modelId
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81
| tags
sequence | pipeline_tag
stringclasses 17
values | config
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int64 0
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| first_commit
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AnonymousSub/AR_cline | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: classificationEsp1
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. -->
# classificationEsp1
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3864, '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
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/AR_rule_based_roberta_bert_triplet_epochs_1_shard_10 | [
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"roberta",
"feature-extraction",
"transformers"
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} | 10 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xlsum-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
args: english
metrics:
- name: Rouge1
type: rouge
value: 23.7508
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xlsum-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6629
- Rouge1: 23.7508
- Rouge2: 5.5427
- Rougel: 18.6777
- Rougelsum: 18.652
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.0789 | 1.0 | 1010 | 2.6881 | 22.6824 | 4.4735 | 17.6707 | 17.5485 |
| 2.9223 | 2.0 | 2020 | 2.6629 | 23.7508 | 5.5427 | 18.6777 | 18.652 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_bert_triplet_epochs_1_shard_1 | [
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-3
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. -->
# my-gpt-model-3
This model is a fine-tuned version of [bigmorning/my-gpt-model](https://huggingface.co/bigmorning/my-gpt-model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.1163
- 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 | Epoch |
|:----------:|:-----:|
| 5.1163 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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}
} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4548
- Wer: 0.3373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3291 | 4.0 | 500 | 1.0403 | 0.7174 |
| 0.5336 | 8.0 | 1000 | 0.4744 | 0.4489 |
| 0.2155 | 12.0 | 1500 | 0.4476 | 0.3832 |
| 0.1256 | 16.0 | 2000 | 0.4358 | 0.3639 |
| 0.0867 | 20.0 | 2500 | 0.4634 | 0.3527 |
| 0.0608 | 24.0 | 3000 | 0.4784 | 0.3466 |
| 0.0476 | 28.0 | 3500 | 0.4548 | 0.3373 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- sumedh/autotrain-data-MeQSum-1
co2_eq_emissions: 35.865521343923916
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 660519466
- CO2 Emissions (in grams): 35.865521343923916
## Validation Metrics
- Loss: 1.3210543394088745
- Rouge1: 52.1593
- Rouge2: 34.5464
- RougeL: 50.1141
- RougeLsum: 50.1067
- Gen Len: 11.93
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/sumedh/autonlp-MeQSum-1-660519466
``` |
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 8 | 2022-03-23T10:05:53Z | ---
language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/kptimes
metrics:
- seqeval
widget:
- text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document.
Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading
it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail
and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents,
this process can take a lot of time.
Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical
and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture
the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency,
occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies
and context of words in a text."
example_title: "Example 1"
- text: "FoodEx is the largest trade exhibition for food and drinks in Asia, with about 70,000 visitors checking out the products presented by hundreds of participating companies. I was lucky to enter as press; otherwise, visitors must be affiliated with the food industry— and pay ¥5,000 — to enter. The FoodEx menu is global, including everything from cherry beer from Germany and premium Mexican tequila to top-class French and Chinese dumplings. The event was a rare chance to try out both well-known and exotic foods and even see professionals making them. In addition to booths offering traditional Japanese favorites such as udon and maguro sashimi, there were plenty of innovative twists, such as dorayaki , a sweet snack made of two pancakes and a red-bean filling, that came in coffee and tomato flavors. While I was there I was lucky to catch the World Sushi Cup Japan 2013, where top chefs from around the world were competing … and presenting a wide range of styles that you would not normally see in Japan, like the flower makizushi above."
example_title: "Example 2"
model-index:
- name: DeDeckerThomas/keyphrase-extraction-distilbert-kptimes
results:
- task:
type: keyphrase-extraction
name: Keyphrase Extraction
dataset:
type: midas/kptimes
name: kptimes
metrics:
- type: F1 (Seqeval)
value: 0.539
name: F1 (Seqeval)
- type: F1@M
value: 0.328
name: F1@M
---
# 🔑 Keyphrase Extraction Model: distilbert-kptimes
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳.
Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text.
## 📓 Model Description
This model uses [KBIR](https://huggingface.co/distilbert-base-uncased) as its base model and fine-tunes it on the [KPTimes dataset](https://huggingface.co/datasets/midas/kptimes).
Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word in the document is classified as being part of a keyphrase or not.
| Label | Description |
| ----- | ------------------------------- |
| B-KEY | At the beginning of a keyphrase |
| I-KEY | Inside a keyphrase |
| O | Outside a keyphrase |
## ✋ Intended Uses & Limitations
### 🛑 Limitations
* This keyphrase extraction model is very domain-specific and will perform very well on news articles from NY Times. It's not recommended to use this model for other domains, but you are free to test it out.
* Limited amount of predicted keyphrases.
* Only works for English documents.
### ❓ How To Use
```python
from transformers import (
TokenClassificationPipeline,
AutoModelForTokenClassification,
AutoTokenizer,
)
from transformers.pipelines import AggregationStrategy
import numpy as np
# Define keyphrase extraction pipeline
class KeyphraseExtractionPipeline(TokenClassificationPipeline):
def __init__(self, model, *args, **kwargs):
super().__init__(
model=AutoModelForTokenClassification.from_pretrained(model),
tokenizer=AutoTokenizer.from_pretrained(model),
*args,
**kwargs
)
def postprocess(self, all_outputs):
results = super().postprocess(
all_outputs=all_outputs,
aggregation_strategy=AggregationStrategy.FIRST,
)
return np.unique([result.get("word").strip() for result in results])
```
```python
# Load pipeline
model_name = "ml6team/keyphrase-extraction-distilbert-kptimes"
extractor = KeyphraseExtractionPipeline(model=model_name)
```
```python
# Inference
text = """
Keyphrase extraction is a technique in text analysis where you extract the
important keyphrases from a document. Thanks to these keyphrases humans can
understand the content of a text very quickly and easily without reading it
completely. Keyphrase extraction was first done primarily by human annotators,
who read the text in detail and then wrote down the most important keyphrases.
The disadvantage is that if you work with a lot of documents, this process
can take a lot of time.
Here is where Artificial Intelligence comes in. Currently, classical machine
learning methods, that use statistical and linguistic features, are widely used
for the extraction process. Now with deep learning, it is possible to capture
the semantic meaning of a text even better than these classical methods.
Classical methods look at the frequency, occurrence and order of words
in the text, whereas these neural approaches can capture long-term
semantic dependencies and context of words in a text.
""".replace("\n", " ")
keyphrases = extractor(text)
print(keyphrases)
```
```
# Output
['artificial intelligence']
```
## 📚 Training Dataset
[KPTimes](https://huggingface.co/datasets/midas/kptimes) is a keyphrase extraction/generation dataset consisting of 279,923 news articles from NY Times and 10K from JPTimes and annotated by professional indexers or editors.
You can find more information in the [paper](https://arxiv.org/abs/1911.12559).
## 👷♂️ Training procedure
### Training parameters
| Parameter | Value |
| --------- | ------|
| Learning Rate | 1e-4 |
| Epochs | 50 |
| Early Stopping Patience | 3 |
### Preprocessing
The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens.
```python
from datasets import load_dataset
from transformers import AutoTokenizer
# Labels
label_list = ["B", "I", "O"]
lbl2idx = {"B": 0, "I": 1, "O": 2}
idx2label = {0: "B", 1: "I", 2: "O"}
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
max_length = 512
# Dataset parameters
dataset_full_name = "midas/kptimes"
dataset_subset = "raw"
dataset_document_column = "document"
dataset_biotags_column = "doc_bio_tags"
def preprocess_fuction(all_samples_per_split):
tokenized_samples = tokenizer.batch_encode_plus(
all_samples_per_split[dataset_document_column],
padding="max_length",
truncation=True,
is_split_into_words=True,
max_length=max_length,
)
total_adjusted_labels = []
for k in range(0, len(tokenized_samples["input_ids"])):
prev_wid = -1
word_ids_list = tokenized_samples.word_ids(batch_index=k)
existing_label_ids = all_samples_per_split[dataset_biotags_column][k]
i = -1
adjusted_label_ids = []
for wid in word_ids_list:
if wid is None:
adjusted_label_ids.append(lbl2idx["O"])
elif wid != prev_wid:
i = i + 1
adjusted_label_ids.append(lbl2idx[existing_label_ids[i]])
prev_wid = wid
else:
adjusted_label_ids.append(
lbl2idx[
f"{'I' if existing_label_ids[i] == 'B' else existing_label_ids[i]}"
]
)
total_adjusted_labels.append(adjusted_label_ids)
tokenized_samples["labels"] = total_adjusted_labels
return tokenized_samples
# Load dataset
dataset = load_dataset(dataset_full_name, dataset_subset)
# Preprocess dataset
tokenized_dataset = dataset.map(preprocess_fuction, batched=True)
```
### Postprocessing (Without Pipeline Function)
If you do not use the pipeline function, you must filter out the B and I labeled tokens. Each B and I will then be merged into a keyphrase. Finally, you need to strip the keyphrases to make sure all unnecessary spaces have been removed.
```python
# Define post_process functions
def concat_tokens_by_tag(keyphrases):
keyphrase_tokens = []
for id, label in keyphrases:
if label == "B":
keyphrase_tokens.append([id])
elif label == "I":
if len(keyphrase_tokens) > 0:
keyphrase_tokens[len(keyphrase_tokens) - 1].append(id)
return keyphrase_tokens
def extract_keyphrases(example, predictions, tokenizer, index=0):
keyphrases_list = [
(id, idx2label[label])
for id, label in zip(
np.array(example["input_ids"]).squeeze().tolist(), predictions[index]
)
if idx2label[label] in ["B", "I"]
]
processed_keyphrases = concat_tokens_by_tag(keyphrases_list)
extracted_kps = tokenizer.batch_decode(
processed_keyphrases,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
return np.unique([kp.strip() for kp in extracted_kps])
```
## 📝 Evaluation Results
Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases.
The model achieves the following results on the KPTimes test set:
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
| KPTimes Test Set | 0.19 | 0.36 | 0.23 | 0.10 | 0.37 | 0.15 | 0.35 | 0.37 | 0.33 |
## 🚨 Issues
Please feel free to start discussions in the Community Tab. |
AnonymousSub/SciFive_pubmedqa_question_generation | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
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"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
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"prefix": "translate English to French: "
},
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"prefix": "translate English to Romanian: "
}
}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: roberta-base-squad2
results: []
---
# Graphcore/roberta-base-squad2
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
## Model description
RoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained.
It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.
As a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.
Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf)
## Intended uses & limitations
This model is a fine-tuned version of [HuggingFace/roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
## Training and evaluation data
Trained and evaluated on the SQuAD v2 dataset:
- [HuggingFace/squad_v2](https://huggingface.co/datasets/squad_v2).
## Training procedure
Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore).
Command line:
```
python examples/question-answering/run_qa.py \
--ipu_config_name Graphcore/roberta-base-ipu \
--model_name_or_path roberta-base \
--dataset_name squad_v2 \
--version_2_with_negative \
--do_train \
--do_eval \
--num_train_epochs 3 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 2 \
--pod_type pod16 \
--learning_rate 7e-5 \
--max_seq_length 384 \
--doc_stride 128 \
--seed 1984 \
--lr_scheduler_type linear \
--loss_scaling 64 \
--weight_decay 0.01 \
--warmup_ratio 0.2 \
--logging_steps 1 \
--save_steps -1 \
--dataloader_num_workers 64 \
--output_dir roberta-base-squad2 \
--overwrite_output_dir \
--push_to_hub
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 1984
- distributed_type: IPU
- total_train_batch_size: 256
- total_eval_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 3.0
- training precision: Mixed Precision
### Training results
```
***** train metrics *****
epoch = 3.0
train_loss = 0.9982
train_runtime = 0:04:44.21
train_samples = 131823
train_samples_per_second = 1391.43
train_steps_per_second = 5.425
***** eval metrics *****
epoch = 3.0
eval_HasAns_exact = 78.1208
eval_HasAns_f1 = 84.6569
eval_HasAns_total = 5928
eval_NoAns_exact = 82.0353
eval_NoAns_f1 = 82.0353
eval_NoAns_total = 5945
eval_best_exact = 80.0809
eval_best_exact_thresh = 0.0
eval_best_f1 = 83.3442
eval_best_f1_thresh = 0.0
eval_exact = 80.0809
eval_f1 = 83.3442
eval_samples = 12165
eval_total = 11873
```
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/T5_pubmedqa_question_generation | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
],
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"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
language: fr
license: mit
tags:
- bert
- language-model
- flaubert
- french
- flaubert-base
- uncased
- asr
- speech
- oral
- natural language understanding
- NLU
- spoken language understanding
- SLU
- understanding
---
# FlauBERT-Oral models: Using ASR-Generated Text for Spoken Language Modeling
**FlauBERT-Oral** are French BERT models trained on a very large amount of automatically transcribed speech from 350,000 hours of diverse French TV shows. They were trained with the [**FlauBERT software**](https://github.com/getalp/Flaubert) using the same parameters as the [flaubert-base-uncased](https://huggingface.co/flaubert/flaubert_base_uncased) model (12 layers, 12 attention heads, 768 dims, 137M parameters, uncased).
## Available FlauBERT-Oral models
- `flaubert-oral-asr` : trained from scratch on ASR data, keeping the BPE tokenizer and vocabulary of flaubert-base-uncased
- `flaubert-oral-asr_nb` : trained from scratch on ASR data, BPE tokenizer is also trained on the same corpus
- `flaubert-oral-mixed` : trained from scratch on a mixed corpus of ASR and text data, BPE tokenizer is also trained on the same corpus
- `flaubert-oral-ft` : fine-tuning of flaubert-base-uncased for a few epochs on ASR data
## Usage for sequence classification
```python
flaubert_tokenizer = FlaubertTokenizer.from_pretrained("nherve/flaubert-oral-asr")
flaubert_classif = FlaubertForSequenceClassification.from_pretrained("nherve/flaubert-oral-asr", num_labels=14)
flaubert_classif.sequence_summary.summary_type = 'mean'
# Then, train your model
```
## References
If you use FlauBERT-Oral models for your scientific publication, or if you find the resources in this repository useful, please cite the following papers:
```
@InProceedings{herve2022flaubertoral,
author = {Herv\'{e}, Nicolas and Pelloin, Valentin and Favre, Benoit and Dary, Franck and Laurent, Antoine and Meignier, Sylvain and Besacier, Laurent},
title = {Using ASR-Generated Text for Spoken Language Modeling},
booktitle = {Proceedings of "Challenges & Perspectives in Creating Large Language Models" ACL 2022 Workshop},
month = {May},
year = {2022}
}
```
|
AnonymousSub/cline-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 31 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Zarkit/classificationEsp2
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. -->
# Zarkit/classificationEsp2
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1649
- Validation Loss: 0.7498
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8979, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.6010 | 0.5679 | 0 |
| 0.4173 | 0.5552 | 1 |
| 0.1649 | 0.7498 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/declutr-emanuals-techqa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
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} | 4 | 2022-03-23T15:42:00Z | This is a control model. Converted directly from the original TF dataset format.
