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81
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sequence | pipeline_tag
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values | config
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int64 0
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Ayham/robertagpt2_xsum2 | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 6 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 270.57 +/- 10.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
language:
- eng
tags:
- text-classification
- Sentiment
- RoBERTa
- Financial Statements
- Accounting
- Finance
- Business
- ESG
- CSR Reports
- Financial News
- Earnings Call Transcripts
- Sustainability
- Corporate governance
---
<!DOCTYPE html>
<html>
<body>
<h1><b>Financial-RoBERTa</b></h1>
<p><b>Financial-RoBERTa</b> is a pre-trained NLP model to analyze sentiment of financial text including:</p>
<ul style="PADDING-LEFT: 40px">
<li>Financial Statements,</li>
<li>Earnings Announcements,</li>
<li>Earnings Call Transcripts,</li>
<li>Corporate Social Responsibility (CSR) Reports,</li>
<li>Environmental, Social, and Governance (ESG) News,</li>
<li>Financial News,</li>
<li>Etc.</li>
</ul>
<p>Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.</p>
<p>The model will give softmax outputs for three labels: <b>Positive</b>, <b>Negative</b> or <b>Neutral</b>.</p>
<p><b>How to perform sentiment analysis:</b></p>
<p>The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:</p>
<pre>
<code>
from transformers import pipeline
sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta-large-sentiment")
print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020."))
</code>
</pre>
<p>I provide an example script via <a href="https://colab.research.google.com/drive/11RGWU3UDtxnjan8Ug6dyX82m9fBV6CGo?usp=sharing" target="_blank">Google Colab</a>. You can load your data to a Google Drive and run the script for free on a Colab.
<p><b>Citation and contact:</b></p>
<p>Please cite <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4115943" target="_blank">this paper</a> when you use the model. Feel free to reach out to [email protected] with any questions or feedback you may have.<p/>
</body>
</html>
|
BaptisteDoyen/camembert-base-xnli | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] | zero-shot-classification | {
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} | 405,474 | 2022-05-16T04:56:54Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: akmal2500/bert-finetuned-squad
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. -->
# akmal2500/bert-finetuned-squad
This model is a fine-tuned version of [akmal2500/bert-finetuned-squad](https://huggingface.co/akmal2500/bert-finetuned-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5715
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5546, '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 |
|:----------:|:-----:|
| 0.5715 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: mit
---
# How to use
```python
from transformers import pipeline
generator = pipeline('text-generation', model="DedsecurityAI/dpt-125mb")
generator("Hello Simon")
[{'generated_text': 'Hello Simon :) Welcome aboard aboard :) :) :) :) :) :) :) :) :) :) :) :) :) :)'}]
``` |
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28 | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
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} | 18 | 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 117759 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
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": null,
"warmup_steps": 11775,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
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} | 0 | 2022-05-16T07:43:43Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- XpCo
model-index:
- name: XpCoDir2
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. -->
# XpCoDir2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the XpCoDataset 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0
- Datasets 2.0.0
- Tokenizers 0.10.3
|
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} | 0 | null | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/355
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/aidatatang_200zh/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless2/train.py \
--world-size 2 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--lang-dir data/lang_char \
--max-duration 250
```
## Evaluation results
The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 5.53 | 6.59 | --epoch 29, --avg 19, --max-duration 100 |
| modified beam search (beam size 4) | 5.28 | 6.32 | --epoch 29, --avg 19, --max-duration 100 |
| fast beam search (set as default) | 5.29 | 6.33 | --epoch 29, --avg 19, --max-duration 1500|
|
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} | 0 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-finetuned-cord
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. -->
# layoutlmv2-finetuned-cord
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.1
- Pytorch 1.10.0+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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} | 0 | null | Thiss project is translated and documented for an internship to gain experince in XLS-R model and Wav2Vec2 architectures. You can read the Turkish documentation on medium.com
https://medium.com/loudest-machine-learning/wav2vec2-xls-r-ile-t%C3%BCrk%C3%A7e-sesten-metine-%C3%A7eviri-25212fdce0d8 |
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1085
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5573 | 1.0 | 2249 | 6.4633 |
| 6.1893 | 2.0 | 4498 | 6.1993 |
| 6.0153 | 3.0 | 6747 | 6.1085 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 10 | 2022-05-16T12:36:12Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5
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. -->
# syp1229/koelectra-base-v3-generator-finetuned-koidiom-epoch5
This model is a fine-tuned version of [monologg/koelectra-base-v3-generator](https://huggingface.co/monologg/koelectra-base-v3-generator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1280
- Validation Loss: 1.8541
- 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.4450 | 2.1108 | 0 |
| 2.2462 | 1.9578 | 1 |
| 2.1990 | 1.9394 | 2 |
| 2.1306 | 1.9433 | 3 |
| 2.1280 | 1.8541 | 4 |
### Framework versions
- Transformers 4.19.1
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 7 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Yarn007/autotrain-data-Napkin
co2_eq_emissions: 0.020162211418903533
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 872827783
- CO2 Emissions (in grams): 0.020162211418903533
## Validation Metrics
- Loss: 0.25198695063591003
- Accuracy: 0.9325714285714286
- Macro F1: 0.9254931094274171
- Micro F1: 0.9325714285714286
- Weighted F1: 0.9323540959391766
- Macro Precision: 0.9286720054236212
- Micro Precision: 0.9325714285714286
- Weighted Precision: 0.9324375609546055
- Macro Recall: 0.9227549386201338
- Micro Recall: 0.9325714285714286
- Weighted Recall: 0.9325714285714286
## 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/Yarn007/autotrain-Napkin-872827783
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Yarn007/autotrain-Napkin-872827783", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
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"gpt2",
"text-generation",
"transformers"
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} | 8 | 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: 283.12 +/- 15.49
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
...
```
|
BigSalmon/InformalToFormalLincoln17 | [
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"gpt2",
"text-generation",
"transformers"
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} | 12 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 236.68 +/- 25.22
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
BigSalmon/InformalToFormalLincoln18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.919
---
<!-- 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. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2402
- Accuracy: 0.919
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9163 | 1.0 | 500 | 0.2402 | 0.919 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
BigSalmon/InformalToFormalLincoln19 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 11 | null | ---
license: apache-2.0
tags:
- multilingual model
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-multilingual-xlsum
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. -->
# mt5-small-finetuned-multilingual-xlsum
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7979
- Rouge1: 9.2017
- Rouge2: 2.3976
- Rougel: 7.7055
- Rougelsum: 7.7347
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.4524 | 1.0 | 3375 | 2.9251 | 8.1565 | 1.9058 | 6.7949 | 6.8196 |
| 3.6707 | 2.0 | 6750 | 2.8524 | 8.7884 | 2.147 | 7.339 | 7.3678 |
| 3.5273 | 3.0 | 10125 | 2.8184 | 9.1157 | 2.3886 | 7.6228 | 7.6592 |
| 3.4452 | 4.0 | 13500 | 2.8028 | 9.2619 | 2.406 | 7.7607 | 7.7921 |
| 3.4074 | 5.0 | 16875 | 2.7979 | 9.2017 | 2.3976 | 7.7055 | 7.7347 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
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} | 5 | null | ---
license: other
---
Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base).
AutoTinyBERT provides a model zoo that can meet different latency requirements. |
BigSalmon/MrLincoln11 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9271021143652434
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2158
- Accuracy: 0.927
- F1: 0.9271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8302 | 1.0 | 250 | 0.3104 | 0.905 | 0.9032 |
| 0.2499 | 2.0 | 500 | 0.2158 | 0.927 | 0.9271 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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} | 0 | null | Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base).
AutoTinyBERT provides a model zoo that can meet different latency requirements. |
BigSalmon/MrLincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 10 | null | ---
license: other
---
Pre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT. In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters for efficient pre-trained language models (at least 6x faster than BERT-base).
