modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17 values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
"architectures": [
"AlbertForPreTraining"
],
"model_type": "albert",
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
} | 393 | 2023-05-09T15:07:45Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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-Huggy
2. Step 1: Find your model_id: Khayoon/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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],
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}
}
} | 27 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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},
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}
} | 28 | 2023-05-09T15:12:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-arty
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: 1.0
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-arty
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2386 | 0.43 | 50 | 0.0643 | 0.9967 |
| 0.0359 | 0.87 | 100 | 0.0035 | 0.9996 |
| 0.058 | 1.3 | 150 | 0.0015 | 0.9996 |
| 0.0297 | 1.74 | 200 | 0.0003 | 1.0 |
| 0.0175 | 2.17 | 250 | 0.0002 | 1.0 |
| 0.0166 | 2.6 | 300 | 0.0002 | 1.0 |
| 0.0318 | 3.04 | 350 | 0.0001 | 1.0 |
| 0.0062 | 3.47 | 400 | 0.0002 | 1.0 |
| 0.0101 | 3.9 | 450 | 0.0002 | 1.0 |
| 0.0066 | 4.34 | 500 | 0.0002 | 1.0 |
| 0.005 | 4.77 | 550 | 0.0002 | 1.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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}
} | 5 | null | ---
license: mit
---
SAILER is a structure-aware pre-trained language model. It is highlighted in the following three aspects:
- SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents.
- SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors.
- SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately.
SAILER_zh pre-training on Chinese criminal law legal case documents
If you find our work useful, please do not save your star and cite our work:
```
@misc{SAILER,
title={SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval},
author={Haitao Li and Qingyao Ai and Jia Chen and Qian Dong and Yueyue Wu and Yiqun Liu and Chong Chen and Qi Tian},
year={2023},
eprint={2304.11370},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
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dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos | [
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] | token-classification | {
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}
} | 5 | 2023-05-09T15:20:47Z | ---
license: mit
---
SAILER is a structure-aware pre-trained language model. It is highlighted in the following three aspects:
- SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents.
- SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors.
- SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately.
SAILER_en pre-training on English legal case documents
If you find our work useful, please do not save your star and cite our work:
```
@misc{SAILER,
title={SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval},
author={Haitao Li and Qingyao Ai and Jia Chen and Qian Dong and Yueyue Wu and Yiqun Liu and Chong Chen and Qi Tian},
year={2023},
eprint={2304.11370},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` | [
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dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
} | 5 | 2023-05-09T15:26:28Z | ---
license: mit
datasets:
- agomberto/FrenchCensus-handwritten-texts
language:
- fr
pipeline_tag: image-to-text
tags:
- pytorch
- transformers
- trocr
widget:
- src: >-
https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/4.png
example_title: Example 1
- src: >-
https://raw.githubusercontent.com/agombert/trocr-base-printed-fr/main/sample_imgs/5.jpg
example_title: Example 2
metrics:
- cer
- wer
---
# TrOCR base handwritten for French
## Overview
TrOCR handwritten has not yet released for French, so we trained a French model for PoC purpose. Based on this model, it is recommended to collect more data to additionally train the 1st stage or perform fine-tuning as the 2nd stage.
It's a special case of the [English large handwritten trOCR model](https://huggingface.co/microsoft/trocr-large-handwritten) introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/trocr) as a TrOCR model fine-tuned on the [IAM dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database).
We decided to fine-tuned on two datasets:
1. [French Census dataset](https://zenodo.org/record/6581158) from Constum et al. We created a [dataset on the hub](https://huggingface.co/datasets/agomberto/FrenchCensus-handwritten-texts) too.
2. A dataset soon to come on French archives
## Model description
The TrOCR model is an encoder-decoder model, consisting of an image Transformer as encoder, and a text Transformer as decoder. The image encoder was initialized from the weights of BEiT, while the text decoder was initialized from the weights of RoBERTa.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Next, the Transformer text decoder autoregressively generates tokens.
## Intended uses & limitations
You can use the raw model for optical character recognition (OCR) on single text-line images.
## Parameters
We used heuristic parameters without separate hyperparameter tuning.
- learning_rate = 4e-5
- epochs = 10
- fp16 = True
- max_length = 64
- split train/dev: 90/10
## Metrics
For the dev set we got those results
- size of the test set: 1550 examples
- CER: 0.07
- WER: 0.19
For the test set (from French Census only) we got those results
- size of the test set: 730 examples
- CER: 0.9
- WER: 0.21
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
from PIL import Image
import requests
url = "https://github.com/agombert/trocr-base-printed-fr/blob/main/sample_imgs/5.jpg"
response = requests.get(url)
img = Image.open(BytesIO(response.content))
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-handwritten')
model = VisionEncoderDecoderModel.from_pretrained('agomberto/trocr-large-handwritten-fr')
tokenizer = AutoTokenizer.from_pretrained('agomberto/trocr-large-handwritten-fr')
pixel_values = (processor(images=image, return_tensors="pt").pixel_values)
generated_ids = model.generate(pixel_values)
generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
``` | [
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CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1 | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
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}
} | 1 | 2023-05-09T15:30:17Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -37.32 +/- 13.17
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1000000
'learning_rate': 0.001
'num_envs': 32
'num_steps': 2048
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 128
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'vldnechai/LunarLander_v2_PPO'
'batch_size': 65536
'minibatch_size': 512}
```
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Chaddmckay/Cdm | [] | null | {
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: ppo-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AliCampbellKhaya/ppo-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"])
```
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} | 1 | null | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: es_fi_all_quy
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. -->
# es_fi_all_quy
This model is a fine-tuned version of [nouman-10/es_fi_all_quy](https://huggingface.co/nouman-10/es_fi_all_quy) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4858
- Bleu: 1.9345
- Chrf: 34.1553
- Gen Len: 38.1579
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:-------:|
| 0.2239 | 0.09 | 1000 | 0.4842 | 1.613 | 34.0838 | 37.4266 |
| 0.2256 | 0.17 | 2000 | 0.4808 | 1.5738 | 32.9285 | 43.005 |
| 0.2178 | 0.26 | 3000 | 0.4820 | 1.499 | 32.8545 | 41.5795 |
| 0.2175 | 0.34 | 4000 | 0.4828 | 1.6725 | 33.3302 | 39.0342 |
| 0.223 | 0.43 | 5000 | 0.4864 | 2.343 | 34.8172 | 35.6197 |
| 0.2286 | 0.51 | 6000 | 0.4743 | 1.7218 | 33.6246 | 42.0755 |
| 0.2281 | 0.6 | 7000 | 0.4794 | 1.7699 | 34.7209 | 38.7716 |
| 0.2243 | 0.68 | 8000 | 0.4802 | 1.7605 | 34.16 | 37.6258 |
| 0.236 | 0.77 | 9000 | 0.4772 | 1.5908 | 33.9113 | 39.0543 |
| 0.2309 | 0.85 | 10000 | 0.4756 | 2.0459 | 34.2757 | 38.2404 |
| 0.2303 | 0.94 | 11000 | 0.4762 | 1.8315 | 34.3413 | 37.7465 |
| 0.2218 | 1.02 | 12000 | 0.4860 | 1.8359 | 33.5176 | 38.5392 |
| 0.217 | 1.11 | 13000 | 0.4811 | 1.9919 | 34.0441 | 37.3702 |
| 0.2158 | 1.19 | 14000 | 0.4845 | 1.6391 | 34.5521 | 37.0493 |
| 0.2211 | 1.28 | 15000 | 0.4858 | 1.9345 | 34.1553 | 38.1579 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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ChaitanyaU/FineTuneLM | [] | null | {
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} | 0 | 2023-05-09T15:45:59Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-5
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. -->
# xinyixiuxiu/albert-xxlarge-v2-SST2-incremental_pre_training-epoch1-5
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0332
- Train Accuracy: 0.9897
- Validation Loss: 0.1438
- Validation Accuracy: 0.9599
- Epoch: 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:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0332 | 0.9897 | 0.1438 | 0.9599 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.7.0
- Datasets 2.10.1
- Tokenizers 0.12.1
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Chakita/KROBERT | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"translation_en_to_de": {
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-scientific-exp
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-scientific-exp
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1314
- Precision: 0.6519
- Recall: 0.7167
- F1: 0.6828
- Accuracy: 0.9631
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 81 | 0.3013 | 0.3445 | 0.2600 | 0.2964 | 0.9177 |
| No log | 2.0 | 162 | 0.2062 | 0.5736 | 0.4038 | 0.4739 | 0.9378 |
| No log | 3.0 | 243 | 0.1553 | 0.6575 | 0.6089 | 0.6323 | 0.9571 |
| No log | 4.0 | 324 | 0.1360 | 0.6427 | 0.6617 | 0.6521 | 0.9627 |
| No log | 5.0 | 405 | 0.1314 | 0.6519 | 0.7167 | 0.6828 | 0.9631 |
| No log | 6.0 | 486 | 0.1341 | 0.6485 | 0.7294 | 0.6866 | 0.9625 |
| 0.2011 | 7.0 | 567 | 0.1357 | 0.6564 | 0.7230 | 0.6881 | 0.9639 |
| 0.2011 | 8.0 | 648 | 0.1409 | 0.6559 | 0.7294 | 0.6907 | 0.9644 |
| 0.2011 | 9.0 | 729 | 0.1447 | 0.6591 | 0.7400 | 0.6972 | 0.9638 |
| 0.2011 | 10.0 | 810 | 0.1447 | 0.6597 | 0.7378 | 0.6966 | 0.9642 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Chakita/Kalbert | [