````
gsutil cp -R gs://t5-data/pretrained_models/small/ .
wget https://huggingface.co/t5-small/raw/main/config.json
python3 convert_t5_original_tf_checkpoint_to_pytorch.py --tf_checkpoint_path "dump/small/" --config_file "config.json" --pytorch_dump_path "/home/perk/dirconv"
``` |
AnonymousSub/declutr-roberta-papers | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 4 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Roberta
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16476, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/dummy_2 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 39 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rocketknight1/temp-colab-upload-test
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. -->
# Rocketknight1/temp-colab-upload-test
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5386
- Validation Loss: 0.0000
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, '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 |
|:----------:|:---------------:|:-----:|
| 0.5386 | 0.0000 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"BertModel"
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rocketknight1/temp-colab-upload-test2
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. -->
# Rocketknight1/temp-colab-upload-test2
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6931
- Validation Loss: 0.6931
- 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': 'Adam', 'learning_rate': 0.001, '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 |
|:----------:|:---------------:|:-----:|
| 0.6931 | 0.6931 | 0 |
| 0.6931 | 0.6931 | 1 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bert-base-multilingual-cased-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. -->
# bert-base-multilingual-cased-squad
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5271
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18600, '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: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.1256 | 0 |
| 0.7252 | 1 |
| 0.5271 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_hier_quadruplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 4 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/rickyflows/1648058984275/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/1385231541278855171/lgH-Kso5_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">∞ ricky flowstate ∞</div>
<div style="text-align: center; font-size: 14px;">@rickyflows</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 ∞ ricky flowstate ∞.
| Data | ∞ ricky flowstate ∞ |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 86 |
| Short tweets | 506 |
| Tweets kept | 2657 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/gn0lyrdk/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 @rickyflows's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2fkt1gts) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2fkt1gts/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/rickyflows')
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)
|
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 8 | null | ---
language: "de"
tags:
- hate-speech-classification
widget:
- text: "Als jemand, der im real existierenden Sozialismus aufgewachsen ist, kann ich über George Weineberg nur sagen, dass er ein Voll...t ist. Finde es schon gut, dass der eingeladen wurde. Hat gezeigt, dass er viel Meinung hat, aber offensichtlich wenig Ahnung. Er hat sich eben so gut wie er kann, für alle sichtbar, zum Trottel gemacht"
- text: "Sobald klar ist dass Trump die Wahl gewinnt liegen alle Deutschen Framing Journalisten im Sauerstoffzelt. Wegen extremer Schnappatmung. Das ist zwar hart, aber Fair!"
---
# Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/gbert-germeval-2021")
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/gbert-germeval-2021")
```
# Dataset
```bibtext
@proceedings{germeval-2021-germeval,
title = "Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments",
editor = "Risch, Julian and
Stoll, Anke and
Wilms, Lena and
Wiegand, Michael",
month = sep,
year = "2021",
address = "Duesseldorf, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.germeval-1.0",
}
```
---
license: mit
---
|
AnonymousSub/rule_based_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 2 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2_ONION_prefinetune_4.0
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_ONION_prefinetune_4.0
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: 4.6484
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 153 | 4.7368 |
| No log | 2.0 | 306 | 4.6732 |
| No log | 3.0 | 459 | 4.6527 |
| 4.8529 | 4.0 | 612 | 4.6484 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 6 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/eigenrobot-moridinamael/1648060937936/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/615582548010229761/0zg9awKn_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/1492994204758278144/rDnqNReU_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">Twisted Mentat Matt & eigenrobot</div>
<div style="text-align: center; font-size: 14px;">@eigenrobot-moridinamael</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 Twisted Mentat Matt & eigenrobot.
| Data | Twisted Mentat Matt | eigenrobot |
| --- | --- | --- |
| Tweets downloaded | 3145 | 3247 |
| Retweets | 1670 | 119 |
| Short tweets | 230 | 651 |
| Tweets kept | 1245 | 2477 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3njfftkj/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 @eigenrobot-moridinamael's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1nbxxa8l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1nbxxa8l/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/eigenrobot-moridinamael')
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)
|
AnonymousSub/rule_based_only_classfn_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 32 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/ryiacy/1648065062687/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/1424813722011410434/73S-oYNT_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">cyriac</div>
<div style="text-align: center; font-size: 14px;">@ryiacy</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 cyriac.
| Data | cyriac |
| --- | --- |
| Tweets downloaded | 1050 |
| Retweets | 32 |
| Short tweets | 60 |
| Tweets kept | 958 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26de85bt/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 @ryiacy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2p7goxic/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/ryiacy')
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)
|
AnonymousSub/rule_based_only_classfn_twostage_epochs_1_shard_1_wikiqa | [
"pytorch",
"bert",
"text-classification",
"transformers"
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} | 27 | null | ---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_docusco_spacy
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.798987704
- name: NER Recall
type: recall
value: 0.7954112218
- name: NER F Score
type: f_score
value: 0.7971954516
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9698599662
---
English pipeline for part-of-speech and rhetorical tagging.
| Feature | Description |
| --- | --- |
| **Name** | `en_docusco_spacy` |
| **Version** | `1.3` |
| **spaCy** | `>=3.5.0,<3.6.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `ner` |
| **Components** | `tok2vec`, `tagger`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `MIT` |
| **Author** | [David Brown](https://docuscope.github.io) |
### Label Scheme
<details>
<summary>View label scheme (308 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `APPGE`, `AT`, `AT1`, `BCL21`, `BCL22`, `CC`, `CCB`, `CS`, `CS21`, `CS22`, `CS31`, `CS32`, `CS33`, `CS41`, `CS42`, `CS43`, `CS44`, `CSA`, `CSN`, `CST`, `CSW`, `CSW31`, `CSW32`, `CSW33`, `DA`, `DA1`, `DA2`, `DAR`, `DAT`, `DB`, `DB2`, `DD`, `DD1`, `DD2`, `DDQ`, `DDQGE`, `DDQGE31`, `DDQGE32`, `DDQGE33`, `DDQV`, `DDQV31`, `DDQV32`, `DDQV33`, `EX`, `FO`, `FU`, `FW`, `GE`, `IF`, `II`, `II21`, `II22`, `II31`, `II32`, `II33`, `II41`, `II42`, `II43`, `II44`, `IO`, `IW`, `JJ`, `JJ21`, `JJ22`, `JJ31`, `JJ32`, `JJ33`, `JJ41`, `JJ42`, `JJ43`, `JJ44`, `JJR`, `JJT`, `JK`, `MC`, `MC1`, `MC2`, `MC221`, `MC222`, `MCMC`, `MD`, `MF`, `ND1`, `NN`, `NN1`, `NN121`, `NN122`, `NN131`, `NN132`, `NN133`, `NN141`, `NN142`, `NN143`, `NN144`, `NN2`, `NN21`, `NN22`, `NN221`, `NN222`, `NN231`, `NN232`, `NN233`, `NN31`, `NN32`, `NN33`, `NNA`, `NNB`, `NNL1`, `NNL2`, `NNO`, `NNO2`, `NNT1`, `NNT131`, `NNT133`, `NNT2`, `NNU`, `NNU1`, `NNU2`, `NNU21`, `NNU22`, `NNU221`, `NNU222`, `NP`, `NP1`, `NP2`, `NPD1`, `NPD2`, `NPM1`, `NPM2`, `PN`, `PN1`, `PN121`, `PN122`, `PN21`, `PN22`, `PNQO`, `PNQS`, `PNQS31`, `PNQS32`, `PNQS33`, `PNQV`, `PNQV31`, `PNQV32`, `PNQV33`, `PNX1`, `PPGE`, `PPH1`, `PPHO1`, `PPHO2`, `PPHS1`, `PPHS2`, `PPIO1`, `PPIO2`, `PPIS1`, `PPIS2`, `PPX1`, `PPX121`, `PPX122`, `PPX2`, `PPX221`, `PPX222`, `PPY`, `RA`, `RA21`, `RA22`, `REX`, `REX21`, `REX22`, `REX41`, `REX42`, `REX43`, `REX44`, `RG`, `RG21`, `RG22`, `RG41`, `RG42`, `RG43`, `RG44`, `RGQ`, `RGQV`, `RGQV31`, `RGQV32`, `RGQV33`, `RGR`, `RGT`, `RL`, `RL21`, `RL22`, `RL31`, `RL32`, `RL33`, `RP`, `RPK`, `RR`, `RR21`, `RR22`, `RR31`, `RR32`, `RR33`, `RR41`, `RR42`, `RR43`, `RR44`, `RR51`, `RR52`, `RR53`, `RR54`, `RR55`, `RRQ`, `RRQV`, `RRQV31`, `RRQV32`, `RRQV33`, `RRR`, `RRT`, `RT`, `RT21`, `RT22`, `RT31`, `RT32`, `RT33`, `RT41`, `RT42`, `RT43`, `RT44`, `TO`, `UH`, `UH21`, `UH22`, `UH31`, `UH32`, `UH33`, `VB0`, `VBDR`, `VBDZ`, `VBG`, `VBI`, `VBM`, `VBN`, `VBR`, `VBZ`, `VD0`, `VDD`, `VDG`, `VDI`, `VDN`, `VDZ`, `VH0`, `VHD`, `VHG`, `VHI`, `VHN`, `VHZ`, `VM`, `VM21`, `VM22`, `VMK`, `VV0`, `VVD`, `VVG`, `VVGK`, `VVI`, `VVN`, `VVNK`, `VVZ`, `XX`, `Y`, `ZZ1`, `ZZ2`, `ZZ221`, `ZZ222` |
| **`ner`** | `AcademicTerms`, `AcademicWritingMoves`, `Character`, `Citation`, `CitationAuthority`, `CitationHedged`, `ConfidenceHedged`, `ConfidenceHigh`, `ConfidenceLow`, `Contingent`, `Description`, `Facilitate`, `FirstPerson`, `ForceStressed`, `Future`, `InformationChange`, `InformationChangeNegative`, `InformationChangePositive`, `InformationExposition`, `InformationPlace`, `InformationReportVerbs`, `InformationStates`, `InformationTopics`, `Inquiry`, `Interactive`, `MetadiscourseCohesive`, `MetadiscourseInteractive`, `Narrative`, `Negative`, `Positive`, `PublicTerms`, `Reasoning`, `Responsibility`, `Strategic`, `Uncertainty`, `Updates` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TAG_ACC` | 96.99 |
| `ENTS_F` | 79.72 |
| `ENTS_P` | 79.90 |
| `ENTS_R` | 79.54 |
| `TOK2VEC_LOSS` | 20924847.53 |
| `TAGGER_LOSS` | 1316790.55 |
| `NER_LOSS` | 5818469.98 | |
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 6 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-gpt-model-4
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. -->
# my-gpt-model-4
This model is a fine-tuned version of [bigmorning/my-gpt-model-3](https://huggingface.co/bigmorning/my-gpt-model-3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.0556
- 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 | Epoch |
|:----------:|:-----:|
| 5.0556 | 0 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-med-term-conditional-masking
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-med-term-conditional-masking
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6808
- Rouge2 Precision: 0.6855
- Rouge2 Recall: 0.486
- Rouge2 Fmeasure: 0.5507
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.9303 | 1.0 | 15827 | 0.8262 | 0.6603 | 0.4698 | 0.5318 |
| 0.8677 | 2.0 | 31654 | 0.7679 | 0.6695 | 0.4762 | 0.539 |
| 0.8315 | 3.0 | 47481 | 0.7393 | 0.6741 | 0.4783 | 0.5418 |
| 0.7999 | 4.0 | 63308 | 0.7194 | 0.6774 | 0.4811 | 0.5448 |
| 0.7746 | 5.0 | 79135 | 0.7059 | 0.6804 | 0.4815 | 0.5459 |
| 0.7785 | 6.0 | 94962 | 0.6958 | 0.6827 | 0.4841 | 0.5485 |
| 0.7592 | 7.0 | 110789 | 0.6893 | 0.6841 | 0.4849 | 0.5494 |
| 0.745 | 8.0 | 126616 | 0.6849 | 0.6846 | 0.4852 | 0.5498 |
| 0.7443 | 9.0 | 142443 | 0.6818 | 0.6854 | 0.4865 | 0.551 |
| 0.7417 | 10.0 | 158270 | 0.6808 | 0.6855 | 0.486 | 0.5507 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 2 | 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/1477531697814011904/6OQ-pQZG_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">Rod (🙂👍)</div>
<div style="text-align: center; font-size: 14px;">@thanksthoth</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 Rod (🙂👍).
| Data | Rod (🙂👍) |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 154 |
| Short tweets | 693 |
| Tweets kept | 2398 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/pd014k0e/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 @thanksthoth's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tswc3hnf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tswc3hnf/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/thanksthoth')
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)
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln30")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln30")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
``` |
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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} | 3 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/radagasttbrown/1648071147429/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/1362404255798280192/yIKMf5AN_400x400.png')">
</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">Radagast 🌋</div>
<div style="text-align: center; font-size: 14px;">@radagasttbrown</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 Radagast 🌋.
| Data | Radagast 🌋 |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 457 |
| Short tweets | 230 |
| Tweets kept | 2541 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1b1t67ko/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 @radagasttbrown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/boipgvkp/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/radagasttbrown')
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)
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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} | 28 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/coscorrodrift/1648073956402/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/1363260889164623877/vz-U9f3l_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">coscorrodrift</div>
<div style="text-align: center; font-size: 14px;">@coscorrodrift</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 coscorrodrift.
| Data | coscorrodrift |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 192 |
| Short tweets | 405 |
| Tweets kept | 2650 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3elna51z/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 @coscorrodrift's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2mof7q9s) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2mof7q9s/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/coscorrodrift')
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)
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_wikiqa_copy | [
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} | 2 | null | <!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Mar 23 05:58:21 UTC 2022`
- python version: `3.9.10 | packaged by conda-forge | (main, Feb 1 2022, 21:24:11) [GCC 9.4.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1`
- Git hash: `1991a25855821b8b61d775681aa0cdfd6161bbc8`
- Commit date: `Mon Mar 21 22:19:19 2022 +0800`
## asr_train_asr_conformer5_raw_mr_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/dev_mr|137|1563|80.2|17.1|2.8|2.0|21.8|71.5|
|inference_asr_model_valid.acc.ave/test_mr|200|2536|73.9|20.8|5.4|1.1|27.2|82.0|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/dev_mr|137|1563|81.3|15.6|3.1|2.0|20.7|72.3|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_mr|200|2536|76.6|20.7|2.7|0.9|24.3|80.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/dev_mr|137|9369|93.7|2.8|3.5|2.3|8.6|71.5|
|inference_asr_model_valid.acc.ave/test_mr|200|14174|90.3|3.7|5.9|1.6|11.3|82.0|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/dev_mr|137|9369|92.4|3.8|3.8|2.7|10.2|72.3|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_mr|200|14174|88.3|7.6|4.1|2.7|14.4|80.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave/dev_mr|137|6050|90.0|5.6|4.5|2.4|12.4|71.5|
|inference_asr_model_valid.acc.ave/test_mr|200|9254|85.6|7.6|6.8|1.6|16.0|82.0|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/dev_mr|137|6050|88.8|7.0|4.2|2.7|13.9|72.3|
|inference_lm_config_mr_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_mr|200|9254|83.2|12.3|4.5|3.9|20.7|80.5| |
AnonymousSub/rule_based_roberta_hier_triplet_0.1_epochs_1_shard_1_squad2.0 | [
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"roberta",
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} | 2 | null | ---
language: en
datasets:
- conll2003
widget:
- text: "My name is jean-baptiste and I live in montreal"
- text: "My name is clara and I live in berkeley, california."