AutoTinyBERT provides a model zoo that can meet different latency requirements. |
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-est
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. -->
# xlm-roberta-base-finetuned-est
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8077
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 52 | 4.2865 |
| No log | 2.0 | 104 | 4.0711 |
| No log | 3.0 | 156 | 3.9351 |
| No log | 4.0 | 208 | 3.8885 |
| No log | 5.0 | 260 | 3.8077 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
BigSalmon/ParaphraseParentheses | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 10 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: nouman10/robertabase-finetuned-claim-ltp-full-prompt_
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. -->
# nouman10/robertabase-finetuned-claim-ltp-full-prompt_
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: 0.0334
- Validation Loss: 0.0237
- 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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -427, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1997 | 0.0443 | 0 |
| 0.0334 | 0.0237 | 1 |
### Framework versions
- Transformers 4.19.1
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 13 | null | ---
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -200.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
---
# **PPO** Agent playing **MountainCar-v0**
This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install stable-baselines3
pip install huggingface_sb3
```
Then, you can use the model like this:
```python
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="kingabzpro/Full-Force-MountainCar-v0", filename="Full-Force-MountainCar-v0.zip")
model = PPO.load(checkpoint)
# Evaluate the agent
eval_env = gym.make('MountainCar-v0')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
action, _state = model.predict(obs)
obs, reward, done, info = eval_env.step(action)
eval_env.render()
if done:
obs = eval_env.reset()
eval_env.close()
```
|
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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} | 10 | 2022-05-16T16:26:33Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: -123.02 +/- 62.23
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **DQN** Agent playing **LunarLander-v2**
This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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}
} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-to-distilbert-NER
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.014488935721812434
- name: Recall
type: recall
value: 0.018512285425782565
- name: F1
type: f1
value: 0.016255356878971478
- name: Accuracy
type: accuracy
value: 0.7597280273150055
---
<!-- 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-to-distilbert-NER
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 44.0386
- Precision: 0.0145
- Recall: 0.0185
- F1: 0.0163
- Accuracy: 0.7597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 201.4012 | 1.0 | 110 | 133.7231 | 0.0153 | 0.0106 | 0.0125 | 0.7539 |
| 106.9317 | 2.0 | 220 | 99.3629 | 0.0266 | 0.0305 | 0.0284 | 0.7593 |
| 81.3601 | 3.0 | 330 | 80.3763 | 0.0159 | 0.0214 | 0.0183 | 0.7604 |
| 63.8325 | 4.0 | 440 | 67.7620 | 0.0179 | 0.0244 | 0.0207 | 0.7599 |
| 52.0271 | 5.0 | 550 | 59.0806 | 0.0203 | 0.0268 | 0.0231 | 0.7598 |
| 44.4419 | 6.0 | 660 | 55.3208 | 0.0211 | 0.0278 | 0.0240 | 0.7603 |
| 39.2351 | 7.0 | 770 | 52.4510 | 0.0170 | 0.0222 | 0.0193 | 0.7598 |
| 35.3438 | 8.0 | 880 | 50.4576 | 0.0205 | 0.0268 | 0.0232 | 0.7604 |
| 32.7385 | 9.0 | 990 | 48.3418 | 0.0173 | 0.0227 | 0.0197 | 0.7595 |
| 30.6531 | 10.0 | 1100 | 46.7304 | 0.0147 | 0.0188 | 0.0165 | 0.7600 |
| 29.0811 | 11.0 | 1210 | 46.3386 | 0.0151 | 0.0190 | 0.0168 | 0.7599 |
| 27.9501 | 12.0 | 1320 | 45.4516 | 0.0163 | 0.0204 | 0.0181 | 0.7604 |
| 26.7452 | 13.0 | 1430 | 44.3425 | 0.0154 | 0.0199 | 0.0173 | 0.7592 |
| 25.5367 | 14.0 | 1540 | 44.0415 | 0.0146 | 0.0190 | 0.0165 | 0.7594 |
| 24.5507 | 15.0 | 1650 | 44.0386 | 0.0145 | 0.0185 | 0.0163 | 0.7597 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-est
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. -->
# xlm-roberta-base-finetuned-est
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6781
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 52 | 4.2576 |
| No log | 2.0 | 104 | 3.8075 |
| No log | 3.0 | 156 | 3.6781 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
] | null | {
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} | 0 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Amalq/autotrain-data-smm4h_large_roberta_clean
co2_eq_emissions: 9.123490454955585
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 874027878
- CO2 Emissions (in grams): 9.123490454955585
## Validation Metrics
- Loss: 0.35724225640296936
- Accuracy: 0.8571428571428571
- Precision: 0.7637362637362637
- Recall: 0.8910256410256411
- AUC: 0.9267555361305361
- F1: 0.8224852071005917
## 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/Amalq/autotrain-smm4h_large_roberta_clean-874027878
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Amalq/autotrain-smm4h_large_roberta_clean-874027878", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Bimal/my_bot_model | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | 2022-05-16T18:42:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-emotion-climateChange
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-emotion-climateChange
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7189
- Accuracy: 0.8416
- F1: 0.7735
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 23 | 0.9234 | 0.8416 | 0.7735 |
| No log | 2.0 | 46 | 0.7189 | 0.8416 | 0.7735 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
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} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: syp1229/roberta-base-finetuned-koidiom-epoch5
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. -->
# syp1229/roberta-base-finetuned-koidiom-epoch5
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9099
- Validation Loss: 1.8647
- 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.4941 | 2.0442 | 0 |
| 2.1324 | 1.9281 | 1 |
| 2.0266 | 1.8105 | 2 |
| 1.9568 | 1.8450 | 3 |
| 1.9099 | 1.8647 | 4 |
### Framework versions
- Transformers 4.19.1
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
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} | 14 | null | ---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 58.17 +/- 51.28
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
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}
} | 0 | null | ---
language:
- fr
- en
datasets:
- covost2
tags:
- audio
- speech-translation
- automatic-speech-recognition
license: mit
pipeline_tag: automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
---
# S2T-SMALL-COVOST2-FR-EN-ST
`s2t-small-covost2-fr-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
transcripts/translations autoregressively.
## Intended uses & limitations
This model can be used for end-to-end French speech to English text translation.
See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
### How to use
As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
transcripts by passing the speech features to the model.
*Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
filter bank features. Make sure to install the `torchaudio` package before running this example.*
You could either install those as extra speech dependancies with
`pip install transformers"[speech, sentencepiece]"` or install the packages seperatly
with `pip install torchaudio sentencepiece`.
```python
import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-fr-en-st")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-fr-en-st")
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset(
"patrickvonplaten/librispeech_asr_dummy",
"clean",
split="validation"
)
ds = ds.map(map_to_array)
inputs = processor(
ds["speech"][0],
sampling_rate=48_000,
return_tensors="pt"
)
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
```
## Training data
The s2t-small-covost2-fr-en-st is trained on French-English subset of [CoVoST2](https://github.com/facebookresearch/covost).
CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster
ST research with the largest ever open dataset
## Training procedure
### Preprocessing
The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
is applied to each example.
The texts are lowercased and tokenized using character based SentencePiece vocab.
### Training
The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate
model training and for better performance the encoder is pre-trained for English ASR.
## Evaluation results
CoVOST2 test results for fr-en (BLEU score): 26.25
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2020fairseqs2t,
title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
year = {2020},
}
```
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5628 | 1.0 | 2249 | 6.4705 |
| 6.1956 | 2.0 | 4498 | 6.2012 |
| 6.021 | 3.0 | 6747 | 6.1128 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BrianTin/MTBERT | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 11 | 2022-05-16T21:26:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
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-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8574
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0916 | 1.0 | 2346 | 7.0492 |
| 6.9039 | 2.0 | 4692 | 6.8751 |
| 6.8845 | 3.0 | 7038 | 6.8929 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
] | text-classification | {
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],
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} | 76 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 280.92 +/- 14.67
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
} | 12 | 2022-05-16T21:56:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-finetuned-CPV_Spanish
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-finetuned-CPV_Spanish
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on a dataset derived from Spanish Public Procurement documents from 2019. The whole fine-tuning process is available in the following [Kaggle notebook](https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes).
