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IIC/marimari-r2r-mlsum | 6230c21c7210c1ab68f32fa56dc1c1f9d3cc27b6 | 2022-04-13T16:49:56.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"es",
"dataset:mlsum",
"arxiv:1907.12461",
"transformers",
"summarization",
"seq2seq",
"model-index",
"autotrain_compatible"
] | summarization | false | IIC | null | IIC/marimari-r2r-mlsum | 66 | 2 | transformers | 5,500 | ---
language:
- es
tags:
- summarization # Example: audio
- seq2seq # Example: automatic-speech-recognition
datasets:
- mlsum
metrics:
- rouge2
- rouge1
- rougel
- rougelsum
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: marimari-r2r-mlsum
results:
- task:
type: summarization # Required. Example: automatic-speech-recognition
name: abstractive summarization # Optional. Example: Speech Recognition
dataset:
type: mlsum # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: mlsum-es # Required. Example: Common Voice zh-CN
args: es # Optional. Example: zh-CN
metrics:
- type: rouge1 # Required. Example: wer
value: 28.7802 # Required. Example: 20.90
name: rouge1 # Optional. Example: Test WER
- type: rouge2
value: 10.6748
name: rouge2
- type: rougeL
value: 23.0447
name: rougeL
- type: rougeLsum
value: 23.4055
name: rougeLsum
---
<img src="https://huggingface.co/IIC/marimari-r2r-mlsum/resolve/main/marimariLogo.png"/>
This is a model for text summarization in Spanish. It has been trained on the spanish portion of [mlsum](https://huggingface.co/datasets/mlsum). For that, MariMari was created. It is called like that because it is an EncoderDecoder model built from Maria model, specifically, the [roberta model from the Maria Project](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne). For building the Encoder Decoder model, [this paper was followed](https://arxiv.org/abs/1907.12461), which has a [direct implementation in transformers](https://huggingface.co/docs/transformers/master/model_doc/encoder-decoder). As there are no natural encoder decoder models in Spanish, such as BART or T5, we decided to leverage the capacity of the Roberta model of the MarIA project, as it has shown great results on several NLU tasks, therefore it was natural to think it could perform well on NLG tasks when trained properly.
For tuning the hyperparameters of the model we used [Optuna](https://optuna.org/), with only 10 different trials and 7 initial random trials, as [the dataset chosen for training the model, mlsum](https://huggingface.co/datasets/mlsum) was huge. The set of hyperparameters used was the following:
```python
def hp_space(trial):
return {
"learning_rate": trial.suggest_float(
"learning_rate", 3e-5, 7e-5, log=True
),
"num_train_epochs": trial.suggest_categorical(
"num_train_epochs", [7]
),
"per_device_train_batch_size": trial.suggest_categorical(
"per_device_train_batch_size", [16]),
"per_device_eval_batch_size": trial.suggest_categorical(
"per_device_eval_batch_size", [32]),
"gradient_accumulation_steps": trial.suggest_categorical(
"gradient_accumulation_steps", [2, 4, 8]),
"warmup_steps": trial.suggest_categorical(
"warmup_steps", [50, 100, 500, 1000]
),
"weight_decay": trial.suggest_float(
"weight_decay", 0.0, 0.1
),
```
The reported results are on the test split of mlsum. As you can see, MariMari-r2r-mlsum works better for summarization on mlsum than the previous best model in this regard, [beto2beto](https://huggingface.co/LeoCordoba/beto2beto-mlsum). The complete metrics on test are:
```json
{"rouge1": 28.7802, "rouge2": 10.6748, "rougeL": 23.0447, "rougeLsum": 23.4055, "gen_len": 25.7803}
```
This model is really easy to use, and with the following lines of code you can just start summarizing your documents in Spanish:
```python
from transformers import EncoderDecoderModel, AutoTokenizer
text = "Hola esto es un ejemplo de texto a resumir. Poco hay que resumir aquí, pero es sólo de muestra."
tokenizer = AutoTokenizer.from_pretrained("IIC/marimari-r2r-mlsum")
model = EncoderDecoderModel.from_pretrained("IIC/marimari-r2r-mlsum")
input_ids = tokenizer(text, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)[0]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
```
### 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. |
questgen/all-mpnet-base-v2-feature-extraction-pipeline | f88ec83121852d920f89e717d4adedf760a94b57 | 2022-05-15T06:29:59.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | feature-extraction | false | questgen | null | questgen/all-mpnet-base-v2-feature-extraction-pipeline | 66 | null | sentence-transformers | 5,501 | ---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
license: apache-2.0
---
# all-mpnet-base-v2
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.