- text: "My name is wolfgang and I live in berlin"
---
# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
## Introduction
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on emails/chat data and outperformed other models on this type of data specifically.
In particular the model seems to work better on entity that don't start with an upper case.
## Training data
Training data was classified as follow:
Abbreviation|Description
-|-
O |Outside of a named entity
MISC |Miscellaneous entity
PER |Person’s name
ORG |Organization
LOC |Location
In order to simplify, the prefix B- or I- from original conll2003 was removed.
I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size:
Train | Validation
-|-
17494 | 3250
## How to use camembert-ner with HuggingFace
##### Load camembert-ner and its sub-word tokenizer :
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("Apple was founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne to develop and sell Wozniak's Apple I personal computer")
[{'entity_group': 'ORG',
'score': 0.99381506,
'word': ' Apple',
'start': 0,
'end': 5},
{'entity_group': 'PER',
'score': 0.99970853,
'word': ' Steve Jobs',
'start': 29,
'end': 39},
{'entity_group': 'PER',
'score': 0.99981767,
'word': ' Steve Wozniak',
'start': 41,
'end': 54},
{'entity_group': 'PER',
'score': 0.99956465,
'word': ' Ronald Wayne',
'start': 59,
'end': 71},
{'entity_group': 'PER',
'score': 0.9997918,
'word': ' Wozniak',
'start': 92,
'end': 99},
{'entity_group': 'MISC',
'score': 0.99956393,
'word': ' Apple I',
'start': 102,
'end': 109}]
```
## Model performances
Model performances computed on conll2003 validation dataset (computed on the tokens predictions)
entity|precision|recall|f1
-|-|-|-
PER|0.9914|0.9927|0.9920
ORG|0.9627|0.9661|0.9644
LOC|0.9795|0.9862|0.9828
MISC|0.9292|0.9262|0.9277
Overall|0.9740|0.9766|0.9753
On private dataset (email, chat, informal discussion), computed on word predictions:
entity|precision|recall|f1
-|-|-|-
PER|0.8823|0.9116|0.8967
ORG|0.7694|0.7292|0.7487
LOC|0.8619|0.7768|0.8171
By comparison on the same private dataset, Spacy (en_core_web_trf-3.2.0) was giving:
entity|precision|recall|f1
-|-|-|-
PER|0.9146|0.8287|0.8695
ORG|0.7655|0.6437|0.6993
LOC|0.8727|0.6180|0.7236
|
AnonymousSub/rule_based_roberta_hier_triplet_epochs_1_shard_10 | [
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} | 6 | null | ---
language: en
datasets:
- squad_v2
license: cc-by-4.0
---
# roberta-base for QA
NOTE: This is version 2 of the model. See [this github issue](https://github.com/deepset-ai/FARM/issues/552) from the FARM repository for an explanation of why we updated. If you'd like to use version 1, specify `revision="v1.0"` when loading the model in Transformers 3.5. For exmaple:
```
model_name = "deepset/roberta-base-squad2"
pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering")
```
## Overview
**Language model:** roberta-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py)
**Infrastructure**: 4x Tesla v100
## Hyperparameters
```
batch_size = 96
n_epochs = 2
base_LM_model = "roberta-base"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
```
## Using a distilled model instead
Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.
## Performance
Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
```
"exact": 79.87029394424324,
"f1": 82.91251169582613,
"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-base-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
### In FARM
```python
from farm.modeling.adaptive_model import AdaptiveModel
from farm.modeling.tokenization import Tokenizer
from farm.infer import Inferencer
model_name = "deepset/roberta-base-squad2"
# a) Get predictions
nlp = Inferencer.load(model_name, task_type="question_answering")
QA_input = [{"questions": ["Why is model conversion important?"],
"text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
# b) Load model & tokenizer
model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
tokenizer = Tokenizer.load(model_name)
```
### In haystack
For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
# or
reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
```
## Authors
Branden Chan: `branden.chan [at] deepset.ai`
Timo Möller: `timo.moeller [at] deepset.ai`
Malte Pietsch: `malte.pietsch [at] deepset.ai`
Tanay Soni: `tanay.soni [at] deepset.ai`
## About us
We bring NLP to the industry via open source!
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
- [FARM](https://github.com/deepset-ai/FARM)
- [Haystack](https://github.com/deepset-ai/haystack/)
Get in touch:
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs)
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1 | [
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} | 10 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: ru
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/russian_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout fa1b865352475b744c37f70440de1cc6b257ba70
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/russian_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Mar 23 19:56:59 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `fa1b865352475b744c37f70440de1cc6b257ba70`
- Commit date: `Wed Feb 16 16:42:36 2022 -0500`
## asr_blstm_specaug_num_time_mask_2_lr_0.1
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|71189|79.3|18.4|2.4|2.1|22.8|71.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|537025|95.0|3.0|2.0|1.1|6.1|71.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_ru|7307|399162|93.2|4.5|2.3|1.4|8.2|71.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_blstm_specaug_num_time_mask_2_lr_0.1
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_ru_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_ru_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_ru_sp/wav.scp
- speech
- sound
- - dump/raw/train_ru_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_ru/wav.scp
- speech
- sound
- - dump/raw/dev_ru/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- е
- о
- и
- с
- м
- а
- в
- н
- д
- т
- у
- .
- я
- ы
- л
- й
- з
- п
- к
- но
- ','
- ▁в
- ра
- б
- ж
- ю
- г
- го
- ▁по
- ▁с
- ни
- ч
- х
- р
- ко
- ре
- ш
- ли
- ть
- ▁на
- ль
- ва
- ер
- ▁и
- ет
- ст
- ро
- на
- ла
- ле
- ь
- ен
- то
- ло
- да
- ка
- ▁не
- ств
- ти
- ци
- ся
- ▁за
- ▁про
- че
- ем
- ру
- же
- та
- ▁при
- ▁со
- ▁это
- ри
- ф
- ки
- бо
- ц
- ▁С
- ста
- ения
- щ
- сти
- э
- К
- О
- А
- И
- '-'
- Т
- Я
- Б
- Д
- М
- '?'
- –
- Г
- —
- '!'
- У
- ъ
- '"'
- »
- ё
- Ф
- ':'
- Х
- Ю
- F
- ;
- O
- I
- E
- R
- −
- В
- С
- ''''
- П
- C
- L
- A
- ‐
- H
- T
- G
- S
- (
- )
- B
- K
- P
- Z
- M
- Й
- X
- Ц
- Ж
- Ч
- Ш
- «
- З
- Л
- Е
- Р
- Э
- N
- Н
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/ru_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_ru_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
AnonymousSub/rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
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} | 2 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/btohtoh-willitbetoomuch/1648087519902/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/1506402743296020484/X79Yfcx5_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/1488467916198539265/3pTy_Kr3_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">BToh & unloading</div>
<div style="text-align: center; font-size: 14px;">@btohtoh-willitbetoomuch</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 BToh & unloading.
| Data | BToh | unloading |
| --- | --- | --- |
| Tweets downloaded | 3241 | 85 |
| Retweets | 347 | 0 |
| Short tweets | 480 | 3 |
| Tweets kept | 2414 | 82 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2d3flykp/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 @btohtoh-willitbetoomuch's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lp51jew/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/btohtoh-willitbetoomuch')
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)
|
AnonymousSub/unsup-consert-base_squad2.0 | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
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} | 2 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9264836138175376
- name: Recall
type: recall
value: 0.9361226087929299
- name: F1
type: f1
value: 0.9312781703856213
- name: Accuracy
type: accuracy
value: 0.9836529143565221
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
- Precision: 0.9265
- Recall: 0.9361
- F1: 0.9313
- Accuracy: 0.9837
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2437 | 1.0 | 878 | 0.0745 | 0.9144 | 0.9173 | 0.9158 | 0.9799 |
| 0.0518 | 2.0 | 1756 | 0.0621 | 0.9177 | 0.9353 | 0.9264 | 0.9826 |
| 0.03 | 3.0 | 2634 | 0.0616 | 0.9265 | 0.9361 | 0.9313 | 0.9837 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/unsup-consert-emanuals | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
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} | 2 | null | ---
license: afl-3.0
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [PAWS](https://github.com/google-research-datasets/paws) for paraphrase generation.
### Details of T5
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Binary Paraphrase Classification)
Dataset: ```PAWS``` [link](https://github.com/google-research-datasets/paws)
## Performance:
F1-score: 0.86
ROC-AUC score: 0.86
## Usage:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
# use GPU for better performance
device = torch.device('cuda')
tokenizer = T5Tokenizer.from_pretrained("etomoscow/T5_paraphrase_detector")
model = T5ForConditionalGeneration.from_pretrained("etomoscow/T5_paraphrase_detector").to(device)
text_1 = 'During her sophomore , junior and senior summers , she spent half of it with her Alaska team , and half playing , and living in Oregon .'
text_2 = 'During her second , junior and senior summers , she spent half of it with her Alaska team , half playing and living in Oregon.'
true_label = '1'
input_text = tokenizer.encode_plus(text_1 + ' <sep> ' + text_2, return_tensors='pt')
out = model.generate(input_text['input_ids'].to(device))
print(tokenizer.decode(out.squeeze(0), skip_special_tokens=True))
# 1
``` |
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} | 0 | null | ---
language:
- tr
- uk
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-base-uk-tr
results:
- task:
name: Translation ukr-tur
type: translation
args: ukr-tur
dataset:
name: flores101-devtest
type: flores_101
args: ukr tur devtest
metrics:
- name: BLEU
type: bleu
value: 20.5
- task:
name: Translation ukr-tur
type: translation
args: ukr-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-tur
metrics:
- name: BLEU
type: bleu
value: 45.2
---
# opus-mt-tc-base-uk-tr
Neural machine translation model for translating from Ukrainian (uk) to Turkish (tr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-07
* source language(s): ukr
* target language(s):
* valid target language labels:
* model: transformer-align
* data: opusTCv20210807+pft ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pft_transformer-align_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-tur/opusTCv20210807+pft_transformer-align_2022-03-07.zip)
* more information released models: [OPUS-MT ukr-tur README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-tur/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>><<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>tur<< Тисячі єн достатньо?",
">>tur<< Цюріх — місто у Швейцарії."
]
model_name = "pytorch-models/opus-mt-tc-base-uk-tr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Binlerce yen yeterli mi?
# Zürih, İsviçre'de bir şehirdir.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-tr")
print(pipe(">>tur<< Тисячі єн достатньо?"))
# expected output: Binlerce yen yeterli mi?
```
## Benchmarks
* test set translations: [opusTCv20210807+pft_transformer-align_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-tur/opusTCv20210807+pft_transformer-align_2022-03-07.test.txt)
* test set scores: [opusTCv20210807+pft_transformer-align_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-tur/opusTCv20210807+pft_transformer-align_2022-03-07.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ukr-tur | tatoeba-test-v2021-08-07 | 0.70938 | 45.2 | 2520 | 11927 |
| ukr-tur | flores101-devtest | 0.54001 | 20.5 | 1012 | 20253 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 22:02:24 EET 2022
* port machine: LM0-400-22516.local
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-music-search
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-music-search
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: 4.6516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 256 | 4.6572 |
| 5.0184 | 2.0 | 512 | 4.6461 |
| 5.0184 | 3.0 | 768 | 4.6516 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.7.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Anthos23/test_trainer | [] | null | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2047
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4786
- Rouge1: 28.2047
- Rouge2: 7.7109
- Rougel: 22.1559
- Rougelsum: 22.1595
- Gen Len: 18.8257
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7156 | 1.0 | 12753 | 2.4786 | 28.2047 | 7.7109 | 22.1559 | 22.1595 | 18.8257 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0a0+b6df043
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Anubhav23/IndianlegalBert | [] | null | {
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} | 0 | null | ---
language:
- bat
- lt
- lv
- ru
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-base-zle-bat
results:
- task:
name: Translation rus-lav
type: translation
args: rus-lav
dataset:
name: flores101-devtest
type: flores_101
args: rus lav devtest
metrics:
- name: BLEU
type: bleu
value: 20.0
- task:
name: Translation rus-lit
type: translation
args: rus-lit
dataset:
name: flores101-devtest
type: flores_101
args: rus lit devtest
metrics:
- name: BLEU
type: bleu
value: 20.6
- task:
name: Translation ukr-lav
type: translation
args: ukr-lav
dataset:
name: flores101-devtest
type: flores_101
args: ukr lav devtest
metrics:
- name: BLEU
type: bleu
value: 21.4
- task:
name: Translation ukr-lit
type: translation
args: ukr-lit
dataset:
name: flores101-devtest
type: flores_101
args: ukr lit devtest
metrics:
- name: BLEU
type: bleu
value: 20.5
- task:
name: Translation rus-lav
type: translation
args: rus-lav
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-lav
metrics:
- name: BLEU
type: bleu
value: 55.3
- task:
name: Translation rus-lit
type: translation
args: rus-lit
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-lit
metrics:
- name: BLEU
type: bleu
value: 47.2
---
# opus-mt-tc-base-zle-bat
Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-14
* source language(s): rus
* target language(s): lav lit
* valid target language labels: >>lav<< >>lit<<
* model: transformer-align
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-align_2022-03-14.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.zip)
* more information released models: [OPUS-MT zle-bat README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-bat/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>lav<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>lav<< Африка - колыбель человечества.",
">>lit<< Том — наш капітан."