It achieves the following results on the evaluation set:
- Loss: 0.0465
- F1: 0.7918
- Roc Auc: 0.8860
- Accuracy: 0.7376
- Coverage Error: 10.2744
- Label Ranking Average Precision Score: 0.7973
## Intended uses & limitations
This model only predicts the first two digits of the CPV codes. The list of divisions CPV codes is the following:
| Division | English | Spanish | | | |
|----------|:----------------------------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------------------------------------------------------------------|:-:|:-:|:-:|
| 03 | Agricultural, farming, fishing, forestry and related products | Productos de la agricultura, ganadería, pesca, silvicultura y productos afines | | | |
| 09 | Petroleum products, fuel, electricity and other sources of energy | Derivados del petróleo, combustibles, electricidad y otras fuentes de energía | | | |
| 14 | Mining, basic metals and related products | Productos de la minería, de metales de base y productos afines | | | |
| 15 | Food, beverages, tobacco and related products | Alimentos, bebidas, tabaco y productos afines | | | |
| 16 | Agricultural machinery | Maquinaria agrícola | | | |
| 18 | Clothing, footwear, luggage articles and accessories | Prendas de vestir, calzado, artículos de viaje y accesorios | | | |
| 19 | Leather and textile fabrics, plastic and rubber materials | Piel y textiles, materiales de plástico y caucho | | | |
| 22 | Printed matter and related products | Impresos y productos relacionados | | | |
| 24 | Chemical products | Productos químicos | | | |
| 30 | Office and computing machinery, equipment and supplies except furniture and software packages | Máquinas, equipo y artículos de oficina y de informática, excepto mobiliario y paquetes de software | | | |
| 31 | Electrical machinery, apparatus, equipment and consumables; lighting | Máquinas, aparatos, equipo y productos consumibles eléctricos; iluminación | | | |
| 32 | Radio, television, communication, telecommunication and related equipment | Equipos de radio, televisión, comunicaciones y telecomunicaciones y equipos conexos | | | |
| 33 | Medical equipments, pharmaceuticals and personal care products | Equipamiento y artículos médicos, farmacéuticos y de higiene personal | | | |
| 34 | Transport equipment and auxiliary products to transportation | Equipos de transporte y productos auxiliares | | | |
| 35 | Security, fire | Equipo de seguridad, extinción de incendios, policía y defensa | | | |
| 37 | Musical instruments, sport goods, games, toys, handicraft, art materials and accessories | Instrumentos musicales, artículos deportivos, juegos, juguetes, artículos de artesanía, materiales artísticos y accesorios | | | |
| 38 | Laboratory, optical and precision equipments (excl. glasses) | Equipo de laboratorio, óptico y de precisión (excepto gafas) | | | |
| 39 | Furniture (incl. office furniture), furnishings, domestic appliances (excl. lighting) and cleaning products | Mobiliario (incluido el de oficina), complementos de mobiliario, aparatos electrodomésticos (excluida la iluminación) y productos de limpieza | | | |
| 41 | Collected and purified water | Agua recogida y depurada | | | |
| 42 | Industrial machinery | Maquinaria industrial | | | |
| 43 | Machinery for mining, quarrying, construction equipment | Maquinaria para la minería y la explotación de canteras y equipo de construcción | | | |
| 44 | Construction structures and materials; auxiliary products to construction (except electric apparatus) | Estructuras y materiales de construcción; productos auxiliares para la construcción (excepto aparatos eléctricos) | | | |
| 45 | Construction work | Trabajos de construcción | | | |
| 48 | Software package and information systems | Paquetes de software y sistemas de información | | | |
| 50 | Repair and maintenance services | Servicios de reparación y mantenimiento | | | |
| 51 | Installation services (except software) | Servicios de instalación (excepto software) | | | |
| 55 | Hotel, restaurant and retail trade services | Servicios comerciales al por menor de hostelería y restauración | | | |
| 60 | Transport services (excl. Waste transport) | Servicios de transporte (excluido el transporte de residuos) | | | |
| 63 | Supporting and auxiliary transport services; travel agencies services | Servicios de transporte complementarios y auxiliares; servicios de agencias de viajes | | | |
| 64 | Postal and telecommunications services | Servicios de correos y telecomunicaciones | | | |
| 65 | Public utilities | Servicios públicos | | | |
| 66 | Financial and insurance services | Servicios financieros y de seguros | | | |
| 70 | Real estate services | Servicios inmobiliarios | | | |
| 71 | Architectural, construction, engineering and inspection services | Servicios de arquitectura, construcción, ingeniería e inspección | | | |
| 72 | IT services: consulting, software development, Internet and support | Servicios TI: consultoría, desarrollo de software, Internet y apoyo | | | |
| 73 | Research and development services and related consultancy services | Servicios de investigación y desarrollo y servicios de consultoría conexos | | | |
| 75 | Administration, defence and social security services | Servicios de administración pública, defensa y servicios de seguridad social | | | |
| 76 | Services related to the oil and gas industry | Servicios relacionados con la industria del gas y del petróleo | | | |
| 77 | Agricultural, forestry, horticultural, aquacultural and apicultural services | Servicios agrícolas, forestales, hortícolas, acuícolas y apícolas | | | |
| 79 | Business services: law, marketing, consulting, recruitment, printing and security | Servicios a empresas: legislación, mercadotecnia, asesoría, selección de personal, imprenta y seguridad | | | |
| 80 | Education and training services | Servicios de enseñanza y formación | | | |
| 85 | Health and social work services | Servicios de salud y asistencia social | | | |
| 90 | Sewage, refuse, cleaning and environmental services | Servicios de alcantarillado, basura, limpieza y medio ambiente | | | |
| 92 | Recreational, cultural and sporting services | Servicios de esparcimiento, culturales y deportivos | | | |
| 98 | Other community, social and personal services | Otros servicios comunitarios, sociales o personales | | | |
## Training and evaluation data
### 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
| 0.0354 | 1.0 | 9054 | 0.0362 | 0.7560 | 0.8375 | 0.6963 | 14.0835 | 0.7357 |
| 0.0311 | 2.0 | 18108 | 0.0331 | 0.7756 | 0.8535 | 0.7207 | 12.7880 | 0.7633 |
| 0.0235 | 3.0 | 27162 | 0.0333 | 0.7823 | 0.8705 | 0.7283 | 11.5179 | 0.7811 |
| 0.0157 | 4.0 | 36216 | 0.0348 | 0.7821 | 0.8699 | 0.7274 | 11.5836 | 0.7798 |
| 0.011 | 5.0 | 45270 | 0.0377 | 0.7799 | 0.8787 | 0.7239 | 10.9173 | 0.7841 |
| 0.008 | 6.0 | 54324 | 0.0395 | 0.7854 | 0.8787 | 0.7309 | 10.9042 | 0.7879 |
| 0.0042 | 7.0 | 63378 | 0.0421 | 0.7872 | 0.8823 | 0.7300 | 10.5687 | 0.7903 |
| 0.0025 | 8.0 | 72432 | 0.0439 | 0.7884 | 0.8867 | 0.7305 | 10.2220 | 0.7934 |
| 0.0015 | 9.0 | 81486 | 0.0456 | 0.7889 | 0.8872 | 0.7316 | 10.1781 | 0.7945 |
| 0.001 | 10.0 | 90540 | 0.0465 | 0.7918 | 0.8860 | 0.7376 | 10.2744 | 0.7973 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
### Aknowledgments
This work has been supported by NextProcurement European Action (grant agreement INEA/CEF/ICT/A2020/2373713-Action 2020-ES-IA-0255) and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with Universidad Politécnica de Madrid in the line Support for R&D projects for Beatriz Galindo researchers, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). We also acknowledge the participation of Jennifer Tabita for the preparation of the initial set of notebooks, and the AI4Gov master students from the first cohort for their validation of the approach. Source of the data: Ministerio de Hacienda. |
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} | 0 | 2022-05-16T22:02:56Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -4.65 +/- 21.40
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
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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:
- metrics:
- type: mean_reward
value: 187.84 +/- 76.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
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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:
- metrics:
- type: mean_reward
value: 220.36 +/- 65.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
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} | 0 | null | ---
language: en
datasets:
- wikitext
---
# ByT5 base English fine tuned for OCR Correction
This model is a fine-tuned version of the [byt5-base](https://huggingface.co/google/byt5-base) for OCR Correction. ByT5 was
introduced in [this paper](https://arxiv.org/abs/2105.13626) and the idea and code for fine-tuning the model for OCR Correction was taken from [here](https://blog.ml6.eu/ocr-correction-with-byt5-5994d1217c07).