## 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('sentence-transformers/all-mpnet-base-v2')
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
import torch.nn.functional as F
#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('sentence-transformers/all-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
# 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
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 384 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** | |
zhifei/autotrain-chinese-title-summarization-1060936832 | e3884592b3c6f4f8b619888e41dad01dc14f9970 | 2022-06-30T12:23:58.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"unk",
"dataset:zhifei/autotrain-data-chinese-title-summarization",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | zhifei | null | zhifei/autotrain-chinese-title-summarization-1060936832 | 66 | null | transformers | 5,502 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zhifei/autotrain-data-chinese-title-summarization
co2_eq_emissions: 3.841483701875158
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060936832
- CO2 Emissions (in grams): 3.841483701875158
## Validation Metrics
- Loss: 0.5115200877189636
- Rouge1: 27.3016
- Rouge2: 10.4762
- RougeL: 27.3016
- RougeLsum: 27.1111
- Gen Len: 14.3619
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zhifei/autotrain-chinese-title-summarization-1060936832
``` |
gaunernst/bert-small-uncased | 10b42d998a29a4ac8b43c866cc1184cb4880cdd4 | 2022-07-02T07:20:15.000Z | [
"pytorch",
"bert",
"transformers",
"license:apache-2.0"
] | null | false | gaunernst | null | gaunernst/bert-small-uncased | 66 | null | transformers | 5,503 | ---
license: apache-2.0
---
|
yazinga/DialoGPT-medium-scout | 81a1b77c99e99f78cde1d2271d5f1c0f9decc015 | 2022-07-21T20:19:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | yazinga | null | yazinga/DialoGPT-medium-scout | 66 | null | transformers | 5,504 | ---
tags:
- conversational
---
# Scout DialoGPT Model |
HooshvareLab/gpt2-fa-comment | a11c3401aa46429b13184a6c94088bd43728c7fd | 2021-05-21T10:47:25.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"transformers",
"license:apache-2.0"
] | text-generation | false | HooshvareLab | null | HooshvareLab/gpt2-fa-comment | 65 | null | transformers | 5,505 | ---
language: fa
license: apache-2.0
widget:
- text: "<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>"
- text: "<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>"
- text: "<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>"
---
# Persian Comment Generator
The model can generate comments based on your aspects, and the model was fine-tuned on [persiannlp/parsinlu](https://github.com/persiannlp/parsinlu). Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.
## Comments Aspects
```text
<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>
<s>نمونه دیدگاه خوب به طور کلی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال <sep>
<s>نمونه دیدگاه منفی از نظر طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم به طور کلی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی به طور کلی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال <sep>
<s>نمونه دیدگاه نظری ندارم از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری و کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صحنه و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر صحنه و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان و موسیقی و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر فیلمبرداری و صحنه و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و صحنه <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و داستان و بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و صحنه <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و صحنه و داستان و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و صدا و صحنه و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی و موسیقی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و بازی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خوب از نظر موسیقی و داستان <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و فیلمبرداری و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر صحنه <sep>
<s>نمونه دیدگاه منفی از نظر داستان و صحنه <sep>
<s>نمونه دیدگاه منفی از نظر بازی و صحنه و صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر فیلمبرداری و صدا <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری و موسیقی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و داستان <sep>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo. |
KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation | c8a9275fdc413f4f8e113cc924861f1994b04d5a | 2021-12-10T00:34:26.000Z | [
"pytorch",
"roberta",
"token-classification",
"lzh",
"transformers",
"classical chinese",
"literary chinese",
"ancient chinese",
"sentence segmentation",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation | 65 | 1 | transformers | 5,506 | ---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "sentence segmentation"
- "token-classification"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
---
# roberta-classical-chinese-base-sentence-segmentation
## Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for sentence segmentation, derived from [roberta-classical-chinese-base-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-base-char). Every segmented sentence begins with token-class "B" and ends with token-class "E" (except for single-character sentence with token-class "S").