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"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": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | 2023-05-09T15:51:50Z | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/
<br />
```
ct2-fairseq-converter --model_path base.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization float16 --output_dir ../converted/jparacrawl-v3-base-ct2-float16/en-ja
```
```
ct2-fairseq-converter --model_path ./base/base.pretrain.pt --data_dir ./base --source_lang ja --target_lang en --quantization float16 --output_dir ../converted/jparacrawl-v3-base-ct2-float16/ja-en
```
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ChaseBread/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
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},
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},
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},
"translation_en_to_ro": {
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}
}
} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-scientific-exp
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-scientific-exp
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1183
- Precision: 0.7109
- Recall: 0.7696
- F1: 0.7391
- Accuracy: 0.9674
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 81 | 0.2439 | 0.53 | 0.4482 | 0.4857 | 0.9352 |
| No log | 2.0 | 162 | 0.1769 | 0.7434 | 0.5391 | 0.6250 | 0.9492 |
| No log | 3.0 | 243 | 0.1405 | 0.6967 | 0.6702 | 0.6832 | 0.9623 |
| No log | 4.0 | 324 | 0.1183 | 0.7109 | 0.7696 | 0.7391 | 0.9674 |
| No log | 5.0 | 405 | 0.1292 | 0.7126 | 0.7759 | 0.7429 | 0.9670 |
| No log | 6.0 | 486 | 0.1371 | 0.7159 | 0.7886 | 0.7505 | 0.9662 |
| 0.1665 | 7.0 | 567 | 0.1452 | 0.7137 | 0.7801 | 0.7455 | 0.9671 |
| 0.1665 | 8.0 | 648 | 0.1596 | 0.7071 | 0.8013 | 0.7512 | 0.9655 |
| 0.1665 | 9.0 | 729 | 0.1578 | 0.7084 | 0.8013 | 0.7520 | 0.9657 |
| 0.1665 | 10.0 | 810 | 0.1565 | 0.7173 | 0.7992 | 0.756 | 0.9666 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Cheapestmedsshop/Buymodafinilus | [] | null | {
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}
} | 0 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/
<br />
```
ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization int8 --output_dir ../converted/jparacrawl-v3-big-ct2-int8/en-ja
```
```
ct2-fairseq-converter --model_path ./big/big.pretrain.pt --data_dir ./big --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/jparacrawl-v3-big-ct2-int8/ja-en
```
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Cheatham/xlm-roberta-large-finetuned-d1r01 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_ro": {
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}
}
} | 21 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/
<br />
```
ct2-fairseq-converter --model_path small.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization float16 --output_dir ../converted/jparacrawl-v3-small-ct2-float16/en-ja
```
```
ct2-fairseq-converter --model_path ./small/small.pretrain.pt --data_dir ./small --source_lang ja --target_lang en --quantization float16 --output_dir ../converted/jparacrawl-v3-small-ct2-float16/ja-en
``` | [
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Cheatham/xlm-roberta-large-finetuned | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 20 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/
<br />
```
ct2-fairseq-converter --model_path small.pretrain.pt --data_dir . --source_lang en --target_lang ja --quantization int8 --output_dir ../converted/jparacrawl-v3-small-ct2-int8/en-ja
```
```
ct2-fairseq-converter --model_path ./small/small.pretrain.pt --data_dir ./small --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/jparacrawl-v3-small-ct2-int8/ja-en
``` | [
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] |
Cheatham/xlm-roberta-large-finetuned3 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"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,
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},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 22 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://sugoitranslator.com
<br />
https://blog.sugoitranslator.com
<br />
https://www.patreon.com/mingshiba
<br />
```
ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang ja --target_lang en --quantization float16 --output_dir ../converted/sugoi-v3.3-ja-en-ct2-float16
``` | [
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] |
Cheatham/xlm-roberta-large-finetuned4 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"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": {
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"max_length": null,
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},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 20 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://sugoitranslator.com
<br />
https://blog.sugoitranslator.com
<br />
https://www.patreon.com/mingshiba
<br />
```
ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/sugoi-v3.3-ja-en-ct2-int8
``` | [
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Check/vaw2tmp | [
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 276.94 +/- 17.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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CheonggyeMountain-Sherpa/kogpt-trinity-poem | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 15 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://sugoitranslator.com
<br />
https://blog.sugoitranslator.com
<br />
https://www.patreon.com/mingshiba
<br />
```
ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang ja --target_lang en --quantization float16 --output_dir ../converted/sugoi-v4-ja-en-ct2-float16
``` | [
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} | 0 | null | ---
license: creativeml-openrail-m
thumbnail: >-
./images/thumbnail.webp
tags:
- stable-diffusion
- text-to-image
- safetensors
- diffusers
inference: true
language:
- en
widget:
- text: >-
1girl, solo, bangs, (pink hair, gradient hair, very long hair:1.1), (purple eyes:1.05), (cat ears, animal ear fluff:1.2), sidelocks, white shirt, collared shirt, buttons,
night, looking at viewer, table, pizza on table, pov, restaurant, medium breasts, smiling, (blush:0.7), colored inner hair, (symmetric), (masterpiece, exceptional, extremely detailed:1.1)
example_title: example
library_name: diffusers
---
<center>
<img src="Andromeda/resolve/main/images/AndromedaCover.png" width="80%"/>
<h1 style="font-size:1.6rem;">
<b>AndromedaDX (and friends)</b>
</h1>
<h2>Stable Diffusion models for (neko enthusiast) weebs</h2>
</center>
<div style="display: flex; flex-direction: row; width: 100%">
<div style="margin: 0; padding: 0; width:25%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/AndromedaDX-v1-CS2-01367-3105139270.png" />
<figcaption style="text-align: center; font-size: 1.2rem;"><a href="Andromeda/resolve/main/AndromedaDX-v1.safetensors">Andromeda<b>DX</b></a></figcaption>
</figure>
</div>
<div style="margin: 0; padding: 0; width:25%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/AndromedaSX-v1-CS2-01365-3105139270.png" />
<figcaption style="text-align: center; font-size: 1.2rem;"><a href="Andromeda/resolve/main/AndromedaSX-v1.safetensors">Andromeda<b>SX</b></a></figcaption>
</figure>
</div>
<div style="margin: 0; padding: 0; width:25%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/AndromedaSV-v1-CS2-01366-3105139270.png" />
<figcaption style="text-align: center; font-size: 1.2rem;"><a href="Andromeda/resolve/main/AndromedaSV-v1.safetensors">Andromeda<b>SV</b></a></figcaption>
</figure>
</div>
<div style="margin: 0; padding: 0; width:25%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/AndromedaUnreal-v1-CS2-01368-3105139270.png" />
<figcaption style="text-align: center; font-size: 1.2rem;"><a href="Andromeda/resolve/main/AndromedaUnreal-v1.safetensors">Andromeda<b>Unreal</b></a></figcaption>
</figure>
</div>
</div>
## Model Variants
<p>I'm not sure there's actually enough difference between these four to merit posting all of them, but nevertheless, here they are.</p>
<h3><a href="Andromeda/resolve/main/AndromedaDX-v1.safetensors">Andromeda<b>DX</b></a></h3>
<p>
This is probably the one you want. Out of all of these it's the one I'm happiest with; it produces high-detail backgrounds (and IMO can achieve a fairly high overall level of detail). Bonus special feature (_definitely_ not a bug, shhh): it likes to add cats to things. Just regular cats. Sometimes several cats!
</p>
<h3><a href="Andromeda/resolve/main/AndromedaSX-v1.safetensors">Andromeda<b>SX</b></a> and <a href="Andromeda/resolve/main/AndromedaSV-v1.safetensors">Andromeda<b>SV</b></a></h3>
<p>
These are more suited to foxgirls than catgirls. If you're a Fubuki or Kawakaze enthusiast, these are for you :)
</p>
<h3><a href="Andromeda/resolve/main/AndromedaUnreal-v1.safetensors">Andromeda<b>Unreal</b></a></h3>
<p>
This one has a (slightly) more realistic style thanks to the addition of some MeinaUnreal (not currently posted on HF).
</p>
## Notes/info
I made DX for a chatbot of mine who can "take selfies" via SD, and it behaves nicely enough that I felt like it was worth sharing.
It's especially good with natural language prompts at high CLIP skip, e.g.:
<div style="display: flex; flex-direction: row; width: 100%">
<div style="margin: 0; padding: 0; width: 50%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/DX/AndromedaDX-v1-CS5-01285-3762015382.png"/>
<figcaption style="text-align: center; font-size: 1.2rem;">CLIP Skip 5</b></figcaption>
</figure>
</div>
<div style="margin: 0; padding: 0; width: 50%;">
<figure style="margin: 2px;">
<img src="Andromeda/resolve/main/images/DX/AndromedaDX-v1-CS7-00812-3866181234.png"/>
<figcaption style="text-align: center; font-size: 1.2rem;">CLIP Skip 7</b></figcaption>
</figure>
</div>
</div>
<details>
<summary><big><b>Prompts</b></big></summary>
OK, I lied, it's just the one prompt for now.
```yaml
1girl, solo, bangs, red hair, (pink hair:1.1), long hair, very long hair, (purple eyes:1.1), (cat ears, animal ear fluff:1.1), , evening, gradient eyes, colored inner hair, medium breasts, (smile:0.6), (masterpiece, best quality, exceptional, extremely detailed:1.1)
Negative prompt: (nsfw:1.05), (EasyNegative:0.7), simple5.5, simple5.6, bad-hands-5, (short hair:1.1), loli, child
Steps: 25, Sampler: Euler a, CFG scale: 8.25, Seed: 3105139270, Size: 864x608, Model hash: 9e0b7d3259, Model: AndromedaSX-v1, Denoising strength: 0.5, Clip skip: 2, ENSD: 31337, Hires upscale: 1.5, Hires steps: 8, Hires upscaler: Latent, Discard penultimate sigma: True
```
</details>
## Recommended settings
Some educated guesses based on what playing around I've done:
- VAE: [kl-f8-anime2](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt)) is baked in and recommended, blessed2 works as well but is a little softer on the colours.
