers/main/en/optimization/fp16#half-precision-weights)。
```py
# !pip install git+https://github.com/huggingface/accelerate
import torch
from diffusers import StableDiffusionPipeline
torch.backends.cudnn.benchmark = True
pipe = StableDiffusionPipeline.from_pretrained("IDEA-CCNL/Taiyi-Stable-Diffusion-1B-Chinese-v0.1", torch_dtype=torch.float16)
pipe.to('cuda')
prompt = '飞流直下三千尺，油画'
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("飞流.png")
```
### 使用手册 Handbook for Taiyi
https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/stable_diffusion_chinese/taiyi_handbook.md
### 怎样微调 How to finetune
https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/finetune_taiyi_stable_diffusion
### webui配置 Configure webui
https://github.com/IDEA-CCNL/stable-diffusion-webui/blob/master/README.md
### DreamBooth
https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/stable_diffusion_dreambooth
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
ArBert/roberta-base-finetuned-ner-agglo | []
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} | 0 | null | ---
language: "de"
tags:
- text-to-speech
- TTS
- speech-synthesis
- Tacotron2
- speechbrain
license: "apache-2.0"
---
Text-to-Speech (TTS) with Tacotron2 trained on a custom german dataset with 12 days voice using speechbrain.
Trained for 39 epochs (english speechbrain models are trained for 750 epochs) so there is room for improvement and the model is most likely to be updated soon. The hifigan vocoder can fortunately be used language-independently.
## How to use
Install speechbrain.
```
pip install speechbrain
```
Generate spectrogram (line 17) and generate wav using hifigan (line 18).
```
import torchaudio
from speechbrain.pretrained import Tacotron2
from speechbrain.pretrained import HIFIGAN
# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
tacotron2 = Tacotron2.from_hparams(source="padmalcom/tts-tacotron2-german", savedir="tmpdir_tts")
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir_vocoder")
# Running the TTS
mel_output, mel_length, alignment = tacotron2.encode_text("Die Sonne schien den ganzen Tag.")
# Running Vocoder (spectrogram-to-waveform)
waveforms = hifi_gan.decode_batch(mel_output)
# Save the waverform
torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050)
``` |
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
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"GPT2LMHeadModel"
],
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}
} | 8 | 2022-10-31T14:06:24Z | <p align="center">
<img width="60%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/studiogan_logo.jpg" />
</p>**StudioGAN** is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea.
This hub provides all the checkpoints we used to create the GAN benchmarks below.
Please visit our github repository ([PyTorch-StudioGAN](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN)) for more details.
<p align="center">
<img width="95%" src="https://raw.githubusercontent.com/POSTECH-CVLab/PyTorch-StudioGAN/master/docs/figures/StudioGAN_Benchmark.png"/>
</p>
## License
PyTorch-StudioGAN is an open-source library under the MIT license (MIT). However, portions of the library are avaiiable under distinct license terms: StyleGAN2, StyleGAN2-ADA, and StyleGAN3 are licensed under [NVIDIA source code license](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN/blob/master/LICENSE-NVIDIA), and PyTorch-FID is licensed under [Apache License](https://github.com/POSTECH-CVLab/PyTorch-StudioGAN/blob/master/src/metrics/fid.py).
## Citation
StudioGAN is established for the following research projects. Please cite our work if you use StudioGAN.
```bib
@article{kang2022StudioGAN,
title = {{StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis}},
author = {MinGuk Kang and Joonghyuk Shin and Jaesik Park},
journal = {2206.09479 (arXiv)},
year = {2022}
}
```
```bib
@inproceedings{kang2021ReACGAN,
title = {{Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training}},
author = {Minguk Kang, Woohyeon Shim, Minsu Cho, and Jaesik Park},
journal = {Conference on Neural Information Processing Systems (NeurIPS)},
year = {2021}
}
```
```bib
@inproceedings{kang2020ContraGAN,
title = {{ContraGAN: Contrastive Learning for Conditional Image Generation}},
author = {Minguk Kang and Jaesik Park},
journal = {Conference on Neural Information Processing Systems (NeurIPS)},
year = {2020}
}
``` |
ArashEsk95/bert-base-uncased-finetuned-cola | []
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} | 0 | 2022-10-31T14:23:45Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: XLMRobertaz
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. -->
# XLMRobertaz
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5404
- Train End Logits Accuracy: 0.8404
- Train Start Logits Accuracy: 0.7997
- Validation Loss: 1.0036
- Validation End Logits Accuracy: 0.7448
- Validation Start Logits Accuracy: 0.7148
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 22396, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2940 | 0.6600 | 0.6215 | 0.9820 | 0.7357 | 0.7047 | 0 |
| 0.8412 | 0.7666 | 0.7252 | 0.9281 | 0.7473 | 0.7137 | 1 |
| 0.6629 | 0.8091 | 0.7681 | 0.9387 | 0.7450 | 0.7130 | 2 |
| 0.5404 | 0.8404 | 0.7997 | 1.0036 | 0.7448 | 0.7148 | 3 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Archie/myProject | []
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.94
- name: F1
type: f1
value: 0.940059296063194
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1895
- Accuracy: 0.94
- F1: 0.9401
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4628 | 1.0 | 2000 | 0.2334 | 0.9315 | 0.9312 |
| 0.1579 | 2.0 | 4000 | 0.1895 | 0.94 | 0.9401 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Arnold/wav2vec2-hausa2-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
]
| automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 491.50 +/- 141.76
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga motmono -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga motmono -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga motmono
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
Aron/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
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}
} | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DIAL-FLANT5-XL
results: []
widget:
- text: "Instruction: Edit the provided response into a response that is fluent and coherent to the dialogue context. \n\nInput: [CONTEXT] How may I help you? [ENDOFTURN] I left a suitcase on the train to London the other day. [RESPONSE] Can describe itit , sir ? It will help us find [ENDOFDIALOGUE] [QUESTION] Given this context and response provided, the edited response is"
- text: "Instruction: Generate a response that starts with the provided initial phrase. \n\nInput: [INITIAL_PHRASE] Please describe [CONTEXT] How may I help you? [ENDOFTURN] I left a suitcase on the train to London the other day. [ENDOFDIALOGUE] [QUESTION] A response with the provided initial phrase is"
- text: "Instruction: Generate a response that starts with the provided initial phrase and contains the provided keywords. \n\nInput: [INITIAL PHRASE] Please describe [KEYWORDS] color, any documents [CONTEXT] How may I help you? [ENDOFTURN] I left a suitcase on the train to London the other day. [ENDOFDIALOGUE] [QUESTION] A response with the provided initial phrase and keywords is"
- text: "Instruction: What is the intent of the response \n\nInput: [CONTEXT] How may I help you? [RESPONSE] I left a suitcase on the train to London the other day. [ENDOFDIALOGUE] [OPTIONS] booking, reservation change, checkout, lost&found, time information, security, schedules [QUESTION] The intent of the response is"
- text: "Instruction: Generate a summary for the following dialog context. \n\nInput: [CONTEXT] Ann: Wanna go out? [ENDOFTURN] Kate: Not really, I feel sick. [ENDOFTURN] Ann: Drink mint tea, they say it helps. Ok, so we'll meet up another time. Take care! [ENDOFTURN] Kate: Thanks! [ENDOFDIALOGUE] [QUESTION] For this dialogue, the summary is: "
- text: "Instruction: Consider the context of the conversation and a document and generate an answer accordingly \n\nInput: [CONTEXT] How may I help you? [ENDOFTURN] I left a suitcase on the train to London the other day. [ENDOFDIALOGUE] [QUESTION] What is the response of the following question: Where was the person going to?"
- text: "Instruction: Generate a response using the provided background knowledge. \n\nInput: [KNOWLEDGE] Emailid for cases related to lost and found is [email protected] [CONTEXT] How may I help you? [ENDOFTURN] I left a suitcase on the train to London the other day. [ENDOFDIALOGUE] [QUESTION] Generate a response using the information from the background knowledge."
---
# InstructDial
Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.
[Paper](https://arxiv.org/abs/2205.12673)
[GIT] (https://github.com/prakharguptaz/Instructdial)
# DIAL-FLANT5-XL
DIAL-FLANT5-XL is a 3B model trained on InstructDial tasks. This model is a fine-tuned version of google/flan-t5-xl model
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
All tasks in InstructDial framework (including all dialogue eval tasks)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 72
- total_eval_batch_size: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ArthurcJP/DialoGPT-small-YODA | []
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} | 0 | null | ---
license: mit
---
### DeGods on Stable Diffusion
This is the `<degods>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
AvatarXD/DialoGPT-medium-Blitzo | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
]
| conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 14 | 2022-10-31T20:56:30Z | ---
languages:
- es
tags:
- paraphrasing
- spanish
widget:
- text: "Di la siguiente frase con otras palabras: \nFrase original: La compañía Narrativa libera algunos modelos de NLP en el Hub de Hugging Face.\nFrase parafraseada:"
license: wtfpl
---
# GPT-J-6B-ES-8bit fine-tuned on PAWS-X (ES) For Paraphrasing
## This model card is WIP! |
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
]
| null | {
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}
} | 0 | 2022-10-31T21:35:27Z | ---
tags:
- generated_from_trainer
datasets:
- swequad
model-index:
- name: bert-base-swedish-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-swedish-cased-finetuned-squad
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased](https://huggingface.co/KBLab/bert-base-swedish-cased) on the swequad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0221
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 123 | 2.8759 |
| No log | 2.0 | 246 | 2.0496 |
| No log | 3.0 | 369 | 2.0221 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Ayham/albert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 9 | null | ---
language:
- en
thumbnail: TBD
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
datasets:
- ChristophSchuhmann/improved_aesthetics_6plus
---
# Mini Stable Diffusion (miniSD)
MiniSD is a latent text-to-image diffusion model that has been conditionned on 256x256 images through finetuning.
## Examples
WIP
## Usage
```
!pip install diffusers==0.3.0
!pip install transformers scipy ftfy
```
```
import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
# TODO: change model_id to "lambdalabs/miniSD"
pipe = StableDiffusionPipeline.from_pretrained("eolecvk/model-test", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "Yoda"
scale = 10
n_samples = 4
# Sometimes the nsfw checker is confused, you can disable it at your own risk here
disable_safety = False
if disable_safety:
def null_safety(images, **kwargs):
return images, False
pipe.safety_checker = null_safety
with autocast("cuda"):
images = pipe(n_samples*[prompt], guidance_scale=scale).images
for idx, im in enumerate(images):
im.save(f"{idx:06}.png")
```
|
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
]
| text2text-generation | {
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"EncoderDecoderModel"
],
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}
} | 8 | null | ---
license: gpl-3.0
---
This project uses ML to recommend ingredients. See [this notebook](https://github.com/map222/Kibbeh/blob/master/ingredient_finder.ipynb) for a walkthrough, or [try using the recommender yourself here](https://huggingface.co/spaces/map222/recipe-spice). |
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