]
model_name = "pytorch-models/opus-mt-tc-base-zle-bat"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Āfrika ir cilvēces šūpulis.
# Tomas yra mūsų kapitonas.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-zle-bat")
print(pipe(">>lav<< Африка - колыбель человечества."))
# expected output: Āfrika ir cilvēces šūpulis.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-align_2022-03-14.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.test.txt)
* test set scores: [opusTCv20210807_transformer-align_2022-03-14.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-bat/opusTCv20210807_transformer-align_2022-03-14.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| rus-lav | tatoeba-test-v2021-08-07 | 0.74223 | 55.3 | 274 | 1518 |
| rus-lit | tatoeba-test-v2021-08-07 | 0.70795 | 47.2 | 3598 | 20662 |
| rus-lav | flores101-devtest | 0.50134 | 20.0 | 1012 | 22092 |
| rus-lit | flores101-devtest | 0.53732 | 20.6 | 1012 | 20695 |
| ukr-lav | flores101-devtest | 0.51379 | 21.4 | 1012 | 22092 |
| ukr-lit | flores101-devtest | 0.54085 | 20.5 | 1012 | 20695 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 22:11:57 EET 2022
* port machine: LM0-400-22516.local
|
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} | 0 | null | ---
language:
- ru
---
Russian GPT2-medium model for RDF-triplet to text conversion.
https://github.com/pavel-blinov/ru-rdf2text
```
@inproceedings{blinov-2020-semantic,
title = "Semantic Triples Verbalization with Generative Pre-Training Model",
author = "Blinov, Pavel",
booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
month = "12",
year = "2020",
address = "Dublin, Ireland (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.webnlg-1.17",
pages = "154--158",
abstract = "The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters{'} influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.",
}
```
|
Apisate/DialoGPT-small-jordan | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
}
} | 12 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/iopred/1648161500488/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/804464329202409472/_-74eUkS_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">diet dr. kit</div>
<div style="text-align: center; font-size: 14px;">@iopred</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 diet dr. kit.
| Data | diet dr. kit |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 177 |
| Short tweets | 258 |
| Tweets kept | 2805 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/52vmud4n/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 @iopred's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i464eff) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i464eff/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/iopred')
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)
|
Apisate/Discord-Ai-Bot | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 11 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/tariqnasheed/1648112086220/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/1506809010988539910/bBCRvJ4K_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">Tariq Nasheed 🇺🇸</div>
<div style="text-align: center; font-size: 14px;">@tariqnasheed</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 Tariq Nasheed 🇺🇸.
| Data | Tariq Nasheed 🇺🇸 |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 273 |
| Short tweets | 396 |
| Tweets kept | 2566 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/f1jq7tem/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 @tariqnasheed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dn7iubq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dn7iubq/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/tariqnasheed')
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)
|
ArBert/bert-base-uncased-finetuned-ner-agglo | [] | null | {
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}
} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/kytalli-vi0linheart/1648114676311/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/1500859213622300673/izXwf0KK_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/1376749372831002627/2B9FZTnI_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">sal & G</div>
<div style="text-align: center; font-size: 14px;">@kytalli-vi0linheart</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 sal & G.
| Data | sal | G |
| --- | --- | --- |
| Tweets downloaded | 3114 | 3249 |
| Retweets | 421 | 55 |
| Short tweets | 541 | 226 |
| Tweets kept | 2152 | 2968 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tj76wad/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 @kytalli-vi0linheart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a1bludi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a1bludi/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/kytalli-vi0linheart')
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)
|
ArBert/bert-base-uncased-finetuned-ner-gmm | [] | null | {
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}
}
} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/madeleine/1648114714373/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/1227670393453936642/6rdB_DqU_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">Madeleine Albright</div>
<div style="text-align: center; font-size: 14px;">@madeleine</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 Madeleine Albright.
| Data | Madeleine Albright |
| --- | --- |
| Tweets downloaded | 1111 |
| Retweets | 249 |
| Short tweets | 3 |
| Tweets kept | 859 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2a3z3e8y/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 @madeleine's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2q01k6dh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2q01k6dh/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/madeleine')
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)
|
ArBert/bert-base-uncased-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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"prefix": null
}
}
} | 8 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/vi0linheart/1648116634962/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/1500859213622300673/izXwf0KK_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">sal</div>
<div style="text-align: center; font-size: 14px;">@vi0linheart</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 sal.
| Data | sal |
| --- | --- |
| Tweets downloaded | 3114 |
| Retweets | 421 |
| Short tweets | 541 |
| Tweets kept | 2152 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21y9qo98/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 @vi0linheart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3t019c6m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3t019c6m/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/vi0linheart')
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)
|
ArBert/roberta-base-finetuned-ner-agglo | [] | null | {
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}
} | 0 | null | ---
language:
- be
- fr
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-fr
results:
- task:
name: Translation bel-fra
type: translation
args: bel-fra
dataset:
name: tatoeba-test-v2020-07-28-v2021-08-07
type: tatoeba_mt
args: bel-fra
metrics:
- name: BLEU
type: bleu
value: 46.4
- task:
name: Translation multi-fra
type: translation
args: multi-fra
dataset:
name: tatoeba-test-v2020-07-28-v2021-08-07
type: tatoeba_mt
args: multi-fra
metrics:
- name: BLEU
type: bleu
value: 52.4
- task:
name: Translation rus-fra
type: translation
args: rus-fra
dataset:
name: tatoeba-test-v2020-07-28-v2021-08-07
type: tatoeba_mt
args: rus-fra
metrics:
- name: BLEU
type: bleu
value: 51.8
- task:
name: Translation ukr-fra
type: translation
args: ukr-fra
dataset:
name: tatoeba-test-v2020-07-28-v2021-08-07
type: tatoeba_mt
args: ukr-fra
metrics:
- name: BLEU
type: bleu
value: 50.7
- task:
name: Translation rus-fra
type: translation
args: rus-fra
dataset:
name: newstest2012
type: wmt-2012-news
args: rus-fra
metrics:
- name: BLEU
type: bleu
value: 25.3
- task:
name: Translation rus-fra
type: translation
args: rus-fra
dataset:
name: newstest2013
type: wmt-2013-news
args: rus-fra
metrics:
- name: BLEU
type: bleu
value: 29.7
---
# opus-mt-tc-big-zle-fr
Neural machine translation model for translating from East Slavic languages (zle) to French (fr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): bel rus ukr
* target language(s): fra
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-fra/opusTCv20210807_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT zle-fra README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-fra/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Подавай блюдо на тарелке.",
"Операція не може чекати."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-fr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Servez le plat dans l'assiette.
# L'opération ne peut pas attendre.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-fr")
print(pipe("Подавай блюдо на тарелке."))
# expected output: Servez le plat dans l'assiette.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-fra/opusTCv20210807_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-fra/opusTCv20210807_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-fra | tatoeba-test-v2020-07-28-v2021-08-07 | 0.65415 | 46.4 | 283 | 2005 |
| multi-fra | tatoeba-test-v2020-07-28-v2021-08-07 | 0.68422 | 52.4 | 10000 | 66671 |
| rus-fra | tatoeba-test-v2020-07-28-v2021-08-07 | 0.68699 | 51.8 | 11490 | 80573 |
| ukr-fra | tatoeba-test-v2020-07-28-v2021-08-07 | 0.67887 | 50.7 | 10035 | 63222 |
| rus-fra | newstest2012 | 0.53679 | 25.3 | 3003 | 78011 |
| rus-fra | newstest2013 | 0.56211 | 29.7 | 3000 | 70037 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 22:45:20 EET 2022
* port machine: LM0-400-22516.local
|
ArBert/roberta-base-finetuned-ner-kmeans | [
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | {
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Horovod_Tweet_Sentiment_1k_5eps
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. -->
# Horovod_Tweet_Sentiment_1k_5eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5216092
- Train Accuracy: 0.784375
- Validation Loss: 0.92405033
- Validation Accuracy: 0.4875
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.7129049 | 0.50937504 | 0.7314203 | 0.490625 | 0 |
| 0.73165804 | 0.47343752 | 0.6929074 | 0.484375 | 1 |
| 0.6827939 | 0.55 | 0.6864271 | 0.50625 | 2 |
| 0.66076773 | 0.5578125 | 0.60817575 | 0.69687504 | 3 |
| 0.5216092 | 0.784375 | 0.92405033 | 0.4875 | 4 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.6.0
- Tokenizers 0.11.6
|
ArJakusz/DialoGPT-small-stark | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-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-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3631
- Wer: 0.3907
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2448 | 7.4 | 400 | 0.5564 | 0.5914 |
| 0.2245 | 14.81 | 800 | 0.3631 | 0.3907 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
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} | 0 | null | ---
language:
- da
- gmq
- nb
- false
- ru
- sv
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-gmq
results:
- task:
name: Translation rus-dan
type: translation
args: rus-dan
dataset:
name: flores101-devtest
type: flores_101
args: rus dan devtest
metrics:
- name: BLEU
type: bleu
value: 28.0
- task:
name: Translation rus-nob
type: translation
args: rus-nob
dataset:
name: flores101-devtest
type: flores_101
args: rus nob devtest
metrics:
- name: BLEU
type: bleu
value: 20.6
- task:
name: Translation rus-swe
type: translation
args: rus-swe
dataset:
name: flores101-devtest
type: flores_101
args: rus swe devtest
metrics:
- name: BLEU
type: bleu
value: 26.4
- task:
name: Translation ukr-dan
type: translation
args: ukr-dan
dataset:
name: flores101-devtest
type: flores_101
args: ukr dan devtest
metrics:
- name: BLEU
type: bleu
value: 30.3
- task:
name: Translation ukr-nob
type: translation
args: ukr-nob
dataset:
name: flores101-devtest
type: flores_101
args: ukr nob devtest
metrics:
- name: BLEU
type: bleu
value: 21.1
- task:
name: Translation ukr-swe
type: translation
args: ukr-swe
dataset:
name: flores101-devtest
type: flores_101
args: ukr swe devtest
metrics:
- name: BLEU
type: bleu
value: 28.8
- task:
name: Translation rus-dan
type: translation
args: rus-dan
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-dan
metrics:
- name: BLEU
type: bleu
value: 59.6
- task:
name: Translation rus-nob
type: translation
args: rus-nob
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-nob
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation rus-swe
type: translation
args: rus-swe
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-swe
metrics:
- name: BLEU
type: bleu
value: 53.3
---
# opus-mt-tc-big-zle-gmq
Neural machine translation model for translating from East Slavic languages (zle) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-14
* source language(s): rus ukr
* target language(s): dan nob nor swe
* valid target language labels: >>dan<< >>nob<< >>nor<< >>swe<<
* model: transformer-big
* data: opusTCv20210807+pft ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pft_transformer-big_2022-03-14.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-gmq/opusTCv20210807+pft_transformer-big_2022-03-14.zip)
* more information released models: [OPUS-MT zle-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-gmq/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>dan<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>dan<< Заўтра ўжо чацвер.",
">>swe<< Том грав з Мері в кішки-мишки."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-gmq"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# I morgen er det torsdag.
# Tom lekte med Mary i katt-möss.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-gmq")
print(pipe(">>dan<< Заўтра ўжо чацвер."))
# expected output: I morgen er det torsdag.
```
## Benchmarks
* test set translations: [opusTCv20210807+pft_transformer-big_2022-03-14.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-gmq/opusTCv20210807+pft_transformer-big_2022-03-14.test.txt)
* test set scores: [opusTCv20210807+pft_transformer-big_2022-03-14.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-gmq/opusTCv20210807+pft_transformer-big_2022-03-14.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| rus-dan | tatoeba-test-v2021-08-07 | 0.74307 | 59.6 | 1713 | 11746 |
| rus-nob | tatoeba-test-v2021-08-07 | 0.66376 | 46.1 | 1277 | 11672 |
| rus-swe | tatoeba-test-v2021-08-07 | 0.69608 | 53.3 | 1282 | 8449 |
| bel-dan | flores101-devtest | 0.47621 | 13.9 | 1012 | 24638 |
| bel-nob | flores101-devtest | 0.44966 | 10.8 | 1012 | 23873 |
| bel-swe | flores101-devtest | 0.47274 | 13.2 | 1012 | 23121 |
| rus-dan | flores101-devtest | 0.55917 | 28.0 | 1012 | 24638 |
| rus-nob | flores101-devtest | 0.50724 | 20.6 | 1012 | 23873 |
| rus-swe | flores101-devtest | 0.55812 | 26.4 | 1012 | 23121 |
| ukr-dan | flores101-devtest | 0.57829 | 30.3 | 1012 | 24638 |
| ukr-nob | flores101-devtest | 0.52271 | 21.1 | 1012 | 23873 |
| ukr-swe | flores101-devtest | 0.57499 | 28.8 | 1012 | 23121 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 23:13:54 EET 2022
* port machine: LM0-400-22516.local
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Horovod_Tweet_Sentiment_1k_3eps
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. -->
# Horovod_Tweet_Sentiment_1k_3eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6961535
- Train Accuracy: 0.49375
- Validation Loss: 0.6676211
- Validation Accuracy: 0.64375
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.717013 | 0.46562502 | 0.73462963 | 0.515625 | 0 |
| 0.70586157 | 0.5078125 | 0.6937375 | 0.484375 | 1 |
| 0.6961535 | 0.49375 | 0.6676211 | 0.64375 | 2 |
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.6.0
- Tokenizers 0.11.6
|
Araf/Ummah | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9241019999324234
---
<!-- 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: 0.2270
- Accuracy: 0.924
- F1: 0.9241
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8204 | 1.0 | 250 | 0.3160 | 0.9035 | 0.9008 |
| 0.253 | 2.0 | 500 | 0.2270 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Aran/DialoGPT-medium-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 8 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- DSTC2
license: cc-by-4.0
---
## ESPnet2 ASR pretrained model
### `espnet/Karthik_DSTC2_asr_train_asr_wav2vec_conformer_2`
This model was trained by Karthik using DSTC2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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} | 0 | null | ---
language:
- be
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zle
results:
- task:
name: Translation rus-ukr
type: translation
args: rus-ukr
dataset:
name: flores101-devtest
type: flores_101
args: rus ukr devtest
metrics:
- name: BLEU
type: bleu
value: 25.5
- task:
name: Translation ukr-rus
type: translation
args: ukr-rus
dataset:
name: flores101-devtest
type: flores_101
args: ukr rus devtest
metrics:
- name: BLEU
type: bleu
value: 28.3
- task:
name: Translation bel-rus
type: translation
args: bel-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-rus
metrics:
- name: BLEU
type: bleu
value: 68.6
- task:
name: Translation bel-ukr
type: translation
args: bel-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-ukr
metrics:
- name: BLEU
type: bleu
value: 65.5
- task:
name: Translation rus-bel
type: translation
args: rus-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-bel
metrics:
- name: BLEU
type: bleu
value: 50.3
- task:
name: Translation rus-ukr
type: translation
args: rus-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-ukr
metrics:
- name: BLEU
type: bleu
value: 70.1
- task:
name: Translation ukr-bel
type: translation
args: ukr-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-bel
metrics:
- name: BLEU
type: bleu
value: 58.9
- task:
name: Translation ukr-rus
type: translation
args: ukr-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-rus
metrics:
- name: BLEU
type: bleu
value: 75.7
---
# opus-mt-tc-big-zle-zle
Neural machine translation model for translating from East Slavic languages (zle) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-07
* source language(s): bel rus ukr
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.zip)
* more information released models: [OPUS-MT zle-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ukr<< Кот мёртвый.",
">>bel<< Джон живе в Нью-Йорку."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Кіт мертвий.