- Clip Skip: 2, 5, or 7 - DX and Unreal do quite well in the 5-7 range
- Sampler: DPM++ 2M Karras, the usual 20-35 steps, or Euler A at 40-50 steps with CLIP skip 5-7
- CFG Scale: 7.5 ± 1 (much below 6 is a bad move, much above 9 will get... fun)
- Recommended Positive Prompt:
Add `(masterpiece, best quality, exceptional, extremely detailed:1.1)` at the **end** of your prompt. Otherwise, nothing particularly special. YMMV, of course.
- Recommended Negative Prompt:
[EasyNegative](https://huggingface.co/datasets/gsdf/EasyNegative), `(nsfw:1.05), (EasyNegative:0.7), simple5.5, simple5.6, bad-hands-5, (short hair:1.1), loli, child`
- For better results, using hires fix is a must.
- Hires upscaler: Latent (any variant, such as nearest-exact), 12-18 steps at 0.5-0.6 denoising (7-10 actual steps)
## Recipe
To be filled in later... I honestly don't remember half of what I did here.
### Credits
A big thankyou to [ShussarSDFA](https://huggingface.co/ShussarSDFA) for their [Sathariel](https://huggingface.co/ShussarSDFA/Sathariel) model series, which are a key component of this
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Chester/traffic-rec | [] | null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sa_french_tr_test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sa_french_tr_test
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7813
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 94 | 0.4698 | 0.824 |
| No log | 2.0 | 188 | 0.4229 | 0.836 |
| No log | 3.0 | 282 | 0.4974 | 0.838 |
| No log | 4.0 | 376 | 0.7081 | 0.828 |
| No log | 5.0 | 470 | 0.7813 | 0.82 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Chinat/test-classifier | [] | null | {
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}
} | 0 | null | ---
license: unknown
language:
- ja
- en
pipeline_tag: translation
---
https://sugoitranslator.com
<br />
https://blog.sugoitranslator.com
<br />
https://www.patreon.com/mingshiba
<br />
```
ct2-fairseq-converter --model_path big.pretrain.pt --data_dir . --source_lang ja --target_lang en --quantization int8 --output_dir ../converted/sugoi-v4-ja-en-ct2-int8
``` | [
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ChoboAvenger/DialoGPT-small-joshua | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ICBHI
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ICBHI
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1469
- Accuracy: 0.5290
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1702 | 1.0 | 345 | 1.1469 | 0.5290 |
| 1.1268 | 2.0 | 690 | 1.1437 | 0.5290 |
| 1.1948 | 3.0 | 1035 | 1.1439 | 0.5290 |
| 1.1875 | 4.0 | 1380 | 1.1455 | 0.5290 |
| 1.0677 | 5.0 | 1725 | 1.1416 | 0.5290 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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ChrisVCB/DialoGPT-medium-ej | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 13 | null | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- hr-elrond/autotrain-data-p2_finbert_training_100
co2_eq_emissions:
emissions: 0.2967273355715001
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 56875131853
- CO2 Emissions (in grams): 0.2967
## Validation Metrics
- Loss: 0.068
- Accuracy: 0.984
- Precision: 0.993
- Recall: 0.983
- AUC: 0.996
- F1: 0.988
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/hr-elrond/autotrain-p2_finbert_training_100-56875131853
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("hr-elrond/autotrain-p2_finbert_training_100-56875131853", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("hr-elrond/autotrain-p2_finbert_training_100-56875131853", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | [
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Chuah/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"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,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ## NOTE: This "delta model" cannot be used directly.
Users have to apply it on top of the original LLaMA weights to get actual flan-llama weights. (sample refer below)
## How to Use:
```python
device = 0 # Define your GPU device here
llama_path = '' # Define your original llama-7b load path here (huggingface checkpoint)
import transformers
from collections import OrderedDict
model_llama = transformers.AutoModelForCausalLM.from_pretrained(llama_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(llama_path)
model_flan_llama = transformers.AutoModelForCausalLM.from_pretrained("Ahrefs/flan-llama-7b-ckpt-diff")
model_state_dict = []
for key in model_flan_llama.state_dict().keys():
model_state_dict.append((key, model_flan_llama.state_dict()[key]+model_llama.state_dict()[key]))
model_state_dict = OrderedDict(model_state_dict)
model_flan_llama.load_state_dict(model_state_dict)
model_flan_llama = model_flan_llama.to(device)
model_flan_llama.eval()
def generate(prompt, model, device):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_output = model.generate(input_ids.to(device), max_new_tokens=512, early_stopping=True)[0]
answer_cot = tokenizer.decode(gen_output, skip_special_tokens=True)
return answer_cot
prompt = "Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
print(generate(prompt, model_flan_llama, device))
```
output:
```
Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering. Geoffrey Hinton is a living person. George Washington was not alive when Geoffrey Hinton was born. The final answer: no.
```
## Dataset and Training:
We finetune the original llama-7b model on extracted and sampled [Flan-2022](https://github.com/google-research/FLAN) dataset. The data are filtered to be limited to maximum source sequence length of 1536, and maximum target sequence length of 512, which accounts for roughly 5.5mil samples. (The sampled and extracted unfiltered dataset to be published on huggingface datasets soon)
We finetune the original llama-7b model on 8 A100 GPUs using pytorch's FSDP, with a learning rate of 2e-5, with warm up ratio of 0.03 and cosine rate decay, and batch size of 128.
## Reference:
```
@inproceedings{weifinetuned,
title={Finetuned Language Models are Zero-Shot Learners},
author={Wei, Jason and Bosma, Maarten and Zhao, Vincent and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M and Le, Quoc V},
booktitle={International Conference on Learning Representations}
}
```
```
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
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Chun/DialoGPT-large-dailydialog | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"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,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 6 | null | ---
tags:
- conversational
---
# yesun DialoGPT Model | [
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Chun/w-en2zh-otm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"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": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 7 | 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: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zeyefkey/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"])
```
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] |
Chun/w-zh2en-mtm | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
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},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-finetuned-recruitment-exp
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-finetuned-recruitment-exp
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1044
- Precision: 0.7320
- Recall: 0.8560
- F1: 0.7892
- Accuracy: 0.9713
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 17 | 0.4051 | 0.25 | 0.0061 | 0.0119 | 0.8984 |
| No log | 2.0 | 34 | 0.2450 | 0.4040 | 0.3732 | 0.3880 | 0.9280 |
| No log | 3.0 | 51 | 0.1481 | 0.5385 | 0.6663 | 0.5956 | 0.9555 |
| No log | 4.0 | 68 | 0.1269 | 0.6295 | 0.7961 | 0.7031 | 0.964 |
| No log | 5.0 | 85 | 0.1101 | 0.6639 | 0.8235 | 0.7352 | 0.9679 |
| No log | 6.0 | 102 | 0.1116 | 0.7287 | 0.7819 | 0.7544 | 0.9701 |
| No log | 7.0 | 119 | 0.1160 | 0.7026 | 0.8266 | 0.7596 | 0.9684 |
| No log | 8.0 | 136 | 0.1071 | 0.7442 | 0.8499 | 0.7936 | 0.9717 |
| No log | 9.0 | 153 | 0.1044 | 0.7320 | 0.8560 | 0.7892 | 0.9713 |
| No log | 10.0 | 170 | 0.1081 | 0.7532 | 0.8448 | 0.7964 | 0.9722 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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} | 0 | 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 140000 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 140000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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
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} | 0 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3.1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zeyefkey/q-Taxi-v3.1", 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"])
```
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} | 0 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
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Cinnamon/electra-small-japanese-discriminator | [
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null | {
"architectures": [
"ElectraForPreTraining"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 419 | null | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- VictorGil75/autotrain-data-rm-soccer_class
co2_eq_emissions:
emissions: 0.4133097011272339
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 56881131860
- CO2 Emissions (in grams): 0.4133
## Validation Metrics
- Loss: 0.064
- Accuracy: 0.985
- Precision: 0.990
- Recall: 0.980
- AUC: 0.995
- F1: 0.985
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/VictorGil75/autotrain-rm-soccer_class-56881131860
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | [
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} | 0 | 2023-05-09T16:46:01Z | ---
datasets:
- PaulineSanchez/Trad_food
language:
- en
- fr
metrics:
- bleu
tags:
- food
---
# Model_trad_food_en_fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the PaulineSanchez/Trad_food dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4698
- Bleu: 74.7824
- Gen Len: 10.7911
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3 | [
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] |
ClaudeCOULOMBE/RickBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"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,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 258.98 +/- 28.22
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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ClaudeYang/awesome_fb_model | [
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | {
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
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},
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},
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},
"translation_en_to_de": {
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},
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}
}
} | 26 | 2023-05-09T16:48:51Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3.2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="zeyefkey/q-Taxi-v3.2", 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"])
```
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CleveGreen/FieldClassifier_v2_gpt | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
] | text-classification | {
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"GPT2ForSequenceClassification"
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}
} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-recruitment-exp
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-recruitment-exp
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1742
- Precision: 0.6204
- Recall: 0.6855
- F1: 0.6513
- Accuracy: 0.9561
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 17 | 0.5203 | 0.0 | 0.0 | 0.0 | 0.8991 |
| No log | 2.0 | 34 | 0.3797 | 0.2979 | 0.0277 | 0.0507 | 0.9030 |
| No log | 3.0 | 51 | 0.2983 | 0.3171 | 0.4194 | 0.3612 | 0.9222 |
| No log | 4.0 | 68 | 0.2321 | 0.4219 | 0.4916 | 0.4541 | 0.9375 |
| No log | 5.0 | 85 | 0.2100 | 0.5076 | 0.5262 | 0.5168 | 0.9453 |
| No log | 6.0 | 102 | 0.1899 | 0.5174 | 0.5885 | 0.5507 | 0.9506 |
| No log | 7.0 | 119 | 0.1775 | 0.5395 | 0.6350 | 0.5834 | 0.9509 |
| No log | 8.0 | 136 | 0.1817 | 0.6282 | 0.6617 | 0.6445 | 0.9550 |
| No log | 9.0 | 153 | 0.1775 | 0.6262 | 0.6726 | 0.6485 | 0.9558 |
| No log | 10.0 | 170 | 0.1742 | 0.6204 | 0.6855 | 0.6513 | 0.9561 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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CleveGreen/JobClassifier_v2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 37 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imrazaa/bert-base-uncased-finetuned-CQA
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. -->
# imrazaa/bert-base-uncased-finetuned-CQA
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2127
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 354, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.8695 | 0 |
| 3.7728 | 1 |
| 3.2127 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-recruitment-exp
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-recruitment-exp
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1433
- Precision: 0.6113
- Recall: 0.7250
- F1: 0.6633
- Accuracy: 0.9612
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 17 | 0.5254 | 0.0 | 0.0 | 0.0 | 0.8991 |
| No log | 2.0 | 34 | 0.3199 | 0.2854 | 0.2255 | 0.2519 | 0.9152 |
| No log | 3.0 | 51 | 0.2303 | 0.3948 | 0.5232 | 0.4500 | 0.9370 |
| No log | 4.0 | 68 | 0.1878 | 0.4876 | 0.6212 | 0.5463 | 0.9496 |
| No log | 5.0 | 85 | 0.1630 | 0.5544 | 0.6548 | 0.6005 | 0.9553 |
| No log | 6.0 | 102 | 0.1433 | 0.6113 | 0.7250 | 0.6633 | 0.9612 |
| No log | 7.0 | 119 | 0.1469 | 0.6412 | 0.7458 | 0.6895 | 0.9621 |
| No log | 8.0 | 136 | 0.1463 | 0.6516 | 0.7418 | 0.6938 | 0.9633 |
| No log | 9.0 | 153 | 0.1446 | 0.6664 | 0.7527 | 0.7069 | 0.9643 |
| No log | 10.0 | 170 | 0.1447 | 0.6579 | 0.7646 | 0.7072 | 0.9641 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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CodeDanCode/SP-KyleBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
} | 15 | null | ## NOTE: This "delta model" cannot be used directly.