## Model description
byt5-base-english-ocr-correction is a model that has taken the byt5-base model and fine-tuned it an OCR Correction dataset. The model has been fine-tuned to take an input sentence that has incorrectly transcribed from an OCR model and output a sentence that corrects the errors.
The model was trained by taking the [wikitext dataset](https://huggingface.co/datasets/wikitext) and adding synthetic OCR errors using [nlpaug](https://github.com/makcedward/nlpaug).
## Intended uses & limitations
You can use the model for Text-to-Text Generation to remove errors caused by an OCR model.
### How to use
```python
from transformers import T5ForConditionalGeneration
import torch
import nlpaug.augmenter.char as nac
aug = nac.OcrAug(aug_char_p =0.4, aug_word_p = 0.6)
corrected_text = "Life is like a box of chocolates"
augmented_text = aug.augment(corrected_text)
model = T5ForConditionalGeneration.from_pretrained('yelpfeast/byt5-base-english-ocr-correction')
input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens
labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens
loss = model(input_ids, labels=labels).loss # forward pass
```
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
import nlpaug.augmenter.char as nac
aug = nac.OcrAug(aug_char_p =0.4, aug_word_p = 0.6)
corrected_text = "Life is like a box of chocolates"
augmented_text = aug.augment(corrected_text)
print(augmented_text)
model = T5ForConditionalGeneration.from_pretrained('yelpfeast/byt5-base-english-ocr-correction')
tokenizer = AutoTokenizer.from_pretrained("yelpfeast/byt5-base-english-ocr-correction")
inputs = tokenizer(augmented_text, return_tensors="pt", padding=True)
output_sequences = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
do_sample=False, # disable sampling to test if batching affects output
)
print(tokenizer.batch_decode(output_sequences, skip_special_tokens=True))
```
### Limitations
The model has been trained on text that has been artificially corrupted to look like OCR errors. These errors may not be similar for all OCR models and hence the model may not do a good job at producing fully correct text. |
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} | 0 | null | ---
tags:
- conversational
---
# human conversation part DialoGPT Model |
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} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: pegasus-cnn-dailymail
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. -->
# pegasus-cnn-dailymail
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4497
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5344 | 0.6 | 500 | 1.4497 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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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:
- metrics:
- type: mean_reward
value: -133.36 +/- 43.53
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
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} | 0 | 2022-05-16T23:01:53Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 151.84 +/- 64.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CALM/backup | [
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} | 4 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 198.09 +/- 20.23
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
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} | 85 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1570620381324578816/UG-qT7hg_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Addison Rae</div>
<div style="text-align: center; font-size: 14px;">@whoisaddison</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Addison Rae.
| Data | Addison Rae |
| --- | --- |
| Tweets downloaded | 3204 |
| Retweets | 473 |
| Short tweets | 957 |
| Tweets kept | 1774 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/6p4jofae/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @whoisaddison's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ofab5t2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/whoisaddison')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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}
} | 42 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 23.37 +/- 115.72
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 18 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 230.68 +/- 19.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"has_space"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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}
} | 19,850 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 206.56 +/- 78.20
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CAMeL-Lab/bert-base-arabic-camelbert-da | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
}
} | 449 | null |
<!-- 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. -->
# d-l-dl
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4495
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 42.4143 | 49.8 | 100 | 21.5116 | 1.0 |
| 5.9884 | 99.8 | 200 | 31.7976 | 1.0 |
| 4.0043 | 149.8 | 300 | 3.4829 | 1.0 |
| 3.653 | 199.8 | 400 | 3.6417 | 1.0 |
| 3.5207 | 249.8 | 500 | 3.5081 | 1.0 |
| 3.63 | 299.8 | 600 | 3.4836 | 1.0 |
| 3.648 | 349.8 | 700 | 3.4515 | 1.0 |
| 3.6448 | 399.8 | 800 | 3.4647 | 1.0 |
| 3.6872 | 449.8 | 900 | 3.4371 | 1.0 |
| 3.6892 | 499.8 | 1000 | 3.4337 | 1.0 |
| 3.684 | 549.8 | 1100 | 3.4375 | 1.0 |
| 3.6843 | 599.8 | 1200 | 3.4452 | 1.0 |
| 3.6842 | 649.8 | 1300 | 3.4416 | 1.0 |
| 3.6819 | 699.8 | 1400 | 3.4498 | 1.0 |
| 3.6832 | 749.8 | 1500 | 3.4524 | 1.0 |
| 3.6828 | 799.8 | 1600 | 3.4495 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 34 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 221.54 +/- 19.66
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 1,860 | null | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 31 | 2022-05-17T01:00:40Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20220517-045629
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. -->
# 20220517-045629
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3700
- Wer: 0.4581
- Cer: 0.0854
## 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: 8
- eval_batch_size: 8
- seed: 1339
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 5.238 | 0.29 | 200 | 3.1770 | 1.0 | 1.0 |
| 2.165 | 0.59 | 400 | 0.7309 | 0.7144 | 0.1543 |
| 0.7022 | 0.88 | 600 | 0.4614 | 0.5521 | 0.1058 |
| 0.5114 | 1.17 | 800 | 0.4202 | 0.4998 | 0.0965 |
| 0.4482 | 1.47 | 1000 | 0.3786 | 0.4645 | 0.0877 |
| 0.4082 | 1.76 | 1200 | 0.3700 | 0.4581 | 0.0854 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 62 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: cjjie/bert-finetuned-squad
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. -->
# cjjie/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7784
- 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': 11090, '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 |
|:----------:|:-----:|
| 1.2674 | 0 |
| 0.7784 | 1 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
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} | 132 | null | ---
language: en
license: mit
tags:
- image-classification
datasets: beans
model-index:
- name: my-cool-model-with-card-2
results:
- task:
type: image-classification
dataset:
type: beans
name: Beans
metrics:
- type: acc
value: 0.9
---
# MyModelName
## Model description
This isn't really a model, it's just a test repo to see if the [modelcards](https://github.com/nateraw/modelcards) package works!
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
Provide some evaluation results.