# Джон жыве ў Нью-Йорку.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zle")
print(pipe(">>ukr<< Кот мёртвый."))
# expected output: Кіт мертвий.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-rus | tatoeba-test-v2021-08-07 | 0.82526 | 68.6 | 2500 | 18895 |
| bel-ukr | tatoeba-test-v2021-08-07 | 0.81036 | 65.5 | 2355 | 15179 |
| rus-bel | tatoeba-test-v2021-08-07 | 0.66943 | 50.3 | 2500 | 18756 |
| rus-ukr | tatoeba-test-v2021-08-07 | 0.83639 | 70.1 | 10000 | 60212 |
| ukr-bel | tatoeba-test-v2021-08-07 | 0.75368 | 58.9 | 2355 | 15175 |
| ukr-rus | tatoeba-test-v2021-08-07 | 0.86806 | 75.7 | 10000 | 60387 |
| bel-rus | flores101-devtest | 0.47960 | 14.5 | 1012 | 23295 |
| bel-ukr | flores101-devtest | 0.47335 | 12.8 | 1012 | 22810 |
| rus-ukr | flores101-devtest | 0.55287 | 25.5 | 1012 | 22810 |
| ukr-rus | flores101-devtest | 0.56224 | 28.3 | 1012 | 23295 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 00:15:39 EET 2022
* port machine: LM0-400-22516.local
|
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} | 0 | null | ---
language:
- be
- cs
- pl
- ru
- uk
- zle
- zlw
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zlw
results:
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: flores101-devtest
type: flores_101
args: rus ces devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation ukr-ces
type: translation
args: ukr-ces
dataset:
name: flores101-devtest
type: flores_101
args: ukr ces devtest
metrics:
- name: BLEU
type: bleu
value: 25.1
- task:
name: Translation bel-pol
type: translation
args: bel-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-pol
metrics:
- name: BLEU
type: bleu
value: 47.1
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-ces
metrics:
- name: BLEU
type: bleu
value: 53.4
- task:
name: Translation rus-pol
type: translation
args: rus-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-pol
metrics:
- name: BLEU
type: bleu
value: 53.7
- task:
name: Translation ukr-ces
type: translation
args: ukr-ces
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-ces
metrics:
- name: BLEU
type: bleu
value: 58.0
- task:
name: Translation ukr-pol
type: translation
args: ukr-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-pol
metrics:
- name: BLEU
type: bleu
value: 57.0
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: newstest2013
type: wmt-2013-news
args: rus-ces
metrics:
- name: BLEU
type: bleu
value: 26.0
---
# opus-mt-tc-big-zle-zlw
Neural machine translation model for translating from East Slavic languages (zle) to West Slavic languages (zlw).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): bel rus ukr
* target language(s): ces pol
* valid target language labels: >>ces<< >>pol<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT zle-zlw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zlw/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>ces<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>pol<< Это метафора.",
">>pol<< Что вы делали?"
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zlw"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# To metafora.
# Co robiliście?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zlw")
print(pipe(">>pol<< Это метафора."))
# expected output: To metafora.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-pol | tatoeba-test-v2021-08-07 | 0.65517 | 47.1 | 287 | 1706 |
| rus-ces | tatoeba-test-v2021-08-07 | 0.69695 | 53.4 | 2934 | 16831 |
| rus-pol | tatoeba-test-v2021-08-07 | 0.72176 | 53.7 | 3543 | 21505 |
| ukr-ces | tatoeba-test-v2021-08-07 | 0.73149 | 58.0 | 1787 | 8550 |
| ukr-pol | tatoeba-test-v2021-08-07 | 0.74649 | 57.0 | 2519 | 13201 |
| bel-ces | flores101-devtest | 0.41248 | 11.1 | 1012 | 22101 |
| bel-pol | flores101-devtest | 0.42240 | 10.2 | 1012 | 22520 |
| rus-ces | flores101-devtest | 0.50971 | 23.1 | 1012 | 22101 |
| rus-pol | flores101-devtest | 0.48672 | 18.4 | 1012 | 22520 |
| ukr-ces | flores101-devtest | 0.52482 | 25.1 | 1012 | 22101 |
| ukr-pol | flores101-devtest | 0.48790 | 18.8 | 1012 | 22520 |
| rus-ces | newstest2012 | 0.45834 | 18.8 | 3003 | 65456 |
| rus-ces | newstest2013 | 0.52364 | 26.0 | 3000 | 57250 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 00:50:29 EET 2022
* port machine: LM0-400-22516.local
|
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} | 0 | null | ---
language:
- cs
- sk
- uk
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-base-ces_slk-uk
results:
- task:
name: Translation ces-ukr
type: translation
args: ces-ukr
dataset:
name: flores101-devtest
type: flores_101
args: ces ukr devtest
metrics:
- name: BLEU
type: bleu
value: 21.8
- task:
name: Translation slk-ukr
type: translation
args: slk-ukr
dataset:
name: flores101-devtest
type: flores_101
args: slk ukr devtest
metrics:
- name: BLEU
type: bleu
value: 21.4
- task:
name: Translation ces-ukr
type: translation
args: ces-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ces-ukr
metrics:
- name: BLEU
type: bleu
value: 48.6
---
# opus-mt-tc-base-ces_slk-uk
Neural machine translation model for translating from Czech and Slovak (cs+sk) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-08
* source language(s): ces
* target language(s): ukr
* model: transformer-align
* data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pbt_transformer-align_2022-03-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ces+slk-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.zip)
* more information released models: [OPUS-MT ces+slk-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces+slk-ukr/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Replace this with text in an accepted source language.",
"This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-base-ces_slk-uk"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-ces_slk-uk")
print(pipe("Replace this with text in an accepted source language."))
```
## Benchmarks
* test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces+slk-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt)
* test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces+slk-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ces-ukr | tatoeba-test-v2021-08-07 | 0.66867 | 48.6 | 1787 | 8891 |
| ces-ukr | flores101-devtest | 0.51387 | 21.8 | 1012 | 22810 |
| slk-ukr | flores101-devtest | 0.51418 | 21.4 | 1012 | 22810 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 01:01:20 EET 2022
* port machine: LM0-400-22516.local
|
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},
"translation_en_to_fr": {
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},
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}
}
} | 8 | null | ---
language:
- be
- fr
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fr-zle
results:
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: flores101-devtest
type: flores_101
args: fra rus devtest
metrics:
- name: BLEU
type: bleu
value: 25.8
- task:
name: Translation fra-ukr
type: translation
args: fra-ukr
dataset:
name: flores101-devtest
type: flores_101
args: fra ukr devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation fra-bel
type: translation
args: fra-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-bel
metrics:
- name: BLEU
type: bleu
value: 31.1
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation fra-ukr
type: translation
args: fra-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-ukr
metrics:
- name: BLEU
type: bleu
value: 39.9
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: newstest2012
type: wmt-2012-news
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: newstest2013
type: wmt-2013-news
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 24.8
---
# opus-mt-tc-big-fr-zle
Neural machine translation model for translating from French (fr) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): fra
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT fra-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>rus<< Ils ont acheté un très bon appareil photo.",
">>ukr<< Il s'est soudain mis à pleuvoir."
]
model_name = "pytorch-models/opus-mt-tc-big-fr-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Они купили очень хорошую камеру.
# Раптом почався дощ.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-fr-zle")
print(pipe(">>rus<< Ils ont acheté un très bon appareil photo."))
# expected output: Они купили очень хорошую камеру.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| fra-bel | tatoeba-test-v2021-08-07 | 0.52711 | 31.1 | 283 | 1703 |
| fra-rus | tatoeba-test-v2021-08-07 | 0.66502 | 46.1 | 11490 | 70123 |
| fra-ukr | tatoeba-test-v2021-08-07 | 0.61860 | 39.9 | 10035 | 54372 |
| fra-rus | flores101-devtest | 0.54106 | 25.8 | 1012 | 23295 |
| fra-ukr | flores101-devtest | 0.52733 | 23.1 | 1012 | 22810 |
| fra-rus | newstest2012 | 0.51254 | 23.1 | 3003 | 64790 |
| fra-rus | newstest2013 | 0.52342 | 24.8 | 3000 | 58560 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 02:05:04 EET 2022
* port machine: LM0-400-22516.local
|
AriakimTaiyo/DialoGPT-revised-Kumiko | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
} | 6 | null | ---
language:
- hu
- uk
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-base-hu-uk
results:
- task:
name: Translation hun-ukr
type: translation
args: hun-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hun-ukr
metrics:
- name: BLEU
type: bleu
value: 38.1
---
# opus-mt-tc-base-hu-uk
Neural machine translation model for translating from Hungarian (hu) to Ukrainian (uk).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-08
* source language(s): hun
* target language(s): ukr
* model: transformer-align
* data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pbt_transformer-align_2022-03-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.zip)
* more information released models: [OPUS-MT hun-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hun-ukr/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"1000 dollárral tartozom neked.",
"Vizet iszom."
]
model_name = "pytorch-models/opus-mt-tc-base-hu-uk"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Я зобов'язаний вам 1000 доларів.
# Я п'ю воду.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-hu-uk")
print(pipe("1000 dollárral tartozom neked."))
# expected output: Я зобов'язаний вам 1000 доларів.
```
## Benchmarks
* test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt)
* test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-ukr/opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| hun-ukr | tatoeba-test-v2021-08-07 | 0.61006 | 38.1 | 473 | 2606 |
| hun-ukr | flores101-devtest | 0.49490 | 19.8 | 1012 | 22810 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 02:19:16 EET 2022
* port machine: LM0-400-22516.local
|
Arpita/opus-mt-en-ro-finetuned-synthon-to-reactant | [] | null | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: electra-base-discriminator-yelp-mlm
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: yelp_review_full yelp_review_full
type: yelp_review_full
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.678284391624956
---
<!-- 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. -->
# electra-base-discriminator-yelp-mlm
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the yelp_review_full yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5550
- Accuracy: 0.6783
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Augustvember/WokkaBot9 | [] | null | {
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}
} | 0 | 2022-03-24T21:36:02Z | ---
language:
- en
- es
tags:
- translation
license: apache-2.0
---
### eng-spa
* source group: English
* target group: Spanish
* OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md)
* model: transformer
* source language(s): eng
* target language(s): spa
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip)
* test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt)
* test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-engspa.eng.spa | 31.0 | 0.583 |
| news-test2008-engspa.eng.spa | 29.7 | 0.564 |
| newstest2009-engspa.eng.spa | 30.2 | 0.578 |
| newstest2010-engspa.eng.spa | 36.9 | 0.620 |
| newstest2011-engspa.eng.spa | 38.2 | 0.619 |
| newstest2012-engspa.eng.spa | 39.0 | 0.625 |
| newstest2013-engspa.eng.spa | 35.0 | 0.598 |
| Tatoeba-test.eng.spa | 54.9 | 0.721 |
### System Info:
- hf_name: eng-spa
- source_languages: eng
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'es']
- src_constituents: {'eng'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt
- src_alpha3: eng
- tgt_alpha3: spa
- short_pair: en-es
- chrF2_score: 0.721
- bleu: 54.9
- brevity_penalty: 0.978
- ref_len: 77311.0
- src_name: English
- tgt_name: Spanish
- train_date: 2020-08-18 00:00:00
- src_alpha2: en
- tgt_alpha2: es
- prefer_old: False
- long_pair: eng-spa
- helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82
- transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9
- port_machine: brutasse
- port_time: 2020-08-24-18:20 |
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
],
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},
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}
}
} | 13 | 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 5547 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1109,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': None, 'do_lower_case': False}) with Transformer model: T5EncoderModel
(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 --> |
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 15 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mt5-finetuned-en-ar
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
args: arabic
metrics:
- name: Rouge1
type: rouge
value: 0.2824
---
<!-- 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-finetuned-en-ar
This model is a fine-tuned version of [ahmeddbahaa/mt5-small-finetuned-mt5-en](https://huggingface.co/ahmeddbahaa/mt5-small-finetuned-mt5-en) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2314
- Rouge1: 0.2824
- Rouge2: 0.0
- Rougel: 0.2902
- Rougelsum: 0.298
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.1685 | 1.0 | 4130 | 2.4262 | 0.0941 | 0.0235 | 0.1098 | 0.1098 |
| 2.686 | 2.0 | 8260 | 2.2853 | 0.2824 | 0.0 | 0.298 | 0.298 |
| 2.481 | 3.0 | 12390 | 2.2314 | 0.2824 | 0.0 | 0.2902 | 0.298 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Babysittingyoda/DialoGPT-small-familyguy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
}
} | 13 | null | ---
language:
- hi
- en
- multilingual
license: cc-by-4.0
tags:
- hi
- en
- codemix
datasets:
- L3Cube-HingCorpus
---
## HingRoBERTa-Mixed
HingRoBERTa-Mixed is a Hindi-English code-mixed BERT model trained on roman + devanagari text. It is a xlm-RoBERTa model fine-tuned on mixed script L3Cube-HingCorpus.