## How to Use
```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation")
s="子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
print("".join(c+"。" if q=="E" or q=="S" else c for c,q in zip(s,p)))
```
## Reference
Koichi Yasuoka: [Sentence Segmentation of Classical Chinese Texts Using Transformers and BERT/RoBERTa Models](http://hdl.handle.net/2433/266539), IPSJ Symposium Series, Vol.2021, No.1 (December 2021), pp.104-109.
|
addy88/perceiver_image_classifier | d13564b81f342b5223ac5001a216ef89cfe2c8a4 | 2022-01-02T13:05:37.000Z | [
"pytorch",
"perceiver",
"image-classification",
"transformers"
] | image-classification | false | addy88 | null | addy88/perceiver_image_classifier | 65 | null | transformers | 5,507 | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("addy88/perceiver_image_classifier")
model = PerceiverForImageClassificationLearned.from_pretrained("addy88/perceiver_image_classifier")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare input
encoding = feature_extractor(image, return_tensors="pt")
inputs = encoding.pixel_values
# forward pass
outputs = model(inputs)
logits = outputs.logits
print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
>>> should print Predicted class: tabby, tabby cat
``` |
amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 | 7e66c09a995a61e7f37d29d72e064383ff7ca13e | 2021-10-17T00:32:35.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:amansolanki/autonlp-data-Tweet-Sentiment-Extraction",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | false | amansolanki | null | amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061 | 65 | null | transformers | 5,508 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- amansolanki/autonlp-data-Tweet-Sentiment-Extraction
co2_eq_emissions: 3.651199395353127
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 20114061
- CO2 Emissions (in grams): 3.651199395353127
## Validation Metrics
- Loss: 0.5046541690826416
- Accuracy: 0.8036219581211093
- Macro F1: 0.807095210403678
- Micro F1: 0.8036219581211093
- Weighted F1: 0.8039634739225368
- Macro Precision: 0.8076842795233988
- Micro Precision: 0.8036219581211093
- Weighted Precision: 0.8052135235094771
- Macro Recall: 0.8075241470527056
- Micro Recall: 0.8036219581211093
- Weighted Recall: 0.8036219581211093
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("amansolanki/autonlp-Tweet-Sentiment-Extraction-20114061", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
ddobokki/electra-small-nli-sts | 3246a95b1b7fba01723b63e394543900f9deaeb5 | 2022-03-28T07:49:33.000Z | [
"pytorch",
"electra",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"ko"
] | sentence-similarity | false | ddobokki | null | ddobokki/electra-small-nli-sts | 65 | 1 | sentence-transformers | 5,509 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- ko
---
# ddobokki/electra-small-nli-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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('ddobokki/electra-small-nli-sts')
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('ddobokki/electra-small-nli-sts')
model = AutoModel.from_pretrained('ddobokki/electra-small-nli-sts')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- 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=ddobokki/electra-small-nli-sts)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 9039 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 903,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 904,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ElectraModel
(1): Pooling({'word_embedding_dimension': 256, '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 --> |
huggingtweets/jesusisathembo | df9d11183402edc0c16c9fa7357808e68220719e | 2021-05-22T09:38:03.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/jesusisathembo | 65 | null | transformers | 5,510 | ---
language: en
thumbnail: https://www.huggingtweets.com/jesusisathembo/1614096400764/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1331300707216068609/s4UcWg76_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">serial experiments maK 🤖 AI Bot </div>
<div style="font-size: 15px">@jesusisathembo bot</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://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@jesusisathembo's tweets](https://twitter.com/jesusisathembo).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3161 |