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` |
CAMeL-Lab/bert-base-arabic-camelbert-msa-half | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 16 | null | ---
tags:
- conversational
---
# Kanna DialoGPT Model |
CAUKiel/JavaBERT-uncased | [
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"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9425
- name: F1
type: f1
value: 0.9422011075095515
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2285
- Accuracy: 0.9425
- F1: 0.9422
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4656 | 1.0 | 8000 | 0.2912 | 0.9365 | 0.9362 |
| 0.2046 | 2.0 | 16000 | 0.2285 | 0.9425 | 0.9422 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
CBreit00/DialoGPT_small_Rick | [] | null | {
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} | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
 |
CLAck/indo-mixed | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
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} | 15 | null | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/skgpt-base-kormath
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. -->
# madatnlp/skgpt-base-kormath
This model is a fine-tuned version of [madatnlp/sk-kogptv2-kormath-causal](https://huggingface.co/madatnlp/sk-kogptv2-kormath-causal) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4912
- Validation Loss: 1.1109
- Epoch: 25
## 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': 5.3799995e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0777 | 1.5042 | 0 |
| 1.4350 | 1.1904 | 1 |
| 1.3248 | 1.2871 | 2 |
| 1.1661 | 1.4441 | 3 |
| 1.0920 | 1.0973 | 4 |
| 1.0431 | 1.3860 | 5 |
| 0.9541 | 1.2228 | 6 |
| 0.9315 | 1.0385 | 7 |
| 0.8875 | 1.2156 | 8 |
| 0.8838 | 1.0195 | 9 |
| 0.8029 | 1.1956 | 10 |
| 0.7533 | 1.1139 | 11 |
| 0.7526 | 1.4868 | 12 |
| 0.6986 | 1.1045 | 13 |
| 0.6999 | 1.1083 | 14 |
| 0.6462 | 1.0082 | 15 |
| 0.6325 | 1.0643 | 16 |
| 0.6350 | 1.0729 | 17 |
| 0.6373 | 1.0455 | 18 |
| 0.5922 | 1.2834 | 19 |
| 0.5606 | 1.1031 | 20 |
| 0.5241 | 1.3085 | 21 |
| 0.5394 | 1.2911 | 22 |
| 0.5486 | 1.2407 | 23 |
| 0.5239 | 1.2075 | 24 |
| 0.4912 | 1.1109 | 25 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaZafar/distilgpt2-finetuned-wikitext2
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. -->
# MariaZafar/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0603
- Validation Loss: 5.5023
- Epoch: 49
## 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4903 | 2.4602 | 0 |
| 2.5910 | 2.5912 | 1 |
| 2.1133 | 2.8207 | 2 |
| 1.5857 | 3.1597 | 3 |
| 1.0852 | 3.3317 | 4 |
| 0.6812 | 3.6312 | 5 |
| 0.4490 | 3.8533 | 6 |
| 0.3188 | 4.0209 | 7 |
| 0.2401 | 4.1932 | 8 |
| 0.1987 | 4.3469 | 9 |
| 0.1705 | 4.4238 | 10 |
| 0.1515 | 4.5274 | 11 |
| 0.1329 | 4.5066 | 12 |
| 0.1302 | 4.6625 | 13 |
| 0.1202 | 4.6441 | 14 |
| 0.1133 | 4.7448 | 15 |
| 0.1076 | 4.8144 | 16 |
| 0.1025 | 4.9662 | 17 |
| 0.0976 | 4.7328 | 18 |
| 0.0928 | 4.8394 | 19 |
| 0.0862 | 4.8873 | 20 |
| 0.0824 | 4.9153 | 21 |
| 0.0869 | 5.2097 | 22 |
| 0.0847 | 5.1124 | 23 |
| 0.0824 | 5.0528 | 24 |
| 0.0826 | 5.0547 | 25 |
| 0.0840 | 5.1079 | 26 |
| 0.0846 | 4.9867 | 27 |
| 0.0802 | 4.9700 | 28 |
| 0.0806 | 5.2266 | 29 |
| 0.0827 | 5.0909 | 30 |
| 0.0784 | 5.2329 | 31 |
| 0.0744 | 5.0834 | 32 |
| 0.0712 | 5.3750 | 33 |
| 0.0715 | 5.2754 | 34 |
| 0.0695 | 5.4315 | 35 |
| 0.0703 | 5.4119 | 36 |
| 0.0732 | 5.5824 | 37 |
| 0.0679 | 5.4020 | 38 |
| 0.0627 | 5.7249 | 39 |
| 0.0659 | 5.1686 | 40 |
| 0.0656 | 5.2962 | 41 |
| 0.0642 | 5.3573 | 42 |
| 0.0661 | 5.4822 | 43 |
| 0.0643 | 5.6516 | 44 |
| 0.0612 | 5.6201 | 45 |
| 0.0666 | 5.4791 | 46 |
| 0.0677 | 5.6865 | 47 |
| 0.0628 | 5.4184 | 48 |
| 0.0603 | 5.5023 | 49 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaZafar/distilroberta-base-finetuned-wikitext2
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. -->
# MariaZafar/distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1524
- Validation Loss: 1.8469
- 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1524 | 1.8469 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
],
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} | 2,988 | null | HI,
Nothing here, just an example model to test
https://docs.google.com/document/d/1Tp39nmCQRlZAOZYcOoXV8NCcQDf31GqarPYT3mCv9CM/edit?usp=sharing
|
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
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}
} | 2 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 217.04 +/- 33.19
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
Calamarii/calamari | [] | null | {
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} | 0 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: wav2vec2-xls-r-1b-mixed
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. -->
# wav2vec2-xls-r-1b-mixed
Finetuned https://huggingface.co/facebook/wav2vec2-xls-r-1b on https://github.com/huseinzol05/malaya-speech/tree/master/data/mixed-stt
This model was finetuned on 3 languages,
1. Malay
2. Singlish
3. Mandarin
**This model trained on a single Tesla V100 32GB VRAM, provided by https://keyreply.com/**. |
Camzure/MaamiBot | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: distilbert-base-uncased-fine-tuned-on-emotion-dataset
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-fine-tuned-on-emotion-dataset
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Accuracy Score: 0.9275
- F1 Score: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:--------:|
| 0.8024 | 1.0 | 250 | 0.3089 | 0.906 | 0.9021 |
| 0.2448 | 2.0 | 500 | 0.2138 | 0.9275 | 0.9275 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Canadiancaleb/DialoGPT-small-jesse | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | ---
tags:
- generated_from_trainer
model-index:
- name: es-kd-XLM-minilmv2-32
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-kd-XLM-minilmv2-32
This model is a fine-tuned version of [subhasisj/es-TAPT-MLM-MiniLM](https://huggingface.co/subhasisj/es-TAPT-MLM-MiniLM) 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8657718120805369
---
<!-- 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. -->
# sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3124
- Accuracy: 0.8667
- F1: 0.8658
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
Captain272/lstm | [] | null | {
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} | 0 | null | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 81.6 | 81.6 |
| test | 82.2 | 82.3 |
|
Carlork314/Carlos | [] | null | {
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} | 0 | null | ---
license: mit
---
动漫图片分类模型 <br>
[Github](https://github.com/chinoll/deepdanbooru_onnx) |
dccuchile/albert-tiny-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20220517-150219
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. -->
# 20220517-150219
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2426
- Wer: 0.2344
- Cer: 0.0434
## 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: 8
- seed: 1339
- 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 | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 5.3867 | 0.02 | 200 | 3.2171 | 1.0 | 1.0 |
| 3.1288 | 0.04 | 400 | 2.9394 | 1.0 | 1.0 |
| 1.8298 | 0.06 | 600 | 0.9138 | 0.8416 | 0.2039 |
| 0.9751 | 0.07 | 800 | 0.6568 | 0.6928 | 0.1566 |
| 0.7934 | 0.09 | 1000 | 0.5314 | 0.6225 | 0.1277 |
| 0.663 | 0.11 | 1200 | 0.4759 | 0.5730 | 0.1174 |
| 0.617 | 0.13 | 1400 | 0.4515 | 0.5578 | 0.1118 |
| 0.5473 | 0.15 | 1600 | 0.4017 | 0.5157 | 0.1004 |
| 0.5283 | 0.17 | 1800 | 0.3872 | 0.5094 | 0.0982 |
| 0.4893 | 0.18 | 2000 | 0.3725 | 0.4860 | 0.0932 |