<br>
[dataset link] (https://github.com/l3cube-pune/code-mixed-nlp)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2204.08398)
```
@inproceedings{nayak-joshi-2022-l3cube,
title = "{L}3{C}ube-{H}ing{C}orpus and {H}ing{BERT}: A Code Mixed {H}indi-{E}nglish Dataset and {BERT} Language Models",
author = "Nayak, Ravindra and Joshi, Raviraj",
booktitle = "Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.wildre-1.2",
pages = "7--12",
}
``` |
Barbarameerr/Barbara | [] | null | {
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} | 0 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "Täkäläinen sanomalehdistö [MASK] erit - täin"
- text: "Det vore [MASK] häller nödvändigt att be"
- text: "Comme, à cette époque [MASK] était celle de la"
- text: "In [MASK] an atmosphärischen Nahrungsmitteln"
---
# hmBERT: Historical Multilingual Language Models for Named Entity Recognition
More information about our hmBERT model can be found in our new paper:
["hmBERT: Historical Multilingual Language Models for Named Entity Recognition"](https://arxiv.org/abs/2205.15575).
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtered)
| French | [Europeana](http://www.europeana-newspapers.eu/) | 11-31GB (filtered)
| English | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered)
| Finnish | [Europeana](http://www.europeana-newspapers.eu/) | 1.2GB
| Swedish | [Europeana](http://www.europeana-newspapers.eu/) | 1.1GB
# Corpora Stats
## German Europeana Corpus
We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size
and use less-noisier data:
| OCR confidence | Size
| -------------- | ----
| **0.60** | 28GB
| 0.65 | 18GB
| 0.70 | 13GB
For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:
## French Europeana Corpus
Like German, we use different ocr confidence thresholds:
| OCR confidence | Size
| -------------- | ----
| 0.60 | 31GB
| 0.65 | 27GB
| **0.70** | 27GB
| 0.75 | 23GB
| 0.80 | 11GB
For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:
## British Library Corpus
Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). Stats incl. year filtering:
| Years | Size
| ----------------- | ----
| ALL | 24GB
| >= 1800 && < 1900 | 24GB
We use the year filtered variant. The following plot shows a tokens per year distribution:
## Finnish Europeana Corpus
| OCR confidence | Size
| -------------- | ----
| 0.60 | 1.2GB
The following plot shows a tokens per year distribution:
## Swedish Europeana Corpus
| OCR confidence | Size
| -------------- | ----
| 0.60 | 1.1GB
The following plot shows a tokens per year distribution:
## All Corpora
The following plot shows a tokens per year distribution of the complete training corpus:
# Multilingual Vocab generation
For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB.
The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:
| Language | Size
| -------- | ----
| German | 10GB
| French | 10GB
| English | 10GB
| Finnish | 9.5GB
| Swedish | 9.7GB
We then calculate the subword fertility rate and portion of `[UNK]`s over the following NER corpora:
| Language | NER corpora
| -------- | ------------------
| German | CLEF-HIPE, NewsEye
| French | CLEF-HIPE, NewsEye
| English | CLEF-HIPE
| Finnish | NewsEye
| Swedish | NewsEye
Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:
| Language | Subword fertility | Unknown portion
| -------- | ------------------ | ---------------
| German | 1.43 | 0.0004
| French | 1.25 | 0.0001
| English | 1.25 | 0.0
| Finnish | 1.69 | 0.0007
| Swedish | 1.43 | 0.0
Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:
| Language | Subword fertility | Unknown portion
| -------- | ------------------ | ---------------
| German | 1.31 | 0.0004
| French | 1.16 | 0.0001
| English | 1.17 | 0.0
| Finnish | 1.54 | 0.0007
| Swedish | 1.32 | 0.0
# Final pretraining corpora
We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:
| Language | Size
| -------- | ----
| German | 28GB
| French | 27GB
| English | 24GB
| Finnish | 27GB
| Swedish | 27GB
Total size is 130GB.
# Pretraining
Details about the pretraining are coming soon.
# Acknowledgments
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as
TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗 |
Barleysack/AERoberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
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}
}
} | 7 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: xlnet-base-cased
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. -->
# xlnet-base-cased
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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16530, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.12.0
|
BatuhanYilmaz/marian-finetuned-kde4-en-to-fr | [] | null | {
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} | 0 | null | The main objective of this project is to create a chatbot that could be used as an application for stress relief for people who feel lonely and need emotional support. This project mainly focuses on creating an environment for people who are distressed and need a companion to talk or chat with.
Our main objective is to create a chatbot that could assist in helping the process of psychological therapy to people who are at the first stage of mental illness and loneliness. This project is mainly designed to create a chatbot that could answer basic questions that could answer back based on the input datasets that are present and also collect the data from the questions asked for which the answers could be complicated and suggest a therapist for them if any question is beyond the dataset that it has been asked.
We are using AWS sagemaker and hugging face for this project as a platform to create the chatbot. We will be focusing more on the BERT Machine learning techniques for natural – language processing and pre-training on the data.
|
Baybars/debateGPT | [] | null | {
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}
} | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: ''
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. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9938
- Wer: 0.9745
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9301 | 2.24 | 500 | 4.6291 | 0.9601 |
| 4.4562 | 4.48 | 1000 | 4.3604 | 0.9608 |
| 3.8356 | 6.73 | 1500 | 4.0728 | 0.9530 |
| 3.2716 | 8.97 | 2000 | 3.9938 | 0.9745 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"prefix": "summarize: "
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"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,225 | null | ---
language: pt
datasets:
- CORAA
- common_voice
- mls
- cetuc
- voxforge
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Alef Iury XLSR Wav2Vec2 Large 53 Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test CORAA WER
type: wer
value: 24.89%
---
# Wav2vec 2.0 trained with CORAA Portuguese Dataset and Open Portuguese Datasets
This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following datasets:
- [CORAA dataset](https://github.com/nilc-nlp/CORAA)
- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz).
- [Multilingual Librispeech (MLS)](http://www.openslr.org/94/).
- [VoxForge](http://www.voxforge.org/).
- [Common Voice 6.1](https://commonvoice.mozilla.org/pt).
## Repository
The repository that implements the model to be trained and tested is avaible [here](https://github.com/alefiury/SE-R_2022_Challenge_Wav2vec2). |
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
] | feature-extraction | {
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}
} | 106 | null | ---
license: apache-2.0
language: "es"
tags:
- generated_from_trainer
- sentiment
- emotion
- suicide
- depresión
- suicidio
- español
- es
- spanish
- depression
widget:
- text: "La vida no merece la pena"
example_title: "Ejemplo 1"
- text: "Para vivir así lo mejor es estar muerto"
example_title: "Ejemplo 2"
- text: "me siento triste por no poder viajar"
example_title: "Ejemplo 3"
- text: "Quiero terminar con todo"
example_title: "Ejemplo 4"
- text: "Disfruto de la vista"
example_title: "Ejemplo 5"
metrics:
- accuracy
model-index:
- name: electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
results: []
---
# electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas
El presente modelo se encentra basado en una versión mejorada de [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator), y con el uso de la base de datos [hackathon-pln-es/comentarios_depresivos](https://huggingface.co/datasets/hackathon-pln-es/comentarios_depresivos).
Siendo de esta manera los resultados obtenidos en la evaluación del modelo:
- Pérdida 0.0458
- Precisión: 0.9916
## Autores
- Danny Vásquez
- César Salazar
- Alexis Cañar
- Yannela Castro
- Daniel Patiño
## Descripción del Modelo
electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas es un modelo Transformers pre-entrenado bajo un largo corpus de comentarios obtenidos de REDDIT traducidos al español, con el fin de poder predecir si un comentario tiene una tendencia suicida en base al contexto. Por ende, recibirá una ENTRADA en la cuál se ingresará el texto a comprobar, para posteriormente obtener como única SALIDA de igual manera dos posibles opciones: “Suicida” o “No Suicida”.
## Motivación
Siendo la principal inspiración del modelo que sea utilizado para futuros proyectos que busquen detectar los casos de depresión a tiempo mediante el procesamiento del lenguaje natural, para poder prevenir los casos de suicido en niños, jóvenes y adultos.
## ¿Cómo usarlo?
El modelo puede ser utilizado de manera directa mediante la importación de la librería pipeline de transformers:
```python
>>> from transformers import pipeline
>>> model_name= 'hackathon-pln-es/electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas'
>>> cls= pipeline("text-classification", model=model_name)
>>> cls(“Estoy feliz”)[0]['label']
[{'resultado': "No Suicida"
}]
>>> cls(“Quiero acabar con todo”)[0]['label']
[{'resultado': " Suicida"
}]
```
## Proceso de entrenamiento
### Datos de entrenamiento
Como se declaró anteriormente, el modelo se pre-entrenó basándose en la base de datos [comentarios_depresivos]( https://huggingface.co/datasets/hackathon-pln-es/comentarios_depresivos), el cuál posee una cantidad de 192 347 filas de datos para el entrenamiento, 33 944 para las pruebas y 22630 para la validación.
### Hiper parámetros de entrenamiento
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- lr_scheduler_type: linear
- num_epochs: 15
### Resultados del entrenamiento
| Pérdida_entrenamiento | Epoch | Pérdida_Validación | Presición |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.161100 | 1.0 | 0.133057 | 0.952718 |
| 0.134500 | 2.0 | 0.110966 | 0.960804 |
| 0.108500 | 3.0 | 0.086417 | 0.970835 |
| 0.099400 | 4.0 | 0.073618 | 0.974856 |
| 0.090500 | 5.0 | 0.065231 | 0.979629 |
| 0.080700 | 6.0 | 0.060849 | 0.982324 |
| 0.069200 | 7.0 | 0.054718 | 0.986125 |
| 0.060400 | 8.0 | 0.051153 | 0.985948 |
| 0.048200 | 9.0 | 0.045747 | 0.989748 |
| 0.045500 | 10.0 | 0.049992 | 0.988069 |
| 0.043400 | 11.0 | 0.046325 | 0.990234 |
| 0.034300 | 12.0 | 0.050746 | 0.989792 |
| 0.032900 | 13.0 | 0.043434 | 0.991737 |
| 0.028400 | 14.0 | 0.045003 | 0.991869 |
| 0.022300 | 15.0 | 0.045819 | 0.991648 |
### Versiones del Framework
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
## Citación BibTeX
```bibtex
@article{ccs_2022,
author = {Danny Vásquez and
César Salazar and
Alexis Cañar and
Yannela Castro and
Daniel Patiño},
title = {Modelo Electricidad-small-discriminator-finetuned-clasificacion-comentarios-suicidas},
journal = {Huggingface},
year = {2022},
}
```
<h3>Visualizar en GRADIO:</h3>
<a href="https://huggingface.co/spaces/hackathon-pln-es/clasificador-comentarios-suicidas">
<img width="300px" src="https://hf.space/embed/hackathon-pln-es/clasificador-comentarios-suicidas/static/img/logo.svg">
</a>
---
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base_toy_train_data_random_noise_0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base_toy_train_data_random_noise_0.1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9263
- Wer: 0.7213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 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: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1296 | 2.1 | 250 | 3.5088 | 1.0 |
| 3.0728 | 4.2 | 500 | 3.1694 | 1.0 |
| 1.8686 | 6.3 | 750 | 1.3414 | 0.9321 |
| 1.1241 | 8.4 | 1000 | 1.0196 | 0.8321 |
| 0.8704 | 10.5 | 1250 | 0.9387 | 0.7962 |
| 0.6734 | 12.6 | 1500 | 0.9309 | 0.7640 |
| 0.5832 | 14.7 | 1750 | 0.9329 | 0.7346 |
| 0.5207 | 16.8 | 2000 | 0.9060 | 0.7247 |
| 0.4857 | 18.9 | 2250 | 0.9263 | 0.7213 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/mkobach-naval-shaneaparrish/1648339620049/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/1374075536595505154/1_1jV_AF_400x400.png')">
</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/1253758424292171778/48gD7Hne_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/1256841238298292232/ycqwaMI2_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">Matthew Kobach & Shane Parrish & Naval</div>
<div style="text-align: center; font-size: 14px;">@mkobach-naval-shaneaparrish</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 Matthew Kobach & Shane Parrish & Naval.
| Data | Matthew Kobach | Shane Parrish | Naval |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3197 | 3249 |
| Retweets | 135 | 102 | 181 |
| Short tweets | 444 | 147 | 617 |
| Tweets kept | 2669 | 2948 | 2451 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17cy2tt4/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 @mkobach-naval-shaneaparrish's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zkb00dh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zkb00dh/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/mkobach-naval-shaneaparrish')
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: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- YXHugging/autotrain-data-xlm-roberta-base-reviews
co2_eq_emissions: 999.5670927087938
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 672119801
- CO2 Emissions (in grams): 999.5670927087938
## Validation Metrics
- Loss: 0.9767692685127258
- Accuracy: 0.5738333333333333
- Macro F1: 0.5698748846905103
- Micro F1: 0.5738333333333333
- Weighted F1: 0.5698748846905102
- Macro Precision: 0.5734242161804903
- Micro Precision: 0.5738333333333333
- Weighted Precision: 0.5734242161804902
- Macro Recall: 0.5738333333333333
- Micro Recall: 0.5738333333333333
- Weighted Recall: 0.5738333333333333
## 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/YXHugging/autotrain-xlm-roberta-base-reviews-672119801
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119801", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119801", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
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} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-russian
results: []
widget:
- src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3
---
<!-- 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-russian
#
модель для распознания аудио. результаты модели можно потом прогнать через мою сеть исправления текстов UrukHan/t5-russian-spell
<table border="0">
<tr>
<td><b style="font-size:30px">Output wav2vec2</b></td>
<td><b style="font-size:30px">Output spell correcor</b></td>
</tr>
<tr>
<td>ывсем привет выныканалетоп армии и это двадцать пятый день спец операций на украине ет самый главной новости российские военные ракетами кинжалы калибр уничтожили крупную военную топливную базу украины ракетным ударом по населенному пункту под жетамиром уничтжены более стаукраинских военных в две тысячи двадцать втором году</td>
<td>Всем привет! Вы в курсе новостей от армии. И это 25 день спецопераций на Украине. Есть самые главные новости. Российские военные ракетами «Кинжалы» и «Кинжалы» калибра уничтожили крупную военную топливную базу Украины. Ракетным ударом по населенному пункту под Жетамиром уничтожены более ста украинских военных в 2022г.</td>
</tr>
</table>
---
Загрузите аудиофайл в формате wav для распознования. Результат можно откорректировать в другой моей сети. которая исправляет ошибки, расставляет знаки припинания и исправляет цифры. https://huggingface.co/UrukHan/t5-russian-spell
#
---
# Запуск сети пример в колабе https://colab.research.google.com/drive/1dVZvccYJq02hmEsapWgmuJ-pLdezFnn1?usp=sharing
#
```python
from transformers import AutoModelForCTC, Wav2Vec2Processor
model = AutoModelForCTC.from_pretrained("UrukHan/wav2vec2-russian")
processor = Wav2Vec2Processor.from_pretrained("UrukHan/wav2vec2-russian")
def map_to_result(batch):
with torch.no_grad():
input_values = torch.tensor(batch["input_values"]).unsqueeze(0) #, device="cuda"
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
batch = processor.batch_decode(pred_ids)[0]
return batch
map_to_result()
```
#
---
# Тренировка модели с обработкой данных и созданием датасета разобрать можете в колабе:
# https://colab.research.google.com/drive/1zkCA2PtKxD2acqLr55USh35OomoOwOhm?usp=sharing |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_data
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-xlsr-53_toy_train_data
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6357
- Wer: 0.5496
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6073 | 2.1 | 250 | 3.5111 | 1.0 |
| 3.0828 | 4.2 | 500 | 3.5133 | 1.0 |
| 1.9969 | 6.3 | 750 | 1.3924 | 0.9577 |
| 0.9279 | 8.4 | 1000 | 0.8378 | 0.7243 |
| 0.6692 | 10.5 | 1250 | 0.7367 | 0.6394 |
| 0.5273 | 12.6 | 1500 | 0.6703 | 0.5907 |
| 0.4314 | 14.7 | 1750 | 0.6594 | 0.5718 |
| 0.3809 | 16.8 | 2000 | 0.6138 | 0.5559 |
| 0.3934 | 18.9 | 2250 | 0.6357 | 0.5496 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/DaBlank | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 4 | null | ---
tags:
- image-captioning
- image-to-text
model-index:
- name: image-caption-generator
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. -->
# Image-caption-generator
This model is trained on [Flickr8k](https://www.kaggle.com/datasets/nunenuh/flickr8k) dataset to generate captions given an image.