| Retweets | 1164 |
| Short tweets | 243 |
| Tweets kept | 1754 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bqj16zu/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 @jesusisathembo's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kuiuxq9x) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kuiuxq9x/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/jesusisathembo')
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)
|
LACAI/DialoGPT-small-SGD | 577683f7fd450f9167ea72c5e398bf2046490ade | 2022-01-02T04:08:07.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | LACAI | null | LACAI/DialoGPT-small-SGD | 65 | 1 | transformers | 5,511 | Base model: [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small)
Fine tuned for dialogue response generation on the [Schema Guided Dialogue Dataset](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) (Rastogi et al., 2019)
Three additional special tokens were added during the fine-tuning process:
- <|pad|> padding token
- <|user|> speaker control token to prompt user responses
- <|system|> speaker control token to prompt system responses |
raynardj/ner-gene-dna-rna-jnlpba-pubmed | 231b91cf27c49cb398a112e24e439dc407a884f3 | 2021-11-05T07:32:32.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:jnlpba",
"transformers",
"ner",
"gene",
"protein",
"rna",
"bioinfomatics",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | raynardj | null | raynardj/ner-gene-dna-rna-jnlpba-pubmed | 65 | 1 | transformers | 5,512 | ---
language:
- en
tags:
- ner
- gene
- protein
- rna
- bioinfomatics
license: apache-2.0
datasets:
- jnlpba
widget:
- text: "It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed of 13 transmembrane domains"
---
# NER to find Gene & Gene products
> The model was trained on jnlpba dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed)
All the labels, the possible token classes.
```json
{"label2id": {
"DNA": 2,
"O": 0,
"RNA": 5,
"cell_line": 4,
"cell_type": 3,
"protein": 1
}
}
```
Notice, we removed the 'B-','I-' etc from data label.🗡
## This is the template we suggest for using the model
```python
from transformers import pipeline
PRETRAINED = "raynardj/ner-gene-dna-rna-jnlpba-pubmed"
ner = pipeline(task="ner",model=PRETRAINED, tokenizer=PRETRAINED)
ner("Your text", aggregation_strategy="first")
```
And here is to make your output more consecutive ⭐️
```python
import pandas as pd
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
def clean_output(outputs):
results = []
current = []
last_idx = 0
# make to sub group by position
for output in outputs:
if output["index"]-1==last_idx:
current.append(output)
else:
results.append(current)
current = [output, ]
last_idx = output["index"]
if len(current)>0:
results.append(current)
# from tokens to string
strings = []
for c in results:
tokens = []
starts = []
ends = []
for o in c:
tokens.append(o['word'])
starts.append(o['start'])
ends.append(o['end'])
new_str = tokenizer.convert_tokens_to_string(tokens)
if new_str!='':
strings.append(dict(
word=new_str,
start = min(starts),
end = max(ends),
entity = c[0]['entity']
))
return strings
def entity_table(pipeline, **pipeline_kw):
if "aggregation_strategy" not in pipeline_kw:
pipeline_kw["aggregation_strategy"] = "first"
def create_table(text):
return pd.DataFrame(
clean_output(
pipeline(text, **pipeline_kw)
)
)
return create_table
# will return a dataframe
entity_table(ner)(YOUR_VERY_CONTENTFUL_TEXT)
```
> check our NER model on
* [gene and gene products](/raynardj/ner-gene-dna-rna-jnlpba-pubmed)
* [chemical substance](/raynardj/ner-chemical-bionlp-bc5cdr-pubmed).
* [disease](/raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed) |
razent/spbert-mlm-base | e98ed8adec9e4cd04a2743cf3f5a10ccabe8a4db | 2022-03-15T03:25:56.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"code",
"arxiv:2106.09997",
"transformers",
"question-answering",
"knowledge-graph",
"autotrain_compatible"
] | question-answering | false | razent | null | razent/spbert-mlm-base | 65 | null | transformers | 5,513 | ---
language:
- code
tags:
- question-answering
- knowledge-graph
---
# SPBERT MLM (Initialized)
## Introduction
Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out [our Github repo](https://github.com/heraclex12/NLP2SPARQL).