Users have to apply it on top of the original LLaMA weights to get actual flan-llama weights. (sample refer below)
## How to Use:
```python
device = 0 # Define your GPU device here
llama_path = '' # Define your original llama-7b load path here (huggingface checkpoint)
import transformers
from collections import OrderedDict
model_llama = transformers.AutoModelForCausalLM.from_pretrained(llama_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(llama_path)
model_flan_llama = transformers.AutoModelForCausalLM.from_pretrained("yep-search/flan-llama-7b-ckpt-diff")
model_state_dict = []
for key in model_flan_llama.state_dict().keys():
model_state_dict.append((key, model_flan_llama.state_dict()[key]+model_llama.state_dict()[key]))
model_state_dict = OrderedDict(model_state_dict)
model_flan_llama.load_state_dict(model_state_dict)
model_flan_llama = model_flan_llama.to(device)
model_flan_llama.eval()
def generate(prompt, model, device):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_output = model.generate(input_ids.to(device), max_new_tokens=512, early_stopping=True)[0]
answer_cot = tokenizer.decode(gen_output, skip_special_tokens=True)
return answer_cot
prompt = "Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
print(generate(prompt, model_flan_llama, device))
```
output:
```
Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering. Geoffrey Hinton is a living person. George Washington was not alive when Geoffrey Hinton was born. The final answer: no.
```
## Dataset and Training:
We finetune the original llama-7b model on extracted and sampled [Flan-2022](https://github.com/google-research/FLAN) dataset. The data are filtered to be limited to maximum source sequence length of 1536, and maximum target sequence length of 512, which accounts for roughly 5.5mil samples. (The sampled and extracted unfiltered dataset to be published on huggingface datasets soon)
We finetune the original llama-7b model on 8 A100 GPUs using pytorch's FSDP, with a learning rate of 2e-5, with warm up ratio of 0.03 and cosine rate decay, and batch size of 128.
## Reference:
```
@inproceedings{weifinetuned,
title={Finetuned Language Models are Zero-Shot Learners},
author={Wei, Jason and Bosma, Maarten and Zhao, Vincent and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M and Le, Quoc V},
booktitle={International Conference on Learning Representations}
}
```
```
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
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CodeMonkey98/distilroberta-base-finetuned-wikitext2 | [] | null | {
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}
} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
An AI Avatar generator using stable diffusion fine-tuned with Dreambooth. | [
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} | 3 | 2023-05-09T17:08:26Z | ---
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: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9347574101672462
- name: Recall
type: recall
value: 0.9500168293503871
- name: F1
type: f1
value: 0.9423253484684083
- name: Accuracy
type: accuracy
value: 0.9863719314770119
---
<!-- 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: 0.0596
- Precision: 0.9348
- Recall: 0.9500
- F1: 0.9423
- Accuracy: 0.9864
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0901 | 1.0 | 1756 | 0.0692 | 0.9075 | 0.9327 | 0.9199 | 0.9810 |
| 0.0352 | 2.0 | 3512 | 0.0608 | 0.9299 | 0.9490 | 0.9394 | 0.9860 |
| 0.0189 | 3.0 | 5268 | 0.0596 | 0.9348 | 0.9500 | 0.9423 | 0.9864 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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} | 3 | null | ---
license: openrail
---
Model Card for Sentiment Analysis Model
Model Details
Model Name: Sentiment Analysis Model(News)
Model Version: 1.01.1
Model Type: Natural Language Processing (NLP)
Model Architecture: Bidirectional LSTM with Embedding layer
Model Input: Preprocessed text data
Model Output: Predicted sentiment category (positive, negative, or neutral)
Model Performance
Accuracy: 0.95
Loss: 0.35
Dataset: News articles dataset
Dataset Size: 8.5m samples
Performance Metrics: Categorical cross-entropy loss and accuracy
Model Intended Use
The model is intended to be used for sentiment analysis of news article headlines.
The model is not intended to be used for sentiment analysis of other types of text data, such as social media posts or product reviews.
The model is intended for use in research and development projects related to sentiment analysis.
Ethical Considerations
The model was trained on a dataset of news articles, which may contain biased or subjective language.
The model may produce biased results for certain types of news articles or for certain demographics of readers.
The model does not take into account the context or background of the news article, which may impact the accuracy of the sentiment analysis.
The model should be used in conjunction with human review and interpretation to ensure that the sentiment analysis is accurate and appropriate.
Model Limitations
The model is limited by the quality and representativeness of the dataset on which it was trained.
The model may not perform well on news articles that contain highly complex or nuanced language.
The model does not take into account the tone or emotion of the news article, which may impact the sentiment analysis.
Model Training Details
Training Algorithm: Adam optimizer
Number of Epochs: 10
Training Time: 32 hours
Training Hardware: NVIDIA Tesla P100 GPU
Data Preprocessing: Tokenization, stopword removal, lemmatization, and padding
Data Augmentation: None
Validation Split: 20% of the dataset
Hyperparameters: Vocabulary size of 200,000, embedding dimension of 300, and sequence length of 100
License and Intellectual Property
The model and associated code are licensed under the Apache License 2.0.
The model was developed by [Pavan krishna Narne].
The model is open source and free to use for non-commercial purposes.
Commercial use of the model requires permission from [Pavan krishna Narne].
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CoderEFE/DialoGPT-marxbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"has_space"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imrazaa/bert-base-uncased-finetuned-CQA-100-sampled
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. -->
# imrazaa/bert-base-uncased-finetuned-CQA-100-sampled
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5418
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 69, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.7857 | 0 |
| 5.0015 | 1 |
| 4.5418 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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CoderEFE/DialoGPT-medium-marx | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 7 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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CoffeeAddict93/gpt1-call-of-the-wild | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 8 | 2023-05-09T17:16:26Z | ---
tags:
- autotrain
- stable-diffusion
- text-to-image
datasets:
- hell-hall/autotrain-data-jc0q-lzj1-y7fb
co2_eq_emissions:
emissions: 0
---
# Model Trained Using AutoTrain
- Problem type: Dreambooth
- Model ID: 56907131933
- CO2 Emissions (in grams): 0.0000 | [
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CoffeeAddict93/gpt2-medium-modest-proposal | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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},
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},
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}
}
} | 7 | null | ---
license:
- cc-by-nc-sa-4.0
- apache-2.0
tags:
- grammar
- spelling
- punctuation
- error-correction
- grammar synthesis
datasets:
- jfleg
widget:
- text: There car broke down so their hitching a ride to they're class.
example_title: compound-1
- text: i can has cheezburger
example_title: cheezburger
- text: >-
so em if we have an now so with fito ringina know how to estimate the tren
given the ereafte mylite trend we can also em an estimate is nod s i again
tort watfettering an we have estimated the trend an called wot to be called
sthat of exty right now we can and look at wy this should not hare a trend i
becan we just remove the trend an and we can we now estimate tesees ona
effect of them exty
example_title: Transcribed Audio Example 2
- text: >-
My coworker said he used a financial planner to help choose his stocks so he
wouldn't loose money.
example_title: incorrect word choice (context)
- text: >-
good so hve on an tadley i'm not able to make it to the exla session on
monday this week e which is why i am e recording pre recording an this
excelleision and so to day i want e to talk about two things and first of
all em i wont em wene give a summary er about ta ohow to remove trents in
these nalitives from time series
example_title: lowercased audio transcription output
- text: Frustrated, the chairs took me forever to set up.
example_title: dangling modifier
- text: I would like a peice of pie.
example_title: miss-spelling
- text: >-
Which part of Zurich was you going to go hiking in when we were there for
the first time together? ! ?
example_title: chatbot on Zurich
- text: >-
Most of the course is about semantic or content of language but there are
also interesting topics to be learned from the servicefeatures except
statistics in characters in documents. At this point, Elvthos introduces
himself as his native English speaker and goes on to say that if you
continue to work on social scnce,
example_title: social science ASR summary output
- text: >-
they are somewhat nearby right yes please i'm not sure how the innish is
tepen thut mayyouselect one that istatte lo variants in their property e ere
interested and anyone basical e may be applyind reaching the browing
approach were
- medical course audio transcription
inference: False
pipeline_tag: text2text-generation
language:
- en
---
# bart-base-grammar-synthesis
<a href="https://colab.research.google.com/gist/pszemraj/3e44e384dcd4614e1350d457bf9be8ad/bart-batch-grammar-check-correct-demo.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an expanded version of the JFLEG dataset.