It achieves the following results on the evaluation set:
- eval_loss: 0.2536
- eval_runtime: 25.369
- eval_samples_per_second: 63.818
- eval_steps_per_second: 8.002
- epoch: 4.0
- step: 3236
# Running the model using transformers library
1. Load the pre-trained model from the model hub
```python
from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
import torch
from PIL import Image
model_name = "bipin/image-caption-generator"
# load model
model = VisionEncoderDecoderModel.from_pretrained(model_name)
feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
```
2. Load the image for which the caption is to be generated
```python
img_name = "flickr_data.jpg"
img = Image.open(img_name)
if img.mode != 'RGB':
img = img.convert(mode="RGB")
```
3. Pre-process the image
```python
pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
```
4. Generate the caption
```python
max_length = 128
num_beams = 4
# get model prediction
output_ids = model.generate(pixel_values, num_beams=num_beams, max_length=max_length)
# decode the generated prediction
preds = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(preds)
```
## Training procedure
The procedure used to train this model can be found [here](https://bipinkrishnan.github.io/ml-recipe-book/image_captioning.html).
### 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: 5
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 10 | null | ---
license: apache-2.0
---
# xlm-roberta-base-faq-extractor |
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 4 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pneumonia-bielefeld-dl-course
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8456632494926453
---
# pneumonia-bielefeld-dl-course
This registry contains the model for making pneumonia predictions and was prepared for
Bielefeld University Deep Learning course homework.
The code used for this implementation mostly comes from here: https://github.com/nateraw/huggingpics it was a ready pipeline for model fine-tuning with huggingface and PyTorch Lightning for another dataset.
|
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} | 0 | null | ---
language: hu
thumbnail:
tags:
- question-answering
- bert
widget:
- text: "Melyik folyó szeli ketté Budapestet?"
context: "Magyarország fővárosát, Budapestet a Duna folyó szeli ketté. A XIX. században épült Lánchíd a dimbes-dombos budai oldalt köti össze a sík Pesttel. A Várdomb oldalában futó siklóval juthatunk fel a budai Óvárosba, ahol a Budapesti Történeti Múzeum egészen a római időkig visszavezetve mutatja be a városi életet. A Szentháromság tér ad otthont a XIII. századi Mátyás-templomnak és a Halászbástya lőtornyainak, amelyekből messzire ellátva gyönyörködhetünk a városban."
- text: "Mivel juthatunk fel az Óvárosba?"
context: "Magyarország fővárosát, Budapestet a Duna folyó szeli ketté. A XIX. században épült Lánchíd a dimbes-dombos budai oldalt köti össze a sík Pesttel. A Várdomb oldalában futó siklóval juthatunk fel a budai Óvárosba, ahol a Budapesti Történeti Múzeum egészen a római időkig visszavezetve mutatja be a városi életet. A Szentháromság tér ad otthont a XIII. századi Mátyás-templomnak és a Halászbástya lőtornyainak, amelyekből messzire ellátva gyönyörködhetünk a városban."
---
## MODEL DESCRIPTION
huBERT base model (cased) fine-tuned on SQuAD v1
- huBert model + Tokenizer: https://huggingface.co/SZTAKI-HLT/hubert-base-cc
- Hungarian SQUAD v1 dataset: Machine Translated SQuAD dataset (Google Translate API)
- This is a demo model. Date of publication: 2022.03.27.
## Model in action
- Fast usage with pipelines:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="mcsabai/huBert-fine-tuned-hungarian-squadv1",
tokenizer="mcsabai/huBert-fine-tuned-hungarian-squadv1"
)
predictions = qa_pipeline({
'context': "Anita vagyok és Budapesten élek már több mint 4 éve.",
'question': "Hol lakik Anita?"
})
print(predictions)
# output:
# {'score': 0.9892364144325256, 'start': 16, 'end': 26, 'answer': 'Budapesten'}
```
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-sentiment-mesd
results: []
---
# wav2vec2-base-finetuned-sentiment-mesd
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the [MESD](https://huggingface.co/hackathon-pln-es/MESD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5729
- Accuracy: 0.8308
## Model description
This model was trained to classify underlying sentiment of Spanish audio/speech.
## 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: 1.25e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.5729 | 0.8308 |
| No log | 2.0 | 14 | 0.6577 | 0.8 |
| 0.1602 | 3.0 | 21 | 0.7055 | 0.8 |
| 0.1602 | 4.0 | 28 | 0.8696 | 0.7615 |
| 0.1602 | 5.0 | 35 | 0.6807 | 0.7923 |
| 0.1711 | 6.0 | 42 | 0.7303 | 0.7923 |
| 0.1711 | 7.0 | 49 | 0.7028 | 0.8077 |
| 0.1711 | 8.0 | 56 | 0.7368 | 0.8 |
| 0.1608 | 9.0 | 63 | 0.7190 | 0.7923 |
| 0.1608 | 10.0 | 70 | 0.6913 | 0.8077 |
| 0.1608 | 11.0 | 77 | 0.7047 | 0.8077 |
| 0.1753 | 12.0 | 84 | 0.6801 | 0.8 |
| 0.1753 | 13.0 | 91 | 0.7208 | 0.7769 |
| 0.1753 | 14.0 | 98 | 0.7458 | 0.7846 |
| 0.203 | 15.0 | 105 | 0.6494 | 0.8077 |
| 0.203 | 16.0 | 112 | 0.6256 | 0.8231 |
| 0.203 | 17.0 | 119 | 0.6788 | 0.8 |
| 0.1919 | 18.0 | 126 | 0.6757 | 0.7846 |
| 0.1919 | 19.0 | 133 | 0.6859 | 0.7846 |
| 0.1641 | 20.0 | 140 | 0.6832 | 0.7846 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.10.3
|
BigSalmon/GPTNeo350MInformalToFormalLincoln4 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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} | 11 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-hindicone
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-hindicone
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5small4-squad1024
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. -->
# t5small4-squad1024
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: tpu
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.9.0+cu102
- Tokenizers 0.11.6
|
BigSalmon/InformalToFormalLincoln15 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 11 | null | ---
license: apache-2.0
language: "en"
tags:
- bag-of-words
- dense-passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# ColBERTer (Dim: 32) for Passage Retrieval
If you want to know more about our ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉
For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter
## Limitations & Bias
- The model is only trained on english text.
- The model inherits social biases from both DistilBERT and MSMARCO.
- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
## Citation
If you use our model checkpoint please cite our work as:
```
@article{Hofstaetter2022_colberter,
author = {Sebastian Hofst{\"a}tter and Omar Khattab and Sophia Althammer and Mete Sertkan and Allan Hanbury},
title = {Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction},
publisher = {arXiv},
url = {https://arxiv.org/abs/2203.13088},
doi = {10.48550/ARXIV.2203.13088},
year = {2022},
}
``` |
BigSalmon/InformalToFormalLincoln17 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 12 | null | ---
license: apache-2.0
language: "en"
tags:
- bag-of-words
- dense-passage-retrieval
- knowledge-distillation
datasets:
- ms_marco
---
# Uni-ColBERTer (Dim: 1) for Passage Retrieval
If you want to know more about our (Uni-)ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉
For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter
## Limitations & Bias
- The model is only trained on english text.
- The model inherits social biases from both DistilBERT and MSMARCO.
- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
## Citation
If you use our model checkpoint please cite our work as:
```
@article{Hofstaetter2022_colberter,
author = {Sebastian Hofst{\"a}tter and Omar Khattab and Sophia Althammer and Mete Sertkan and Allan Hanbury},
title = {Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction},
publisher = {arXiv},
url = {https://arxiv.org/abs/2203.13088},
doi = {10.48550/ARXIV.2203.13088},
year = {2022},
}
``` |
BigSalmon/InformalToFormalLincoln19 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 11 | null | ---
language:
- es
tags:
- sentence similarity # Example: audio
- passage retrieval # Example: automatic-speech-recognition
datasets:
- squad_es
- PlanTL-GOB-ES/SQAC
- IIC/bioasq22_es
metrics:
- eval_loss: 0.010779764448327261
- eval_accuracy: 0.9982682224158297
- eval_f1: 0.9446059155411182
- average_rank: 0.11728500598392888
model-index:
- name: dpr-spanish-passage_encoder-allqa-base
results:
- task:
type: text similarity # Required. Example: automatic-speech-recognition
name: text similarity # Optional. Example: Speech Recognition
dataset:
type: squad_es # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: squad_es # Required. Example: Common Voice zh-CN
args: es # Optional. Example: zh-CN
metrics:
- type: loss
value: 0.010779764448327261
name: eval_loss
- type: accuracy
value: 0.9982682224158297
name: accuracy
- type: f1
value: 0.9446059155411182
name: f1
- type: avgrank
value: 0.11728500598392888
name: avgrank
---
[Dense Passage Retrieval](https://arxiv.org/abs/2004.04906)-DPR is a set of tools for performing State of the Art open-domain question answering. It was initially developed by Facebook and there is an [official repository](https://github.com/facebookresearch/DPR). DPR is intended to retrieve the relevant documents to answer a given question, and is composed of 2 models, one for encoding passages and other for encoding questions. This concrete model is the one used for encoding passages.
With this and the [question encoder model](https://huggingface.co/avacaondata/dpr-spanish-question_encoder-allqa-base) we introduce the best passage retrievers in Spanish up to date (to the best of our knowledge), improving over the [previous model we developed](https://huggingface.co/IIC/dpr-spanish-question_encoder-squades-base), by training it for longer and with more data.
Regarding its use, this model should be used to vectorize a question that enters in a Question Answering system, and then we compare that encoding with the encodings of the database (encoded with [the passage encoder](https://huggingface.co/avacaondata/dpr-spanish-passage_encoder-squades-base)) to find the most similar documents , which then should be used for either extracting the answer or generating it.
For training the model, we used a collection of Question Answering datasets in Spanish:
- the Spanish version of SQUAD, [SQUAD-ES](https://huggingface.co/datasets/squad_es)
- [SQAC- Spanish Question Answering Corpus](https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC)
- [BioAsq22-ES](https://huggingface.co/datasets/IIC/bioasq22_es) - we translated this last one by using automatic translation with Transformers.
With this complete dataset we created positive and negative examples for the model (For more information look at [the paper](https://arxiv.org/abs/2004.04906) to understand the training process for DPR). We trained for 25 epochs with the same configuration as the paper. The [previous DPR model](https://huggingface.co/IIC/dpr-spanish-passage_encoder-squades-base) was trained for only 3 epochs with about 60% of the data.