Here is an example in Pytorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-base')
model = AutoModel.from_pretrained("razent/spbert-mlm-base")
text = "select * where brack_open var_a var_b var_c sep_dot brack_close"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
or Tensorflow
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-base')
model = TFAutoModel.from_pretrained("razent/spbert-mlm-base")
text = "select * where brack_open var_a var_b var_c sep_dot brack_close"
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Citation
```
@misc{tran2021spbert,
title={SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs},
author={Hieu Tran and Long Phan and James Anibal and Binh T. Nguyen and Truong-Son Nguyen},
year={2021},
eprint={2106.09997},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
kazandaev/opus-mt-en-ru-finetuned-v3 | 37eb193690cff71e2e0c82e2a09523e0a99becce | 2022-03-08T10:50:47.000Z | [
"pytorch",
"tensorboard",
"rust",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | kazandaev | null | kazandaev/opus-mt-en-ru-finetuned-v3 | 65 | null | transformers | 5,514 | ---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-en-ru-finetuned-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-ru-finetuned-v3
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8300
- Bleu: 36.9731
- Gen Len: 29.5504
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 85
- eval_batch_size: 85
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 1.073 | 1.0 | 71642 | 0.8276 | 37.1982 | 29.5248 |
| 1.0769 | 2.0 | 143284 | 0.8289 | 37.1495 | 29.5305 |
| 1.0772 | 3.0 | 214926 | 0.8294 | 37.1719 | 29.5099 |
| 1.0767 | 4.0 | 286568 | 0.8297 | 37.0152 | 29.5522 |
| 1.0668 | 5.0 | 358210 | 0.8300 | 36.9731 | 29.5504 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
|
clapika2010/adult_finetuned | 87bb8dcfb670a100d351462b78c17a1a568d849e | 2022-03-13T00:53:24.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | clapika2010 | null | clapika2010/adult_finetuned | 65 | null | transformers | 5,515 | Entry not found |
fabiochiu/t5-small-medium-title-generation | 512b08cb241e724542ff9791bd01937294593dd2 | 2022-05-17T08:46:22.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | fabiochiu | null | fabiochiu/t5-small-medium-title-generation | 65 | null | transformers | 5,516 | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-medium-title-generation
results: []
widget:
- text: "summarize: Many financial institutions started building conversational AI, prior to the Covid19 pandemic, as part of a digital transformation initiative. These initial solutions were high profile, highly personalized virtual assistants — like the Erica chatbot from Bank of America. As the pandemic hit, the need changed as contact centers were under increased pressures. As Cathal McGloin of ServisBOT explains in 'how it started, and how it is going,' financial institutions were looking for ways to automate solutions to help get back to 'normal' levels of customer service. This resulted in a change from the 'future of conversational AI' to a real tactical assistant that can help in customer service. Haritha Dev of Wells Fargo, saw a similar trend. Banks were originally looking to conversational AI as part of digital transformation to keep up with the times. However, with the pandemic, it has been more about customer retention and customer satisfaction. In addition, new use cases came about as a result of Covid-19 that accelerated adoption of conversational AI. As Vinita Kumar of Deloitte points out, banks were dealing with an influx of calls about new concerns, like questions around the Paycheck Protection Program (PPP) loans. This resulted in an increase in volume, without enough agents to assist customers, and tipped the scale to incorporate conversational AI. When choosing initial use cases to support, financial institutions often start with high volume, low complexity tasks. For example, password resets, checking account balances, or checking the status of a transaction, as Vinita points out. From there, the use cases can evolve as the banks get more mature in developing conversational AI, and as the customers become more engaged with the solutions. Cathal indicates another good way for banks to start is looking at use cases that are a pain point, and also do not require a lot of IT support. Some financial institutions may have a multi-year technology roadmap, which can make it harder to get a new service started. A simple chatbot for document collection in an onboarding process can result in high engagement, and a high return on investment. For example, Cathal has a banking customer that implemented a chatbot to capture a driver’s license to be used in the verification process of adding an additional user to an account — it has over 85% engagement with high satisfaction. An interesting use case Haritha discovered involved educating customers on financial matters. People feel more comfortable asking a chatbot what might be considered a 'dumb' question, as the chatbot is less judgmental. Users can be more ambiguous with their questions as well, not knowing the right words to use, as chatbot can help narrow things down."