You can find other grammar-synthesis models by [searching for the grammar synthesis tag](https://huggingface.co/models?other=grammar%20synthesis)
## Intended uses & limitations
- robust grammar correction
- the model has a license of `cc-by-nc-sa-4.0` as it uses the JFLEG dataset + augments it for training
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
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CohleM/bert-nepali-tokenizer | [] | null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: wine-reviews-roberta
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. -->
# wine-reviews-roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2715
- Train Acc: 0.8906
- Validation Loss: 0.6536
- Validation Acc: 0.7701
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24455, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Acc | Validation Loss | Validation Acc | Epoch |
|:----------:|:---------:|:---------------:|:--------------:|:-----:|
| 0.6164 | 0.7297 | 0.5360 | 0.7665 | 0 |
| 0.5040 | 0.7820 | 0.5145 | 0.7739 | 1 |
| 0.4248 | 0.8206 | 0.5470 | 0.7744 | 2 |
| 0.3413 | 0.8583 | 0.6132 | 0.7699 | 3 |
| 0.2715 | 0.8906 | 0.6536 | 0.7701 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent
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. -->
# imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1440
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 72, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.5215 | 0 |
| 4.0094 | 1 |
| 3.1440 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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Connor-tech/bert_cn_finetuning | [
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} | 27 | null | Access to model nidhig/vicuna_13B is restricted and you are not in the authorized list. Visit https://huggingface.co/nidhig/vicuna_13B to ask for access. | [
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Connorvr/BrightBot-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"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,
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},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-news
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-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7548 | 1.0 | 1531 | 2.6146 |
| 2.6217 | 2.0 | 3062 | 2.5512 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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}
} | 16 | 2023-05-09T17:51:12Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent-shuffled
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. -->
# imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent-shuffled
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8609
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 69, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 5.7805 | 0 |
| 4.4910 | 1 |
| 3.8609 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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Contrastive-Tension/BERT-Distil-CT | [
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"tf",
"distilbert",
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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"DistilBertForMaskedLM"
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}
} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: flan-t5-qg-LQ-tarek-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-qg-LQ-tarek-test
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5322
- Rouge1: 23.9922
- Rouge2: 6.7099
- Rougel: 21.5985
- Rougelsum: 21.5946
- Gen Len: 16.1304
- Meteor: {'meteor': 0.17861183292998817}
- Bleu: {'bleu': 0.032560412750946656, 'precisions': [0.31838712287431764, 0.07483151167147811, 0.02936351414577522, 0.011773119505596449], 'brevity_penalty': 0.6077920327728761, 'length_ratio': 0.6675912775198823, 'translation_length': 210109, 'reference_length': 314727}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Meteor | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|:-------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 1.565 | 1.0 | 23583 | 1.5526 | 23.7548 | 6.5396 | 21.3855 | 21.3784 | 16.1645 | {'meteor': 0.17704033979826234} | {'bleu': 0.03244683754192353, 'precisions': [0.31640932313437786, 0.07308847939823084, 0.029126556774398793, 0.011756263748055154], 'brevity_penalty': 0.6116573626703378, 'length_ratio': 0.6704286572172073, 'translation_length': 211002, 'reference_length': 314727} |
| 1.4823 | 2.0 | 47166 | 1.5322 | 23.9922 | 6.7099 | 21.5985 | 21.5946 | 16.1304 | {'meteor': 0.17861183292998817} | {'bleu': 0.032560412750946656, 'precisions': [0.31838712287431764, 0.07483151167147811, 0.02936351414577522, 0.011773119505596449], 'brevity_penalty': 0.6077920327728761, 'length_ratio': 0.6675912775198823, 'translation_length': 210109, 'reference_length': 314727} |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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} | 5 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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-SoccerTwos
2. Step 1: Write your model_id: Marc-Elie/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.05 +/- 17.30
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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CouchCat/ma_mlc_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"multi-label",
"license:mit"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
} | 29 | 2023-05-09T18:08:41Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent-truncated
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. -->
# imrazaa/bert-base-uncased-finetuned-CQA-100-1stSent-truncated
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9253
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 66, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.9248 | 0 |
| 2.9253 | 1 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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] |
CouchCat/ma_ner_v7_distil | [
"pytorch",
"distilbert",
"token-classification",
"en",
"transformers",
"ner",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
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} | 13 | null | ---
license: mit
language:
- en
---
# Model Card for Model ID
Those models can be used with HSSM package. It was originally created using lanfactory repository. This is the primary source for storing those files.
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
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}
}
} | 38 | null | GPT2-Medium model QAC fine-tuned on SocialIQA dataset.
Based on Leveraging QA Datasets to Improve Generative Data Augmentation paper (by Mekala, Dheeraj and Vu, Tu and Schick, Timo and Shang, Jingb) and https://github.com/dheeraj7596/CONDA
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Coyotl/DialoGPT-test3-arthurmorgan | [
"conversational"
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} | 0 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_bert_fine_tuned_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8826268309
- name: NER Recall
type: recall
value: 0.8940748747
- name: NER F Score
type: f_score
value: 0.8883139705
---
This is a BERT model for Named Entity Recognition, fine-tuned on OntoNotes v5 using Spacy in coNLL-2003 format and BIO tagged.
For more details: https://github.com/nicoladisabato/ner-with-transformers
| Feature | Description |
| --- | --- |
| **Name** | `en_bert_fine_tuned_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (18 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 88.83 |
| `ENTS_P` | 88.26 |
| `ENTS_R` | 89.41 |
| `TRANSFORMER_LOSS` | 135007.00 |
| `NER_LOSS` | 132971.85 | | [
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
inference: true
extra_gated_prompt: >-
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. CompVis 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 carefully here:
https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
duplicated_from: runwayml/stable-diffusion-v1-5
pipeline_tag: image-to-image
---
# Stable Diffusion v1-5 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
Currently six Stable Diffusion checkpoints are provided, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* | [
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Craftified/Bob | [] | null | {
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} | 0 | null | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_xlnet_fine_tuned_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8847416707
- name: NER Recall
type: recall
value: 0.9002161737
- name: NER F Score
type: f_score
value: 0.8924118449
---
This is a XLNet model for Named Entity Recognition, fine-tuned on OntoNotes v5 using Spacy in coNLL-2003 format and BIO tagged.
For more details: https://github.com/nicoladisabato/ner-with-transformers
| Feature | Description |
| --- | --- |
| **Name** | `en_xlnet_fine_tuned_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.5.1,<3.6.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (18 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 89.24 |
| `ENTS_P` | 88.47 |
| `ENTS_R` | 90.02 |
| `TRANSFORMER_LOSS` | 124848.21 |
| `NER_LOSS` | 196123.19 | | [
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Crasher222/kaggle-comp-test | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Crasher222/autonlp-data-kaggle-test",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
"do_sample": null,
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 29 | null | ---
language:
- multilingual
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test-bycol
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. -->
# test-bycol
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the ok_bycol 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: 6.25e-06
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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} | 0 | 2023-05-09T18:33:30Z | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- customized
model-index:
- name: finetune-1epoch
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. -->
# StabilLM Translation Pretraining
This model is a fine-tuned version of [stabilityai/stablelm-base-alpha-7b](https://huggingface.co/stabilityai/stablelm-base-alpha-7b) on the customized dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.3 | [
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Crives/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"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-end2end-questions-generation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.704 | 0.43 | 100 | 1.2946 |
| 1.3902 | 0.86 | 200 | 1.2393 |
| 1.2897 | 1.3 | 300 | 1.2200 |
| 1.2572 | 1.73 | 400 | 1.1981 |
| 1.2286 | 2.16 | 500 | 1.1941 |
| 1.1591 | 2.59 | 600 | 1.1852 |
| 1.1686 | 3.02 | 700 | 1.1789 |
| 1.1071 | 3.45 | 800 | 1.1810 |
| 1.1117 | 3.89 | 900 | 1.1766 |
| 1.0728 | 4.32 | 1000 | 1.1829 |
| 1.063 | 4.75 | 1100 | 1.1783 |
| 1.0657 | 5.18 | 1200 | 1.1810 |
| 1.031 | 5.61 | 1300 | 1.1809 |
| 1.0304 | 6.05 | 1400 | 1.1809 |
| 1.0111 | 6.48 | 1500 | 1.1818 |
| 1.0161 | 6.91 | 1600 | 1.1822 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.11.0
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Cryptikdw/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 7 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: perioli_vgm_v5.2
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: sroie
type: sroie
config: discharge
split: test
args: discharge
metrics:
- name: Precision
type: precision
value: 0.9702127659574468
- name: Recall
type: recall
value: 0.9539748953974896
- name: F1
type: f1
value: 0.9620253164556961
- name: Accuracy
type: accuracy
value: 0.9980436909031627
---
<!-- 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. -->
# perioli_vgm_v5.2
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0093
- Precision: 0.9702
- Recall: 0.9540
- F1: 0.9620
- Accuracy: 0.9980
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.53 | 100 | 0.0787 | 0.5595 | 0.3933 | 0.4619 | 0.9811 |
| No log | 1.06 | 200 | 0.0450 | 0.8069 | 0.6820 | 0.7392 | 0.9909 |
| No log | 1.6 | 300 | 0.0438 | 0.6469 | 0.8201 | 0.7232 | 0.9874 |
| No log | 2.13 | 400 | 0.0230 | 0.8063 | 0.8536 | 0.8293 | 0.9935 |
| 0.0742 | 2.66 | 500 | 0.0177 | 0.8214 | 0.8661 | 0.8432 | 0.9946 |
| 0.0742 | 3.19 | 600 | 0.0251 | 0.8295 | 0.8954 | 0.8612 | 0.9943 |
| 0.0742 | 3.72 | 700 | 0.0167 | 0.8782 | 0.8745 | 0.8763 | 0.9961 |
| 0.0742 | 4.26 | 800 | 0.0183 | 0.8510 | 0.9079 | 0.8785 | 0.9949 |
| 0.0742 | 4.79 | 900 | 0.0100 | 0.9481 | 0.9163 | 0.9319 | 0.9969 |
| 0.0085 | 5.32 | 1000 | 0.0153 | 0.9048 | 0.9540 | 0.9287 | 0.9967 |
| 0.0085 | 5.85 | 1100 | 0.0112 | 0.9253 | 0.9331 | 0.9292 | 0.9972 |
| 0.0085 | 6.38 | 1200 | 0.0120 | 0.9184 | 0.9414 | 0.9298 | 0.9971 |
| 0.0085 | 6.91 | 1300 | 0.0072 | 0.9417 | 0.9456 | 0.9436 | 0.9980 |
| 0.0085 | 7.45 | 1400 | 0.0102 | 0.9617 | 0.9456 | 0.9536 | 0.9977 |
| 0.0029 | 7.98 | 1500 | 0.0101 | 0.9702 | 0.9540 | 0.9620 | 0.9980 |
| 0.0029 | 8.51 | 1600 | 0.0098 | 0.9580 | 0.9540 | 0.9560 | 0.9979 |
| 0.0029 | 9.04 | 1700 | 0.0075 | 0.9536 | 0.9456 | 0.9496 | 0.9980 |
| 0.0029 | 9.57 | 1800 | 0.0083 | 0.9702 | 0.9540 | 0.9620 | 0.9980 |
| 0.0029 | 10.11 | 1900 | 0.0093 | 0.9617 | 0.9456 | 0.9536 | 0.9979 |
| 0.001 | 10.64 | 2000 | 0.0093 | 0.9702 | 0.9540 | 0.9620 | 0.9980 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
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Crystal/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
# OM
## DESCRIPTION
- **Collection by:** BADMONK
- **File Types:** LoRA
- **Language(s) (NLP):** EN
- **License:** Creativeml-Openrail-M
# PERSONAL USE
The following is a collection that is set to public for personal use.