Example of use:
```python
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
model_str = "IIC/dpr-spanish-passage_encoder-allqa-base"
tokenizer = DPRContextEncoderTokenizer.from_pretrained(model_str)
model = DPRContextEncoder.from_pretrained(model_str)
input_ids = tokenizer("Usain Bolt ganó varias medallas de oro en las Olimpiadas del año 2012", return_tensors="pt")["input_ids"]
embeddings = model(input_ids).pooler_output
```
The full metrics of this model on the evaluation split of SQUADES are:
```
eval_loss: 0.010779764448327261
eval_acc: 0.9982682224158297
eval_f1: 0.9446059155411182
eval_acc_and_f1: 0.9714370689784739
eval_average_rank: 0.11728500598392888
```
And the classification report:
```
precision recall f1-score support
hard_negative 0.9991 0.9991 0.9991 1104999
positive 0.9446 0.9446 0.9446 17547
accuracy 0.9983 1122546
macro avg 0.9719 0.9719 0.9719 1122546
weighted avg 0.9983 0.9983 0.9983 1122546
```
### Contributions
Thanks to [@avacaondata](https://huggingface.co/avacaondata), [@alborotis](https://huggingface.co/alborotis), [@albarji](https://huggingface.co/albarji), [@Dabs](https://huggingface.co/Dabs), [@GuillemGSubies](https://huggingface.co/GuillemGSubies) for adding this model. |
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
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}
}
} | 10 | null | ---
tags:
- conversational
---
# Lavenza DialoGPT Model |
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_data_augmented
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-xlsr-53_toy_train_data_augmented
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5016
- Wer: 0.4656
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.418 | 1.05 | 250 | 3.4171 | 1.0 |
| 3.0886 | 2.1 | 500 | 3.4681 | 1.0 |
| 2.9422 | 3.15 | 750 | 2.6151 | 1.0 |
| 1.3195 | 4.2 | 1000 | 0.8789 | 0.7739 |
| 0.9154 | 5.25 | 1250 | 0.6364 | 0.6518 |
| 0.6519 | 6.3 | 1500 | 0.5682 | 0.5949 |
| 0.5622 | 7.35 | 1750 | 0.5273 | 0.5625 |
| 0.4965 | 8.4 | 2000 | 0.4891 | 0.5283 |
| 0.4283 | 9.45 | 2250 | 0.5018 | 0.5260 |
| 0.4019 | 10.5 | 2500 | 0.5016 | 0.5006 |
| 0.3585 | 11.55 | 2750 | 0.5047 | 0.5003 |
| 0.3275 | 12.6 | 3000 | 0.5148 | 0.4866 |
| 0.3427 | 13.65 | 3250 | 0.5035 | 0.4786 |
| 0.3229 | 14.7 | 3500 | 0.4855 | 0.4768 |
| 0.3332 | 15.75 | 3750 | 0.5040 | 0.4769 |
| 0.2861 | 16.81 | 4000 | 0.5138 | 0.4669 |
| 0.3029 | 17.86 | 4250 | 0.5133 | 0.4670 |
| 0.2633 | 18.91 | 4500 | 0.5063 | 0.4637 |
| 0.2621 | 19.96 | 4750 | 0.5016 | 0.4656 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 9 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ikram54/autotrain-data-harassement
co2_eq_emissions: 2.6332836871905054
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 675420038
- CO2 Emissions (in grams): 2.6332836871905054
## Validation Metrics
- Loss: 0.8747465014457703
- Accuracy: 0.7085201793721974
- Macro F1: 0.579743989078862
- Micro F1: 0.7085201793721974
- Weighted F1: 0.6913786522271296
- Macro Precision: 0.5669375905888698
- Micro Precision: 0.7085201793721974
- Weighted Precision: 0.6760144007300164
- Macro Recall: 0.5940655209452201
- Micro Recall: 0.7085201793721974
- Weighted Recall: 0.7085201793721974
## 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/ikram54/autotrain-harassement-675420038
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ikram54/autotrain-harassement-675420038", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
BigSalmon/NEO125InformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- few_nerd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: few_nerd
type: few_nerd
args: supervised
metrics:
- name: Precision
type: precision
value: 0.7377633209417596
- name: Recall
type: recall
value: 0.7817648386368765
- name: F1
type: f1
value: 0.7591269959856158
- name: Accuracy
type: accuracy
value: 0.9383331648547562
---
<!-- 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. -->
# Cybonto-distilbert-base-uncased-finetuned-ner-v0.1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1930
- Precision: 0.7378
- Recall: 0.7818
- F1: 0.7591
- Accuracy: 0.9383
## 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: 36
- eval_batch_size: 36
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2001 | 1.0 | 3661 | 0.1954 | 0.7244 | 0.7750 | 0.7488 | 0.9360 |
| 0.1717 | 2.0 | 7322 | 0.1898 | 0.7392 | 0.7767 | 0.7575 | 0.9384 |
| 0.1485 | 3.0 | 10983 | 0.1930 | 0.7378 | 0.7818 | 0.7591 | 0.9383 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BigSalmon/Points2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
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}
} | 12 | 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: 0.8591260810195721
---
<!-- 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: 0.1352
- F1: 0.8591
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.257 | 1.0 | 525 | 0.1512 | 0.8302 |
| 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 |
| 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BigSalmon/Robertsy | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 4 | null | ---
pipeline_tag: sentence-similarity
license: apache-2.0
language:
- it
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# sentence-BERTino
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. It was trained on a dataset made from question/context pairs ([squad-it](https://github.com/crux82/squad-it)) and tags/news-article pairs (via scraping).
If you like this project, consider supporting me with a cup of coffee! 🤖✨🌞
[](https://bmc.link/edoardofederici)
## 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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
model = SentenceTransformer('efederici/sentence-BERTino')
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 = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-BERTino')
model = AutoModel.from_pretrained('efederici/sentence-BERTino')
# 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)
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
``` |
BigSalmon/Rowerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 4 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/baguioni-elonmusk-jacobe/1648421056394/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/1503591435324563456/foUrqiEw_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/1025926108984664064/2ZHTSIof_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/1506662013707046914/hVtCPrPL_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">Elon Musk & Rowel Atienza & baguio</div>
<div style="text-align: center; font-size: 14px;">@baguioni-elonmusk-jacobe</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 Elon Musk & Rowel Atienza & baguio.
| Data | Elon Musk | Rowel Atienza | baguio |
| --- | --- | --- | --- |
| Tweets downloaded | 1621 | 100 | 3012 |
| Retweets | 69 | 29 | 1090 |
| Short tweets | 520 | 4 | 527 |
| Tweets kept | 1032 | 67 | 1395 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xuj1tda/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 @baguioni-elonmusk-jacobe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3fpkbu3i/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/baguioni-elonmusk-jacobe')
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)
|
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": 2,
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"prefix": "summarize: "
},
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},
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"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/jacobe/1648422127637/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/1025926108984664064/2ZHTSIof_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">Rowel Atienza</div>
<div style="text-align: center; font-size: 14px;">@jacobe</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 Rowel Atienza.
| Data | Rowel Atienza |
| --- | --- |
| Tweets downloaded | 100 |
| Retweets | 29 |
| Short tweets | 4 |
| Tweets kept | 67 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1uzq4b7w/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 @jacobe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ouo6sis/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/jacobe')
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)
|
BigSalmon/TS3 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
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"T5ForConditionalGeneration"
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} | 7 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln32")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln32")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
``` |
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
}
} | 16 | null | ---
tags:
- conversational
---
# harry Potter DialoGPT Model |
Bimal/my_bot_model | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3466
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2739 | 1.0 | 4118 | 1.2801 |
| 1.0001 | 2.0 | 8236 | 1.2823 |
| 0.8484 | 3.0 | 12354 | 1.3466 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract
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. -->
# Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract
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:
- eval_loss: 0.0231
- eval_precision: 0.7448
- eval_recall: 0.75
- eval_f1: 0.7474
- eval_accuracy: 0.9942
- eval_runtime: 61.7618
- eval_samples_per_second: 27.201
- eval_steps_per_second: 3.4
- epoch: 3.0
- step: 5670
## 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
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Bloodwarrior/Chikfalay | [] | null | {
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}
} | 0 | null | ---
language:
- en
- code
- multilingual
license: mpl-2.0
---
## PythonGPT
A GPT2-type neural network trained on 16 gigabytes of Pyhon scripts from scratch. It has 50 million parameters.
Made as a toy. |
Botjallu/DialoGPT-small-harrypotter | [] | null | {
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"model_type": null,
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2hindia
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. -->
# wav2vec2hindia
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BotterHax/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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}
}
} | 8 | null | ---
license: mit
---
Replicated [SPECTER model](https://huggingface.co/allenai/specter) based on w/o leakage training corpus with `seed=0`. See [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://arxiv.org/abs/2202.06671).
|
Branex/gpt-neo-2.7B | [] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/nsawaikar/1648454046318/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/1508184022052184064/yqLU6MxW_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">Nathan.eth</div>
<div style="text-align: center; font-size: 14px;">@nsawaikar</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 Nathan.eth.
| Data | Nathan.eth |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 336 |
| Short tweets | 621 |
| Tweets kept | 2293 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/pn1domem/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 @nsawaikar's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g9hqx5dx/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/nsawaikar')
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)
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
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}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnndm
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.417
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6854
- Rouge1: 24.417
- Rouge2: 11.6924
- Rougel: 20.1756
- Rougelsum: 23.0414
- Gen Len: 18.9996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:-------:|:---------:|:-------:|
| 1.8522 | 1.0 | 35890 | 1.6854 | 24.417 | 11.6924 | 20.1756 | 23.0414 | 18.9996 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 10 | null | ---
language:
- is
- en
tags:
- translation
license: mit
---
# mBART 25 SentencePiece tokenizer
This tokenizer is used for Mideind's mBART translation models.
It is based on Facebooks mBART-25 SentencePiece model.
A language token from the original model has been replaced with "is_IS".
Usage example (for debugging):
```python
import sys
from transformers.models import mbart
MODEL_DIR = sys.argv[1]
tokenizer: mbart.MBartTokenizerFast = mbart.MBartTokenizerFast.from_pretrained(
MODEL_DIR, src_lang="en_XX"
)
is_lang_idx = tokenizer.convert_tokens_to_ids("is_IS")
model = mbart.MBartForConditionalGeneration.from_pretrained(MODEL_DIR)
test_sentence = "This is a test."
input_ids = tokenizer(test_sentence, return_tensors="pt")
print(input_ids)
outputs = model.generate(
**input_ids, decoder_start_token_id=is_lang_idx
)
print(outputs)
print(tokenizer.batch_decode(outputs))
``` |
Bryson575x/riceboi | [] | null | {
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}
} | 0 | null | ---
language:
- es
license: apache-2.0
tags:
- Text2Text Generation
- Inclusive Language
- Text Neutralization
- pytorch
datasets:
- hackathon-pln-es/neutral-es
metrics:
- sacrebleu
model-index:
- name: es_text_neutralizer
results:
- task:
type: Text2Text Generation
name: Neutralization of texts in Spanish
dataset:
type: hackathon-pln-es/neutral-es
name: neutral-es
metrics:
- type: sacrebleu
value: 0.96
name: sacrebleu # Optional. Example: Test WER
- type: bertscore # Required. Example: wer
value: 0.98
name: BertScoreF1 # Optional. Example: Test WER
- type: DiffBleu # Required. Example: wer
value: 0.35
name: DiffBleu # Optional. Example: Test WER
---
## Model objective
Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about people. The purpose of this collaboratively trained model is to create a solution that reinforces the UN objective of the gender equality.
Given any input, our model will generate a gender neutral sentence, correcting any non-inclusive expressions or words.
It's a straightforward and fast solution that creates a positive impact in the contemporary social panorama.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/2/29/Gender_equality_symbol_%28clipart%29.png" width="250"/>
</p>
By using gender inclusive models we can help reducing gender bias in a language corpus by, for instance, adding data augmentation and creating different examples
## Training and evaluation data
The data used for the model training has been created form a compilation of sources, obtained from a series of guidelines and manuals issued by Spanish Ministry of Health, Social Services and Equality in the matter of the usage of non-sexist language, stipulated in this linked [document:](https://www.inmujeres.gob.es/servRecursos/formacion/GuiasLengNoSexista/docs/Guiaslenguajenosexista_.pdf):
### Compiled sources
[Guía para un discurso igualitario en la universidad de alicante](https://ieg.ua.es/es/documentos/normativasobreigualdad/guia-para-un-discurso-igualitario-en-la-ua.pdf)
[Guía UC de Comunicación en Igualdad](<https://web.unican.es/unidades/igualdad/SiteAssets/igualdad/comunicacion-en-igualdad/guia%20comunicacion%20igualdad%20(web).pdf>)
[Buenas prácticas para el tratamiento del lenguaje en igualdad](https://e-archivo.uc3m.es/handle/10016/22811)
[Guía del lenguaje no sexista de la Universidad de Castilla-La Mancha](https://unidadigualdad.ugr.es/page/guiialenguajeuniversitarionosexista_universidaddecastillalamancha/!)
[Guía de Lenguaje Para el Ámbito Educativo](https://www.educacionyfp.gob.es/va/dam/jcr:8ce318fd-c8ff-4ad2-97b4-7318c27d1682/guialenguajeambitoeducativo.pdf)
[Guía para un uso igualitario y no sexista del lenguaje y dela imagen en la Universidad de Jaén](https://www.ujaen.es/servicios/uigualdad/sites/servicio_uigualdad/files/uploads/Guia_lenguaje_no_sexista.pdf)
[Guía de uso no sexista del vocabulario español](https://www.um.es/documents/2187255/2187763/guia-leng-no-sexista.pdf/d5b22eb9-b2e4-4f4b-82aa-8a129cdc83e3)
[Guía para el uso no sexista de la lengua castellana y de imágnes en la UPV/EHV](https://www.ehu.eus/documents/1734204/1884196/Guia_uso_no_sexista_EHU.pdf)
[Guía de lenguaje no sexista UNED](http://portal.uned.es/pls/portal/docs/PAGE/UNED_MAIN/LAUNIVERSIDAD/VICERRECTORADOS/GERENCIA/OFICINA_IGUALDAD/CONCEPTOS%20BASICOS/GUIA_LENGUAJE.PDF)
[COMUNICACIÓN AMBIENTAL CON PERSPECTIVA DE GÉNERO](https://cima.cantabria.es/documents/5710649/5729124/COMUNICACI%C3%93N+AMBIENTAL+CON+PERSPECTIVA+DE+G%C3%89NERO.pdf/ccc18730-53e3-35b9-731e-b4c43339254b)
[Recomendaciones para la utilización de lenguaje no sexista](https://www.csic.es/sites/default/files/guia_para_un_uso_no_sexista_de_la_lengua_adoptada_por_csic2.pdf)
[Estudio sobre lenguaje y contenido sexista en la Web](https://www.mujeresenred.net/IMG/pdf/Estudio_paginas_web_T-incluye_ok.pdf)
[Nombra.en.red. En femenino y en masculino](https://www.inmujeres.gob.es/areasTematicas/educacion/publicaciones/serieLenguaje/docs/Nombra_en_red.pdf)
## Model specs
This model is a fine-tuned version of [spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the data described below.
It achieves the following results on the evaluation set:
- 'eval_bleu': 93.8347,
- 'eval_f1': 0.9904,
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-04
- train_batch_size: 32
- seed: 42
- num_epochs: 10
- weight_decay: 0,01
## Metrics
For training, we used both Blue (sacrebleu implementation in HF) and BertScore. The first one, a standard in Machine Translation processes, has been added for ensuring robustness of the newly generated data, while the second one is kept for keeping the expected semantic similarity.
However, given the actual use case, we expect generated segments to be very close to input segments and to label segments in training. As an example, we can take the following:
inputSegment = 'De acuerdo con las informaciones anteriores , las alumnas se han quejado de la actitud de los profesores en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.'
expectedOutput (label) = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son los alumnos Juanju y Javi.'
actualOutput = 'De acuerdo con las informaciones anteriores, el alumnado se ha quejado de la actitud del profesorado en los exámenes finales. Los representantes estudiantiles son el alumnado Juanju y Javi.'
As you can see, segments are pretty similar. So, instead of measuring Bleu or BertScore here, we propose an alternate metric that would be DiffBleu:
$$DiffBleu = BLEU(actualOutput - inputSegment, labels - inputSegment)$$
Where the minuses as in set notation. This way, we also evaluate DiffBleu after the model has been trained.
## Team Members
- Fernando Velasco [(fermaat)](https://huggingface.co/fermaat)
- Cibeles Redondo [(CibelesR)](https://huggingface.co/CibelesR)
- Juan Julian Cea [(Juanju)](https://huggingface.co/Juanju)
- Magdalena Kujalowicz [(MacadellaCosta)](https://huggingface.co/MacadellaCosta)
- Javier Blasco [(javiblasco)](https://huggingface.co/javiblasco)
Enjoy! |
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