| 0.495 | 0.2 | 2200 | 0.3580 | 0.4542 | 0.0878 |
| 0.4438 | 0.22 | 2400 | 0.3443 | 0.4366 | 0.0858 |
| 0.4425 | 0.24 | 2600 | 0.3428 | 0.4284 | 0.0865 |
| 0.4293 | 0.26 | 2800 | 0.3329 | 0.4221 | 0.0819 |
| 0.3779 | 0.28 | 3000 | 0.3278 | 0.4146 | 0.0794 |
| 0.4116 | 0.29 | 3200 | 0.3242 | 0.4107 | 0.0757 |
| 0.3912 | 0.31 | 3400 | 0.3217 | 0.4040 | 0.0776 |
| 0.391 | 0.33 | 3600 | 0.3127 | 0.3955 | 0.0764 |
| 0.3696 | 0.35 | 3800 | 0.3153 | 0.3892 | 0.0748 |
| 0.3576 | 0.37 | 4000 | 0.3156 | 0.3846 | 0.0737 |
| 0.3553 | 0.39 | 4200 | 0.3024 | 0.3814 | 0.0726 |
| 0.3394 | 0.4 | 4400 | 0.3022 | 0.3637 | 0.0685 |
| 0.3345 | 0.42 | 4600 | 0.3130 | 0.3641 | 0.0698 |
| 0.3357 | 0.44 | 4800 | 0.2913 | 0.3602 | 0.0701 |
| 0.3411 | 0.46 | 5000 | 0.2941 | 0.3514 | 0.0674 |
| 0.3031 | 0.48 | 5200 | 0.3043 | 0.3613 | 0.0685 |
| 0.3305 | 0.5 | 5400 | 0.2967 | 0.3468 | 0.0657 |
| 0.3004 | 0.51 | 5600 | 0.2723 | 0.3309 | 0.0616 |
| 0.31 | 0.53 | 5800 | 0.2835 | 0.3404 | 0.0648 |
| 0.3224 | 0.55 | 6000 | 0.2743 | 0.3358 | 0.0622 |
| 0.3261 | 0.57 | 6200 | 0.2803 | 0.3358 | 0.0620 |
| 0.305 | 0.59 | 6400 | 0.2835 | 0.3397 | 0.0629 |
| 0.3025 | 0.61 | 6600 | 0.2684 | 0.3340 | 0.0639 |
| 0.2952 | 0.62 | 6800 | 0.2654 | 0.3256 | 0.0617 |
| 0.2903 | 0.64 | 7000 | 0.2588 | 0.3174 | 0.0596 |
| 0.2907 | 0.66 | 7200 | 0.2789 | 0.3256 | 0.0623 |
| 0.2887 | 0.68 | 7400 | 0.2634 | 0.3142 | 0.0605 |
| 0.291 | 0.7 | 7600 | 0.2644 | 0.3097 | 0.0582 |
| 0.2646 | 0.72 | 7800 | 0.2753 | 0.3089 | 0.0582 |
| 0.2683 | 0.73 | 8000 | 0.2703 | 0.3036 | 0.0574 |
| 0.2808 | 0.75 | 8200 | 0.2544 | 0.2994 | 0.0561 |
| 0.2724 | 0.77 | 8400 | 0.2584 | 0.3051 | 0.0592 |
| 0.2516 | 0.79 | 8600 | 0.2575 | 0.2959 | 0.0557 |
| 0.2561 | 0.81 | 8800 | 0.2594 | 0.2945 | 0.0552 |
| 0.264 | 0.83 | 9000 | 0.2607 | 0.2987 | 0.0552 |
| 0.2383 | 0.84 | 9200 | 0.2641 | 0.2983 | 0.0546 |
| 0.2548 | 0.86 | 9400 | 0.2714 | 0.2930 | 0.0538 |
| 0.2284 | 0.88 | 9600 | 0.2542 | 0.2945 | 0.0555 |
| 0.2354 | 0.9 | 9800 | 0.2564 | 0.2937 | 0.0551 |
| 0.2624 | 0.92 | 10000 | 0.2466 | 0.2891 | 0.0542 |
| 0.24 | 0.94 | 10200 | 0.2404 | 0.2895 | 0.0528 |
| 0.2372 | 0.95 | 10400 | 0.2590 | 0.2782 | 0.0518 |
| 0.2357 | 0.97 | 10600 | 0.2629 | 0.2867 | 0.0531 |
| 0.2439 | 0.99 | 10800 | 0.2722 | 0.2902 | 0.0556 |
| 0.2204 | 1.01 | 11000 | 0.2618 | 0.2856 | 0.0535 |
| 0.2043 | 1.03 | 11200 | 0.2662 | 0.2789 | 0.0520 |
| 0.2081 | 1.05 | 11400 | 0.2744 | 0.2831 | 0.0532 |
| 0.199 | 1.06 | 11600 | 0.2586 | 0.2800 | 0.0519 |
| 0.2063 | 1.08 | 11800 | 0.2711 | 0.2842 | 0.0531 |
| 0.2116 | 1.1 | 12000 | 0.2463 | 0.2782 | 0.0529 |
| 0.2095 | 1.12 | 12200 | 0.2371 | 0.2757 | 0.0510 |
| 0.1786 | 1.14 | 12400 | 0.2693 | 0.2768 | 0.0520 |
| 0.1999 | 1.16 | 12600 | 0.2625 | 0.2793 | 0.0513 |
| 0.1985 | 1.17 | 12800 | 0.2734 | 0.2796 | 0.0532 |
| 0.187 | 1.19 | 13000 | 0.2654 | 0.2676 | 0.0514 |
| 0.188 | 1.21 | 13200 | 0.2548 | 0.2648 | 0.0489 |
| 0.1853 | 1.23 | 13400 | 0.2684 | 0.2641 | 0.0509 |
| 0.197 | 1.25 | 13600 | 0.2589 | 0.2662 | 0.0507 |
| 0.1873 | 1.27 | 13800 | 0.2633 | 0.2686 | 0.0516 |
| 0.179 | 1.28 | 14000 | 0.2682 | 0.2598 | 0.0508 |
| 0.2008 | 1.3 | 14200 | 0.2505 | 0.2609 | 0.0493 |
| 0.1802 | 1.32 | 14400 | 0.2470 | 0.2598 | 0.0493 |
| 0.1903 | 1.34 | 14600 | 0.2572 | 0.2672 | 0.0500 |
| 0.1852 | 1.36 | 14800 | 0.2576 | 0.2633 | 0.0491 |
| 0.1933 | 1.38 | 15000 | 0.2649 | 0.2602 | 0.0493 |
| 0.191 | 1.4 | 15200 | 0.2578 | 0.2612 | 0.0484 |
| 0.1863 | 1.41 | 15400 | 0.2572 | 0.2566 | 0.0488 |
| 0.1785 | 1.43 | 15600 | 0.2661 | 0.2520 | 0.0478 |
| 0.1755 | 1.45 | 15800 | 0.2637 | 0.2605 | 0.0485 |
| 0.1677 | 1.47 | 16000 | 0.2481 | 0.2559 | 0.0478 |
| 0.1633 | 1.49 | 16200 | 0.2584 | 0.2531 | 0.0476 |
| 0.166 | 1.51 | 16400 | 0.2576 | 0.2595 | 0.0487 |
| 0.1798 | 1.52 | 16600 | 0.2517 | 0.2570 | 0.0488 |
| 0.1879 | 1.54 | 16800 | 0.2555 | 0.2531 | 0.0479 |
| 0.1636 | 1.56 | 17000 | 0.2419 | 0.2467 | 0.0464 |
| 0.1706 | 1.58 | 17200 | 0.2426 | 0.2457 | 0.0463 |
| 0.1763 | 1.6 | 17400 | 0.2427 | 0.2496 | 0.0467 |
| 0.1687 | 1.62 | 17600 | 0.2507 | 0.2496 | 0.0467 |
| 0.1662 | 1.63 | 17800 | 0.2553 | 0.2474 | 0.0466 |
| 0.1637 | 1.65 | 18000 | 0.2576 | 0.2450 | 0.0461 |
| 0.1744 | 1.67 | 18200 | 0.2394 | 0.2414 | 0.0454 |
| 0.1597 | 1.69 | 18400 | 0.2442 | 0.2443 | 0.0452 |
| 0.1606 | 1.71 | 18600 | 0.2488 | 0.2435 | 0.0453 |
| 0.1558 | 1.73 | 18800 | 0.2563 | 0.2464 | 0.0464 |
| 0.172 | 1.74 | 19000 | 0.2501 | 0.2411 | 0.0452 |
| 0.1594 | 1.76 | 19200 | 0.2481 | 0.2460 | 0.0458 |
| 0.1732 | 1.78 | 19400 | 0.2427 | 0.2414 | 0.0443 |
| 0.1706 | 1.8 | 19600 | 0.2367 | 0.2418 | 0.0446 |
| 0.1724 | 1.82 | 19800 | 0.2376 | 0.2390 | 0.0444 |
| 0.1621 | 1.84 | 20000 | 0.2430 | 0.2382 | 0.0438 |
| 0.1501 | 1.85 | 20200 | 0.2445 | 0.2404 | 0.0438 |
| 0.1526 | 1.87 | 20400 | 0.2472 | 0.2361 | 0.0436 |
| 0.1756 | 1.89 | 20600 | 0.2431 | 0.2400 | 0.0437 |
| 0.1598 | 1.91 | 20800 | 0.2472 | 0.2368 | 0.0439 |
| 0.1554 | 1.93 | 21000 | 0.2431 | 0.2347 | 0.0435 |
| 0.1354 | 1.95 | 21200 | 0.2427 | 0.2354 | 0.0438 |
| 0.1587 | 1.96 | 21400 | 0.2427 | 0.2347 | 0.0435 |
| 0.1541 | 1.98 | 21600 | 0.2426 | 0.2344 | 0.0434 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
dccuchile/albert-xlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 26 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 236.77 +/- 42.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
dccuchile/albert-xlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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"AlbertForSequenceClassification"
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} | 24 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 274.34 +/- 16.12
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
dccuchile/albert-xxlarge-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
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"AlbertForSequenceClassification"
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} | 26 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ksabeh/distilbert-base-uncased-mlm-electronics
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. -->
# ksabeh/distilbert-base-uncased-mlm-electronics
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: 2.1782
- Validation Loss: 2.0887
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.3455 | 2.2411 | 0 |
| 2.2561 | 2.1496 | 1 |
| 2.1782 | 2.0887 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.3
- Datasets 2.1.0
- Tokenizers 0.12.1
|
dccuchile/albert-xxlarge-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 26 | null | ---
license: apache-2.0
tags:
- multilingual model
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-multilingual-xlsum-new
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. -->
# mt5-small-finetuned-multilingual-xlsum-new
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the 45 languages of the XL-Sum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7679
- Rouge1: 9.1993
- Rouge2: 2.3416
- Rougel: 7.6684
- Rougelsum: 7.7074
## 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: 5.6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.9684 | 1.0 | 1687 | 2.8902 | 8.0531 | 1.8357 | 6.7234 | 6.7401 |
| 3.62 | 2.0 | 3374 | 2.8486 | 8.4881 | 2.0178 | 7.0542 | 7.0854 |
| 3.3765 | 3.0 | 5061 | 2.7986 | 8.7796 | 2.2342 | 7.3363 | 7.3645 |
| 3.5043 | 4.0 | 6748 | 2.7677 | 9.0486 | 2.3099 | 7.5493 | 7.5685 |
| 3.338 | 5.0 | 8435 | 2.7679 | 9.1993 | 2.3416 | 7.6684 | 7.7074 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 7 | null | ---
license: mit
---
This Repository includes the files required to run the `BioAssays Semantification` ORKG-NLP service.
Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service.
The [Scikit-Learn](https://scikit-learn.org/stable/) models are converted using [skl2onnx](https://github.com/onnx/sklearn-onnx) and may not include all original scikit-learn functionalities. |
dccuchile/albert-xxlarge-spanish-finetuned-xnli | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 68 | null | ---
license: apache-2.0
---
Fine-tuned T5 base model for use as a frame semantic parser in the [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer) project. This model is trained on data from [FrameNet 1.7](https://framenet2.icsi.berkeley.edu/).
### Usage
This is meant to be used a part of [Frame Semantic Transformer](https://github.com/chanind/frame-semantic-transformer). See that project for usage instructions.
### Tasks
This model is trained to perform 3 tasks related to semantic frame parsing:
1. Identify frame trigger locations in the text
2. Classify the frame given a trigger location
3. Extract frame elements in the sentence
### Performance
This model is trained and evaluated using the same train/dev/test splits from FrameNet 1.7 annotated corpora as used by [Open Sesame](https://github.com/swabhs/open-sesame).
| Task | F1 Score (Dev) | F1 Score (Test) |
| ---------------------- | -------------- | --------------- |
| Trigger identification | 0.78 | 0.74 |
| Frame Classification | 0.91 | 0.89 |
| Argument Extraction | 0.78 | 0.75 |
|
dccuchile/albert-tiny-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
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} | 393 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.861372046683746
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1390
- F1: 0.8614
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2617 | 1.0 | 525 | 0.1550 | 0.8199 |
| 0.1271 | 2.0 | 1050 | 0.1389 | 0.8470 |
| 0.0802 | 3.0 | 1575 | 0.1390 | 0.8614 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
dccuchile/albert-xxlarge-spanish | [
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null | {
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} | 42 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: opt-125m
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. -->
# opt-125m
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- TensorFlow 2.9.1
- Datasets 2.2.2
- Tokenizers 0.12.1
|
dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 27 | null | TODO: This is still a demo model, the file does not match with the model card!!!
# poetry-generation-firstline-mbart-ws-fi-sorted
* `nextline`: generates the first poem line from keywords
* `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
* `ws`: trained on Wikisource data
* `en`: English language
* `sorted`: the order of input keywords matter when generating candidates |
dccuchile/distilbert-base-spanish-uncased-finetuned-pos | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
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"DistilBertForTokenClassification"
],
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-ar-7
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-xls-r-300m-ar-7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 61.6652
- Wer: 0.2222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- 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_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6306.7719 | 4.71 | 400 | 617.7255 | 1.0 |
| 1222.8073 | 9.41 | 800 | 81.7446 | 0.3820 |
| 326.9842 | 14.12 | 1200 | 67.3986 | 0.2859 |
| 223.859 | 18.82 | 1600 | 60.8896 | 0.2492 |
| 175.5662 | 23.53 | 2000 | 59.2339 | 0.2256 |
| 146.3602 | 28.24 | 2400 | 61.6652 | 0.2222 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
CennetOguz/distilbert-base-uncased-finetuned-recipe | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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} | 2 | 2022-05-17T15:02:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: MlpPolicyPPO
results:
- metrics:
- type: mean_reward
value: 272.58 +/- 18.37
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **MlpPolicyPPO** Agent playing **LunarLander-v2**
This is a trained model of a **MlpPolicyPPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Chaddmckay/Cdm | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-chinese-taiwan-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-chinese-taiwan-colab
!!!this model has just been trained with very high learning rate and small epochs, please do not use this to do the speech to text.
!!!It's just a test, I'll retrain this model with more time later when I have time.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ChaitanyaU/FineTuneLM | [] | null | {
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} | 0 | null | ---
language:
- hy
- hye
- multilingual
license: apache-2.0
tags:
- automatic-speech-recognition
- hy
- mozilla-foundation/common_voice_9_0
- google/fleurs
datasets:
- mozilla-foundation/common_voice_9_0
- google/fleurs
- mc4
models:
- facebook/wav2vec2-xls-r-2b
task_categories:
- automatic-speech-recognition
- speech-processing
task_ids:
- speech-recognition
---
# Automatic SPeech Recognition for ArMenian
TODO Model details |
Chan/distilgpt2-finetuned-wikitext2 | [] | null | {
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} | 0 | 2022-05-17T16:19:02Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-noisy-pretrain-fine-tuned
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-german-cased-noisy-pretrain-fine-tuned
This model is a fine-tuned version of [tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData](https://huggingface.co/tbosse/bert-base-german-cased-finetuned-subj_preTrained_with_noisyData) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2925
- Precision: 0.7933
- Recall: 0.7457
- F1: 0.7688
- Accuracy: 0.9147
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 33 | 0.3093 | 0.7456 | 0.6029 | 0.6667 | 0.8808 |
| No log | 2.0 | 66 | 0.2587 | 0.7774 | 0.7286 | 0.7522 | 0.9078 |
| No log | 3.0 | 99 | 0.2529 | 0.7775 | 0.7686 | 0.7730 | 0.9136 |
| No log | 4.0 | 132 | 0.2598 | 0.8063 | 0.7257 | 0.7639 | 0.9147 |
| No log | 5.0 | 165 | 0.2783 | 0.7927 | 0.7429 | 0.7670 | 0.9159 |
| No log | 6.0 | 198 | 0.2899 | 0.8019 | 0.74 | 0.7697 | 0.9165 |
| No log | 7.0 | 231 | 0.2925 | 0.7933 | 0.7457 | 0.7688 | 0.9147 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CharlieChen/feedback-bigbird | [] | null | {
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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:
- metrics:
- type: mean_reward
value: 244.64 +/- 30.60
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
Cheatham/xlm-roberta-large-finetuned-d1 | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 20 | 2022-05-17T17:19:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- inspec
metrics:
- f1
- precision
- recall
model-index:
- name: bert-finetuned-inspec-3-epochs
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: inspec
type: inspec
args: extraction
metrics:
- name: F1
type: f1
value: 0.28328008519701814
- name: Precision
type: precision
value: 0.26594090202177295
- name: Recall
type: recall
value: 0.3030379746835443
---
<!-- 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-inspec-3-epochs
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the inspec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2728
- F1: 0.2833
- Precision: 0.2659
- Recall: 0.3030
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
| 0.3338 | 1.0 | 125 | 0.2837 | 0.1401 | 0.1510 | 0.1306 |
| 0.2575 | 2.0 | 250 | 0.2658 | 0.2183 | 0.2519 | 0.1927 |
| 0.2259 | 3.0 | 375 | 0.2728 | 0.2833 | 0.2659 | 0.3030 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper | [
"ko",
"gpt2",
"license:cc-by-nc-sa-4.0"
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} | 0 | 2022-05-17T17:55:17Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410721079383969795/28HNul1J_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/936390568946651136/mFZ9oOfR_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1221967496640704512/3lOox3Kt_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">PT Brasil & Gregorio Duvivier & Guilherme Boulos</div>
<div style="text-align: center; font-size: 14px;">@gduvivier-guilhermeboulos-ptbrasil</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from PT Brasil & Gregorio Duvivier & Guilherme Boulos.