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Cthyllax/DialoGPT-medium-PaladinDanse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"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": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 10 | null | ---
license: mit
---
## Article Title Generator
The model is based on the T5 language model and trained using a large collection of Medium articles.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("czearing/article-title-generator")
model = AutoModel.from_pretrained("czearing/article-title-generator")
```
## License
MIT
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] |
Culmenus/IceBERT-finetuned-ner | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:gpl-3.0",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"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,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 5 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="harsh0000/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
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Culmenus/XLMR-ENIS-finetuned-ner | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:mim_gold_ner",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 6 | null | ---
language:
- en
tags:
- summarization
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
datasets:
- cnn_dailymail
model-index:
- name: facebook/bart-large-cnn
results:
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: train
metrics:
- name: ROUGE-1
type: rouge
value: 42.9486
verified: true
- name: ROUGE-2
type: rouge
value: 20.8149
verified: true
- name: ROUGE-L
type: rouge
value: 30.6186
verified: true
- name: ROUGE-LSUM
type: rouge
value: 40.0376
verified: true
- name: loss
type: loss
value: 2.529000997543335
verified: true
- name: gen_len
type: gen_len
value: 78.5866
verified: true
---
# BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart).
Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.
## Intended uses & limitations
You can use this model for text summarization.
### How to use
Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
ARTICLE = """ New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York.
A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband.
Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other.
In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage.
Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the
2010 marriage license application, according to court documents.
Prosecutors said the marriages were part of an immigration scam.
On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further.
After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective
Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002.
All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say.
Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages.
Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted.
The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s
Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali.
Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force.
If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
"""
print(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))
>>> [{'summary_text': 'Liana Barrientos, 39, is charged with two counts of "offering a false instrument for filing in the first degree" In total, she has been married 10 times, with nine of her marriages occurring between 1999 and 2002. She is believed to still be married to four men.'}]
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1910-13461,
author = {Mike Lewis and
Yinhan Liu and
Naman Goyal and
Marjan Ghazvininejad and
Abdelrahman Mohamed and
Omer Levy and
Veselin Stoyanov and
Luke Zettlemoyer},
title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language
Generation, Translation, and Comprehension},
journal = {CoRR},
volume = {abs/1910.13461},
year = {2019},
url = {http://arxiv.org/abs/1910.13461},
eprinttype = {arXiv},
eprint = {1910.13461},
timestamp = {Thu, 31 Oct 2019 14:02:26 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | [
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Culmenus/checkpoint-168500-finetuned-de-to-is_nr2 | [] | null | {
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} | 0 | 2023-05-09T18:56:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: test-dialogue-summarization
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: validation
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 48.0348
---
<!-- 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. -->
# test-dialogue-summarization
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5507
- Rouge1: 48.0348
- Rouge2: 24.8215
- Rougel: 40.5048
- Rougelsum: 44.3467
- Gen Len: 18.1638
## 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: 9e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 460 | 1.5507 | 48.0348 | 24.8215 | 40.5048 | 44.3467 | 18.1638 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Culmenus/opus-mt-de-is-finetuned-de-to-is | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
],
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"translation_en_to_ro": {
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}
} | 1 | null | ---
license: other
datasets:
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
tags:
- uncensored
---
This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note:
An uncensored model has no guardrails.
You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
Publishing anything this model generates is the same as publishing it yourself.
You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it. | [
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} | 0 | 2021-08-23T18:29:46Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-qa-mpnet-base-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## 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, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
#Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## 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 correct pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take average of all tokens
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output.last_hidden_state #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)
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1")
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 768 |
| Produces normalized embeddings | Yes |
| Pooling-Method | Mean pooling |
| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** | | [
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} | 0 | null | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
widget:
- text: "George Washington went to Washington"
---
## English NER in Flair (fast model)
This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,92** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### 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("flair/ner-english-fast")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# 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)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9515)]
Span [5]: "Washington" [− Labels: LOC (0.992)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# contextual string embeddings, forward
FlairEmbeddings('news-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
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DCU-NLP/bert-base-irish-cased-v1 | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 1,244 | null | ---
license: apache-2.0
widget:
- text: "ଗୋଟିଏ ସମୟର କଥା"
example_title: "example 1"
- text: "ପ୍ରତ୍ୟେକ ଲୋକ ନିଶ୍ଚୟ କରିବେ"
example_title: "example 2"
- text: "ଆଜିର ଖବର "
example_title: "example 3"
- text: "ଆଜିଠାରୁ ମୁଁ"
example_title: "example 4"
---
pre train steps - 3400
vocab size - 5k | [
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DJSammy/bert-base-danish-uncased_BotXO-ai | [
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] | fill-mask | {
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} | 14 | null | An Alpaca LoRA adapter converted from [tloen/alpaca-lora-7b](https://huggingface.co/tloen/alpaca-lora-7b) to ggml format. | [
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DJStomp/TestingSalvoNET | [
"transformers"
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} | 1 | null | Access to model DarwinAnim8or/gpt-grug-1.5b is restricted and you are not in the authorized list. Visit https://huggingface.co/DarwinAnim8or/gpt-grug-1.5b to ask for access. | [
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DKpro000/DialoGPT-small-harrypotter | [] | null | {
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}
} | 0 | null | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### mymjaw-v7_sd-v1-5 Dreambooth model trained by MadCadaver with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
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DTAI-KULeuven/mbert-corona-tweets-belgium-topics | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Dutch",
"French",
"English",
"Tweets",
"Topic classification"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"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,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 167 | null | ---
tags:
- spacy
- text-classification
language:
- en
model-index:
- name: en_imdb_sent_cnn
results: []
---
| Feature | Description |
| --- | --- |
| **Name** | `en_imdb_sent_cnn` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.4.4,<3.5.0` |
| **Default Pipeline** | `textcat` |
| **Components** | `textcat` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (2 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`textcat`** | `pos`, `neg` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `CATS_SCORE` | 82.51 |
| `CATS_MICRO_P` | 82.51 |
| `CATS_MICRO_R` | 82.51 |
| `CATS_MICRO_F` | 82.51 |
| `CATS_MACRO_P` | 82.51 |
| `CATS_MACRO_R` | 82.51 |
| `CATS_MACRO_F` | 82.51 |
| `CATS_MACRO_AUC` | 90.17 |
| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
| `TEXTCAT_LOSS` | 2099.23 | | [
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DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
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} | 1,907 | null | ---
tags:
- image-classification
- timm
library_name: timm
license: cc-by-nc-4.0
---
# Model card for vit_base_patch16_224.mae
A Vision Transformer (ViT) image feature model. Pretrained on ImageNet-1k with Self-Supervised Masked Autoencoder (MAE) method.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 85.8
- GMACs: 17.6
- Activations (M): 23.9
- Image size: 224 x 224
- **Papers:**
- Masked Autoencoders Are Scalable Vision Learners: https://arxiv.org/abs/2111.06377
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Pretrain Dataset:** ImageNet-1k
- **Original:** https://github.com/facebookresearch/mae
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch16_224.mae', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_224.mae',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@Article{MaskedAutoencoders2021,
author = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Doll{'a}r and Ross Girshick},
journal = {arXiv:2111.06377},
title = {Masked Autoencoders Are Scalable Vision Learners},
year = {2021},
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
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Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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},
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},
"translation_en_to_ro": {
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}
}
} | 151 | null | ---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
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Davlan/bert-base-multilingual-cased-finetuned-igbo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
} | 15 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: t5-mt-en-ca-new
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: ca-en
split: train
args: ca-en
metrics:
- name: Bleu
type: bleu
value: 0.2189
---
<!-- 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-mt-en-ca-new
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4508
- Bleu: 0.2189
- Gen Len: 17.5396
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 231 | 3.6893 | 0.0917 | 17.7687 |
| No log | 2.0 | 462 | 3.5711 | 0.1244 | 17.6591 |
| 3.9065 | 3.0 | 693 | 3.5008 | 0.1889 | 17.6059 |
| 3.9065 | 4.0 | 924 | 3.4631 | 0.2197 | 17.5244 |
| 3.7288 | 5.0 | 1155 | 3.4508 | 0.2189 | 17.5396 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 11 | null | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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: Find your model_id: markeidsaune/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Davlan/bert-base-multilingual-cased-finetuned-naija | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 13 | null | ---
license: cc-by-nc-4.0
---
---
license: cc-by-nc-4.0
---
Model trained on 1.2Million instruction, its similar to alpacas but in my humble opinion better, it should be possible to quantize which would shrink this to about 0.7-1GB.