| Data | PT Brasil | Gregorio Duvivier | Guilherme Boulos |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 3223 | 3248 |
| Retweets | 535 | 1358 | 657 |
| Short tweets | 116 | 450 | 122 |
| Tweets kept | 2599 | 1415 | 2469 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1dcswedc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gduvivier-guilhermeboulos-ptbrasil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/202hdnnd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/202hdnnd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gduvivier-guilhermeboulos-ptbrasil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Chester/traffic-rec | [] | null | {
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} | 0 | 2022-05-17T18:09:35Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 274.17 +/- 16.14
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
Chinat/test-classifier | [] | null | {
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} | 0 | null | ---
library_name: stable-baselines3
tags:
- BipedalWalker-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 80.95 +/- 9.81
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalker-v3
type: BipedalWalker-v3
---
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
ChrisVCB/DialoGPT-medium-cmjs | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 7 | 2022-05-17T18:45:55Z | ---
language: en
thumbnail: http://www.huggingtweets.com/lulaoficial-ptbrasil/1652813188143/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410721079383969795/28HNul1J_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1518543225933512705/T4r0T3SE_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">PT Brasil & Lula</div>
<div style="text-align: center; font-size: 14px;">@lulaoficial-ptbrasil</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from PT Brasil & Lula.
| Data | PT Brasil | Lula |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3247 |
| Retweets | 535 | 705 |
| Short tweets | 116 | 191 |
| Tweets kept | 2599 | 2351 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n5vn7b0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lulaoficial-ptbrasil's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1dh0f8u4) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1dh0f8u4/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lulaoficial-ptbrasil')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-medium-commands
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. -->
# gpt2-medium-commands
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CleveGreen/FieldClassifier_v2_gpt | [
"pytorch",
"gpt2",
"text-classification",
"transformers"
] | text-classification | {
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} | 26 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.04 +/- 16.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **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
|
ClydeWasTaken/DialoGPT-small-joshua | [
"conversational"
] | conversational | {
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} | 0 | null | ---
language:
- en
tags:
- codegen
- text generation
- pytorch
- causal-lm
license: bsd-3-clause
---
# Salesforce CodeGen
ported salesforce codegen models to work on huggingface transformers without any extra code (the model specific code is bundled)
## Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.
The abstract from the paper is the following: Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We plan to make the training library JaxFormer including checkpoints available as open source.
## Usage
`trust_remote_code` is needed because the [torch modules](https://github.com/salesforce/CodeGen/tree/main/jaxformer/hf/codegen) for the custom codegen model is bundled.
```sh
from transformers import AutoModelForCausalLM, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained(model_folder, local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(model_folder, local_files_only=True, trust_remote_code=True)
``` |
CodeMonkey98/distilroberta-base-finetuned-wikitext2 | [] | null | {
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} | 0 | null | ---
tags:
- conversational
---
# Rick and Morty DialoGPT Model |
CoderBoy432/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 11 | null | ---
language:
- en
tags:
- text-classification
- emotion
- pytorch
license: mit
datasets:
- emotion
metrics:
- accuracy
- precision
- recall
- f1
---
# bert-base-uncased-emotion
## Model description
`bert-base-uncased` finetuned on the unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets) [~250K texts with 7 labels -- neutral, happy, sad, anger, disgust, surprise, fear], then transferred to
a small sample of 10K hand-tagged StockTwits messages. Optimized for extracting emotions from financial social media, such as StockTwits.
Sequence length 64, learning rate 2e-5, batch size 128, 8 epochs.
For more details, please visit https://github.com/dvamossy/EmTract.
## Training data
Data came from https://github.com/sarnthil/unify-emotion-datasets.
|
Contrastive-Tension/BERT-Distil-CT | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 9 | null | ---
tags:
- PyTorch
- GAN
---
StyleGAN model, tuned on cats and dogs.
Author: [@MLArt](https://t.me/MLArt)
[Colab](https://colab.research.google.com/github/tg-bomze/collection-of-notebooks/blob/master/PetBreeder.ipynb) |
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 | # VITS-Osman
Originally from https://github.com/jaywalnut310/vits, https://arxiv.org/abs/2106.06103 trained on https://github.com/huseinzol05/malaya-speech/tree/master/data/azure-tts
**This model trained on a single Tesla V100 32GB VRAM, provided by https://keyreply.com/**.
## Preparation scripts
All scripts and notebooks can get at https://github.com/malaysia-ai/projects/tree/master/malaysia_ai_projects/malay_vits |
CouchCat/ma_sa_v7_distil | [
"pytorch",
"distilbert",
"text-classification",
"en",
"transformers",
"sentiment-analysis",
"license:mit"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
} | 38 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: MariaZafar/gpt2-finetuned-wikitext2
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. -->
# MariaZafar/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7785
- Validation Loss: 3.7004
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.8858 | 7.5655 | 0 |
| 4.0619 | 5.8193 | 1 |
| 3.3766 | 4.9585 | 2 |
| 3.0686 | 4.5764 | 3 |
| 2.9022 | 4.3847 | 4 |
| 2.7838 | 4.2249 | 5 |
| 2.6997 | 4.1060 | 6 |
| 2.6154 | 4.0100 | 7 |
| 2.5575 | 3.9412 | 8 |
| 2.4933 | 3.8447 | 9 |
| 2.4397 | 3.7619 | 10 |
| 2.3835 | 3.7510 | 11 |
| 2.3403 | 3.6810 | 12 |
| 2.2924 | 3.6716 | 13 |
| 2.2513 | 3.6335 | 14 |
| 2.2031 | 3.6208 | 15 |
| 2.1619 | 3.5915 | 16 |
| 2.1234 | 3.5497 | 17 |
| 2.0792 | 3.5540 | 18 |
| 2.0398 | 3.5461 | 19 |
| 1.9976 | 3.5282 | 20 |
| 1.9577 | 3.5260 | 21 |
| 1.9176 | 3.5041 | 22 |
| 1.8745 | 3.4994 | 23 |
| 1.8304 | 3.5250 | 24 |
| 1.7881 | 3.4864 | 25 |
| 1.7423 | 3.4718 | 26 |
| 1.6993 | 3.5194 | 27 |
| 1.6503 | 3.5019 | 28 |
| 1.6025 | 3.5055 | 29 |
| 1.5500 | 3.5109 | 30 |
| 1.4964 | 3.5389 | 31 |
| 1.4448 | 3.5393 | 32 |
| 1.3954 | 3.5363 | 33 |
| 1.3464 | 3.5446 | 34 |
| 1.2978 | 3.5117 | 35 |
| 1.2494 | 3.5225 | 36 |
| 1.2004 | 3.5443 | 37 |
| 1.1534 | 3.5909 | 38 |
| 1.1124 | 3.5380 | 39 |
| 1.0709 | 3.6162 | 40 |
| 1.0265 | 3.6758 | 41 |
| 0.9936 | 3.6168 | 42 |
| 0.9590 | 3.6243 | 43 |
| 0.9238 | 3.6308 | 44 |
| 0.8886 | 3.6429 | 45 |
| 0.8635 | 3.7137 | 46 |
| 0.8352 | 3.6512 | 47 |
| 0.8050 | 3.7033 | 48 |
| 0.7785 | 3.7004 | 49 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.0
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CoveJH/ConBot | [] | null | {
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}
} | 0 | null | # VITS-Yasmin
Originally from https://github.com/jaywalnut310/vits, https://arxiv.org/abs/2106.06103 trained on https://github.com/huseinzol05/malaya-speech/tree/master/data/azure-tts
**This model trained on a single Tesla V100 32GB VRAM, provided by https://keyreply.com/**.
## Preparation scripts
All scripts and notebooks can get at https://github.com/malaysia-ai/projects/tree/master/malaysia_ai_projects/malay_vits |
Culmenus/checkpoint-168500-finetuned-de-to-is_nr2 | [] | null | {
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} | 0 | null | BUY HOUSES ANYWHERE IN TUCSON ARIZONA
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