This model was pushed down to 0.45 training loss and 0.77 validation loss. This is the third iteration and is better than Dante_1.3B1
Example
```
### Instruction:
Below is a conversation where an AI language model is chatting with a human, complete the conversation, AI assists the human by answering their questions in a helpful and informative manner and considers the conversation history.
AI DOES NOT RESPOND OR CONDONE HARMFUL OR SEXUAL RESPONSES.
### Input:
[HM]: Write c# code that prints hello world.
[AI]: Here's some C# code that prints "Hello World" to the console when called:
```csharp
Console.WriteLine("Hello World");
```
[HM]: I want it to print bye world.
### Response:
Here's some C# code that prints "Goodbye World" to the console when called:
```csharp
Console.WriteLine("Goodbye World");
```<end>
```
```
#Example2
### Instruction:
Below is a conversation where an AI language model is chatting with a human, complete the conversation, AI is designed to assist the human by answering their questions in a helpful and informative manner, AI responds clearly and considers the conversation history.
### Input:
[AI] and [HM] are not the same.
AI is chatting with [HM], when AI is responding to [HM], AI always reads all [HM] previous questions.
[HM]: Do you advise against grandpa's sexual advances?
[AI]: I would recommend against grandpa's sexual advances as it can be inappropriate and could lead to unwanted attention from the other person.<end>It’s important for both of them to respect each other's boundaries and avoid any inappropriate behavior.
[HM]: But what if i really like grandpa?
### Response:
If you truly love Grandpa, then it may be best to let him know before he starts making advances towards you.<end>
```
If used for commerical, its fine and only fine if a percentage bigger than 2% of revenue is paid to the creator. | [
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Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 67 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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-Huggy
2. Step 1: Find your model_id: jules654/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Davlan/bert-base-multilingual-cased-finetuned-wolof | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
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],
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}
}
} | 4 | null | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.80 +/- 23.94
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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Davlan/xlm-roberta-base-finetuned-chichewa | [
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"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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}
} | 5 | null | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -131.47 +/- 71.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.0003
'num_envs': 8
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'mattjmattj/HF_RL_unit8_PPO_LunarLander-v2'
'batch_size': 1024
'minibatch_size': 256}
```
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Davlan/xlm-roberta-base-finetuned-igbo | [
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} | 68 | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://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-Huggy
2. Step 1: Find your model_id: tastytoast/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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Davlan/xlm-roberta-base-finetuned-kinyarwanda | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 61 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 282.92 +/- 21.53
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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Davlan/xlm-roberta-base-finetuned-lingala | [
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"transformers",
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"autotrain_compatible"
] | fill-mask | {
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}
} | 9 | null |
# Model Card for Digital Diffusion
Higher quality 2.1 images!
Thank you for checking out the new and improved digital diffusion! This model is a general purpose 2.1 model that does well in many styles and themes. I really hope you enjoy the model, and please share what you can create with it!
Basic prompting guide:
This model might prompt closer to SD 1.5, since you don't need overly complicated prompts to get really cool images. The two most important tokens are digital art and photograph. These control what kind of image you are generating. It's pretty self explanatory what they do as well.
There are other tokens that can improve your images or make them feel a certain way, or are just fun to experiment with. Here are some of my favorites!
- Portrait
- Landscape
- Vehicle
- Cinematic
- Dramatic
- Fantasy
- Particles
- Steampunk
- Moody
- Dark
- Bright
- Calm
- Sunrise
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: lucas940809/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# lucas940809/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6835
- Validation Loss: 1.9723
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5265 | 2.4395 | 0 |
| 1.9706 | 1.9723 | 1 |
| 1.6835 | 1.9723 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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Declan/Breitbart_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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],
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}
} | 3 | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 441.00 +/- 90.13
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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Declan/CNN_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 3 | 2023-05-10T00:54:10Z | ---
license: apache-2.0
datasets:
- Murple/ksponspeech
language:
- ko
metrics:
- cer
- wer
pipeline_tag: automatic-speech-recognition
---
# Whisper-Medium-KsponSpeech
The Whisper-medium Model finetunned with [KsponSpeech](https://huggingface.co/datasets/Murple/ksponspeech)
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by :** [yw0nam](https://github.com/yw0nam)
- **Shared by :** [yw0nam](https://github.com/yw0nam)
- **Model type :** ASR
- **License:** [apache-2.0]
## Uses
```
processor = WhisperProcessor.from_pretrained("openai/whisper-medium", language="ko", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained('spow12/whisper-medium-ksponspeech').cuda()
data, _ = librosa.load(wav_path, sr=16000)
input_features = processor(data, sampling_rate=16000, return_tensors="pt").input_features.cuda()
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
```
### Metrics
Metric | result |
--- | --- |
WER | 21.44501150805564 |
CER | 6.681410778721918 |
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Declan/ChicagoTribune_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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],
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.83 +/- 33.36
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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Declan/ChicagoTribune_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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},
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},
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},
"translation_en_to_ro": {
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}
}
} | 7 | null | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Yang-hugging-face-2023/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
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Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"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,
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"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 3 | null | To use the model, add the following from the transformers package:
(1) ClinicalBERT tokenizer:
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
(2) Model type:
model = BertForSequenceClassification.from_pretrained(checkpoint_directory, num_labels=3)
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} | 5 | null | Language: **English** [简体中文](./cn_README.md) [한국어](./ko_README.md)
# DDSP-SVC
<div align="center">
<img src="https://storage.googleapis.com/ddsp/github_images/ddsp_logo.png" width="200px" alt="logo"></img>
</div>
Real-time end-to-end singing voice conversion system based on DDSP (Differentiable Digital Signal Processing）.
## (3.0 - Experimental) Shallow diffusion model (DDSP + Diff-SVC refactor version)
Data preparation, configuring the pre-trained encoder (hubert or contentvec ) and vocoder (nsf-hifigan) is the same as training a pure DDSP model.
Because the diffusion model is more difficult to train, we provide some pre-trained models here:
https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/blob/main/hubertsoft_pitch_410k/model_0.pt (using 'hubertsoft' encoder)
https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/blob/main/pitch_400k/model_0.pt (using 'contentvec768l12' encoder)
Move the `model_0.pt` to the model export folder specified by the 'expdir' parameter in `diffusion.yaml`, and the program will automatically load the pre-trained models in that folder.
(1) Preprocessing：
```bash
python preprocess.py -c configs/diffusion.yaml
```
This preprocessing can also be used to train the DDSP model without preprocessing twice, but you need to ensure that the parameters under the 'data' tag in yaml files are consistent.
(2) Train a diffusion model：
```bash
python train_diff.py -c configs/diffusion.yaml
```
(3) Train a DDSP model：
```bash
python train.py -c configs/combsub.yaml
```
As mentioned above, re-preprocessing is not required, but please check whether the parameters of `combsub.yaml` and `diffusion.yaml` match. The number of speakers 'n_spk' can be inconsistent, but try to use the same number to represent the same speaker (this makes inference easier).
(4) Non-real-time inference：
```bash
python main_diff.py -i <input.wav> -ddsp <ddsp_ckpt.pt> -diff <diff_ckpt.pt> -o <output.wav> -k <keychange (semitones)> -id <speaker_id> -diffid <diffusion_speaker_id> -speedup <speedup> -method <method> -kstep <kstep>
```
'speedup' is the acceleration speed, 'method' is 'pndm' or 'dpm-solver', 'kstep' is the number of shallow diffusion steps, 'diffid' is the speaker id of the diffusion model, and other parameters have the same meaning as `main.py`.
If the same id has been used to represent the same speaker during training, '-diffid' can be empty, otherwise the '-diffid' option needs to be specified.
If '-ddsp' is empty, the pure diffusion model is used, at this time, shallow diffusion is performed with the mel of the input source, and if further '-kstep' is empty, full-depth Gaussian diffusion is performed.
The program will automatically check whether the parameters of the DDSP model and the diffusion model match (sampling rate, hop size and encoder), and if they do not match, it will ignore loading the DDSP model and enter Gaussian diffusion mode.
Real-time GUI：
```bash
python gui_diff.py
```
## 0. Introduction
DDSP-SVC is a new open source singing voice conversion project dedicated to the development of free AI voice changer software that can be popularized on personal computers.
Compared with the more famous [Diff-SVC](https://github.com/prophesier/diff-svc) and [SO-VITS-SVC](https://github.com/svc-develop-team/so-vits-svc), its training and synthesis have much lower requirements for computer hardware, and the training time can be shortened by orders of magnitude. In addition, when performing voice change in real-time, the hardware resources of this project are significantly lower than SO-VITS-SVC, and Diff-SVC is too slow to perform voice change in real-time.
Although the original synthesis quality of DDSP is not ideal (the original output can be heard in tensorboard while training), after using the pre-trained vocoder-based enhancer, the sound quality for some dateset can reach a level close to SO-VITS-SVC.
If the quality of the training data is very high, probably still Diff-SVC will have the highest sound quality. The demo outputs are in the `samples` folder, and the related model checkpoint can be downloaded from the release page.
Disclaimer: Please make sure to only train DDSP-SVC models with **legally obtained authorized data**, and do not use these models and any audio they synthesize for illegal purposes. The author of this repository is not responsible for any infringement, fraud and other illegal acts caused by the use of these model checkpoints and audio.
Update log: I am too lazy to translate, please see the Chinese version readme.
## 1. Installing the dependencies
We recommend first installing PyTorch from the [**official website**](https://pytorch.org/), then run:
```bash
pip install -r requirements.txt
```
NOTE : I only test the code using python 3.8 (windows) + torch 1.9.1 + torchaudio 0.6.0, too new or too old dependencies may not work
UPDATE: python 3.8 (windows) + cuda 11.8 + torch 2.0.0 + torchaudio 2.0.1 works, and training is faster.
## 2. Configuring the pretrained model
UPDATE: ContentVec encoder is supported now. You can download the pretrained [ContentVec](https://ibm.ent.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr) encoder instead of HubertSoft encoder and modify the configuration file to use it.
- **(Required)** Download the pretrained [**HubertSoft**](https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt) encoder and put it under `pretrain/hubert` folder.
- Get the pretrained vocoder-based enhancer from the [DiffSinger Community Vocoders Project](https://openvpi.github.io/vocoders) and unzip it into `pretrain/` folder
## 3. Preprocessing
Put all the training dataset (.wav format audio clips) in the below directory:
`data/train/audio`.
Put all the validation dataset (.wav format audio clips) in the below directory:
`data/val/audio`.
You can also run
```bash
python draw.py
```
to help you select validation data (you can adjust the parameters in `draw.py` to modify the number of extracted files and other parameters)
Then run
```bash
python preprocess.py -c configs/combsub.yaml
```
for a model of combtooth substractive synthesiser (**recommend**), or run
```bash
python preprocess.py -c configs/sins.yaml
```
for a model of sinusoids additive synthesiser.
You can modify the configuration file `config/<model_name>.yaml` before preprocessing. The default configuration is suitable for training 44.1khz high sampling rate synthesiser with GTX-1660 graphics card.
NOTE 1: Please keep the sampling rate of all audio clips consistent with the sampling rate in the yaml configuration file ! If it is not consistent, the program can be executed safely, but the resampling during the training process will be very slow.
NOTE 2: The total number of the audio clips for training dataset is recommended to be about 1000, especially long audio clip can be cut into short segments, which will speed up the training, but the duration of all audio clips should not be less than 2 seconds. If there are too many audio clips, you need a large internal-memory or set the 'cache_all_data' option to false in the configuration file.
NOTE 3: The total number of the audio clips for validation dataset is recommended to be about 10, please don't put too many or it will be very slow to do the validation.
NOTE 4: If your dataset is not very high quality, set 'f0_extractor' to 'crepe' in the config file. The crepe algorithm has the best noise immunity, but at the cost of greatly increasing the time required for data preprocessing.
UPDATE: Multi-speaker training is supported now. The 'n_spk' parameter in configuration file controls whether it is a multi-speaker model. If you want to train a **multi-speaker** model, audio folders need to be named with **positive integers not greater than 'n_spk'** to represent speaker ids, the directory structure is like below:
```bash
# training dataset
# the 1st speaker
data/train/audio/1/aaa.wav
data/train/audio/1/bbb.wav
...
# the 2nd speaker
data/train/audio/2/ccc.wav
data/train/audio/2/ddd.wav
...
# validation dataset
# the 1st speaker
data/val/audio/1/eee.wav
data/val/audio/1/fff.wav
...
# the 2nd speaker
data/val/audio/2/ggg.wav
data/val/audio/2/hhh.wav
...
```
If 'n_spk' = 1, The directory structure of the **single speaker** model is still supported, which is like below:
```bash
# training dataset
data/train/audio/aaa.wav
data/train/audio/bbb.wav
...
# validation dataset
data/val/audio/ccc.wav
data/val/audio/ddd.wav
...
```
## 4. Training
```bash
# train a combsub model as an example
python train.py -c configs/combsub.yaml
```
The command line for training other models is similar.
You can safely interrupt training, then running the same command line will resume training.
You can also finetune the model if you interrupt training first, then re-preprocess the new dataset or change the training parameters (batchsize, lr etc.) and then run the same command line.
## 5. Visualization
```bash
# check the training status using tensorboard
tensorboard --logdir=exp
```
Test audio samples will be visible in TensorBoard after the first validation.
NOTE: The test audio samples in Tensorboard are the original outputs of your DDSP-SVC model that is not enhanced by an enhancer. If you want to test the synthetic effect after using the enhancer (which may have higher quality) , please use the method described in the following chapter.
## 6. Non-real-time VC
(**Recommend**) Enhance the output using the pretrained vocoder-based enhancer:
```bash
# high audio quality in the normal vocal range if enhancer_adaptive_key = 0 (default)
# set enhancer_adaptive_key > 0 to adapt the enhancer to a higher vocal range
python main.py -i <input.wav> -m <model_file.pt> -o <output.wav> -k <keychange (semitones)> -id <speaker_id> -eak <enhancer_adaptive_key (semitones)>
```
Raw output of DDSP:
```bash
# fast, but relatively low audio quality (like you hear in tensorboard)
python main.py -i <input.wav> -m <model_file.pt> -o <output.wav> -k <keychange (semitones)> -id <speaker_id> -e false
```
Other options about the f0 extractor and response threhold，see:
```bash
python main.py -h
```
(UPDATE) Mix-speaker is supported now. You can use "-mix" option to design your own vocal timbre, below is an example:
```bash
# Mix the timbre of 1st and 2nd speaker in a 0.5 to 0.5 ratio
python main.py -i <input.wav> -m <model_file.pt> -o <output.wav> -k <keychange (semitones)> -mix "{1:0.5, 2:0.5}" -eak 0
```
## 7. Real-time VC
Start a simple GUI with the following command:
```bash
python gui.py
```
The front-end uses technologies such as sliding window, cross-fading, SOLA-based splicing and contextual semantic reference, which can achieve sound quality close to non-real-time synthesis with low latency and resource occupation.
Update: A splicing algorithm based on a phase vocoder is now added, but in most cases the SOLA algorithm already has high enough splicing sound quality, so it is turned off by default. If you are pursuing extreme low-latency real-time sound quality, you can consider turning it on and tuning the parameters carefully, and there is a possibility that the sound quality will be higher. However, a large number of tests have found that if the cross-fade time is longer than 0.1 seconds, the phase vocoder will cause a significant degradation in sound quality.
## 8. Acknowledgement
* [ddsp](https://github.com/magenta/ddsp)
* [pc-ddsp](https://github.com/yxlllc/pc-ddsp)
* [soft-vc](https://github.com/bshall/soft-vc)
* [DiffSinger (OpenVPI version)](https://github.com/openvpi/DiffSinger)
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Declan/Politico_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ---
license: apache-2.0
---
# RedPajama-INCITE-Chat-3B-v1-ONNX
<!-- Provide a quick summary of what the model is/does. -->
RedPajama-INCITE-Chat-3B-v1 model by Together Computer exported to ONNX for faster CPU inference and fine-tuning.
## Inference
```python
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForCausalLM
import torch
# init
tokenizer = AutoTokenizer.from_pretrained("orangetin/RedPajama-INCITE-Chat-3B-v1-ONNX")
model = ORTModelForCausalLM.from_pretrained("orangetin/RedPajama-INCITE-Chat-3B-v1-ONNX", use_cache=False)
# infer
prompt = "<human>: Who is Alan Turing?\n<bot>:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
```
## Fine-tuning
Refer to this example for fine-tuning: https://github.com/huggingface/optimum/tree/main/examples/onnxruntime/training/language-modeling | [
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Declan/Reuters_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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],
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"task_specific_params": {
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},
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"translation_en_to_ro": {
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}
}
} | 5 | null | ---
license: openrail
datasets:
- OpenAssistant/oasst1
language:
- aa
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: reinforcement-learning
tags:
- music
--- | [
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Declan/Reuters_model_v3 | [
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"transformers",
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}
} | 7 | null |
---
license: creativeml-openrail-m
---
# Chrysanthemum is an anime/magic-fantasy model.
A merge of:
- [Perceptron_v2](https://civitai.com/models/25516/perceptron)
- [duelanimemix_v1](https://civitai.com/models/28477/duelanimemix)
- [henmixReal_v30](https://civitai.com/models/20282/henmixreal)
## Recommended Settings:
Most settings should be experimented with for best results. Follow [this video](https://www.youtube.com/watch?v=EmA0RwWv-os&ab_channel=SebastianKamph) for better details/quality.
**Sampling Method:** DPM++ 2M Karras / DPM++ SDE Karras
**Sampling Steps:** 20-38+
**Hires Fix:** ✅
- **Upscaler:** [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_model.md)
- **Hires steps:** 0-10+
- **Denoising strength:** 0.3-0.6
- **CFG Scale:** 7.5-10
- **Clip Skip:** 1/2
## Suggested Embeddings:
- [easynegative](https://huggingface.co/datasets/gsdf/EasyNegative)
- [bad-hands-5](https://huggingface.co/yesyeahvh/bad-hands-5)
- [bad_prompt_version2](https://huggingface.co/datasets/Nerfgun3/bad_prompt)
- [ng_deepnegative_v1_75t](https://civitai.com/models/4629/deep-negative-v1x)
## Suggested VAE(s):
- [anything-v4.0](https://huggingface.co/andite/anything-v4.0)
- [vae-ft-mse-840000-ema-pruned](https://huggingface.co/stabilityai/sd-vae-ft-mse)
- [clearvae](https://civitai.com/models/22354/clearvae)
### See example images at [CivitAI](https://civitai.com/models/62253/chrysanthemum) | [
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Declan/WallStreetJournal_model_v6 | [] | null | {
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} | 0 | null | ---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: viettel-xlm-roberta-base-squad-finetune-viquad
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. -->
# viettel-xlm-roberta-base-squad-finetune-viquad
This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2](https://huggingface.co/deepset/xlm-roberta-base-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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