example_title: "Banking on Bots"
---
<!-- 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. -->
# Model description
This model is [t5-small](https://huggingface.co/t5-small) fine-tuned on the [190k Medium Articles](https://www.kaggle.com/datasets/fabiochiusano/medium-articles) dataset for predicting article titles using the article textual content as input.
There are two versions of the model:
- [t5-small-medium-title-generation](https://huggingface.co/fabiochiu/t5-small-medium-title-generation): trained from [t5-small](https://huggingface.co/t5-small).
- [t5-base-medium-title-generation](https://huggingface.co/fabiochiu/t5-base-medium-title-generation): trained from [t5-base](https://huggingface.co/t5-base).
Visit the [title-generation space](https://huggingface.co/spaces/fabiochiu/title-generation) to try the model with different text generation parameters.
# How to use the model
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import nltk
nltk.download('punkt')
tokenizer = AutoTokenizer.from_pretrained("fabiochiu/t5-small-medium-title-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("fabiochiu/t5-small-medium-title-generation")
text = """
Many financial institutions started building conversational AI, prior to the Covid19
pandemic, as part of a digital transformation initiative. These initial solutions
were high profile, highly personalized virtual assistants — like the Erica chatbot
from Bank of America. As the pandemic hit, the need changed as contact centers were
under increased pressures. As Cathal McGloin of ServisBOT explains in “how it started,
and how it is going,” financial institutions were looking for ways to automate
solutions to help get back to “normal” levels of customer service. This resulted
in a change from the “future of conversational AI” to a real tactical assistant
that can help in customer service. Haritha Dev of Wells Fargo, saw a similar trend.
Banks were originally looking to conversational AI as part of digital transformation
to keep up with the times. However, with the pandemic, it has been more about
customer retention and customer satisfaction. In addition, new use cases came about
as a result of Covid-19 that accelerated adoption of conversational AI. As Vinita
Kumar of Deloitte points out, banks were dealing with an influx of calls about new
concerns, like questions around the Paycheck Protection Program (PPP) loans. This
resulted in an increase in volume, without enough agents to assist customers, and
tipped the scale to incorporate conversational AI. When choosing initial use cases
to support, financial institutions often start with high volume, low complexity
tasks. For example, password resets, checking account balances, or checking the
status of a transaction, as Vinita points out. From there, the use cases can evolve
as the banks get more mature in developing conversational AI, and as the customers
become more engaged with the solutions. Cathal indicates another good way for banks
to start is looking at use cases that are a pain point, and also do not require a
lot of IT support. Some financial institutions may have a multi-year technology
roadmap, which can make it harder to get a new service started. A simple chatbot
for document collection in an onboarding process can result in high engagement,
and a high return on investment. For example, Cathal has a banking customer that
implemented a chatbot to capture a driver’s license to be used in the verification
process of adding an additional user to an account — it has over 85% engagement
with high satisfaction. An interesting use case Haritha discovered involved
educating customers on financial matters. People feel more comfortable asking a
chatbot what might be considered a “dumb” question, as the chatbot is less judgmental.
Users can be more ambiguous with their questions as well, not knowing the right
words to use, as chatbot can help narrow things down.
"""
inputs = ["summarize: " + text]
inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]
print(predicted_title)
# Conversational AI: The Future of Customer Service
```
## Training and evaluation data
The model has been trained on a single epoch spanning about 16000 articles, evaluating on 1000 random articles not used during training.
### Training results
The model has been evaluated on a random dataset split of 1000 articles not used during training and validation.
- Rouge-1: 27.8%
- Rouge-2: 14.9%
- Rouge-L: 26.9%
- Rouge-Lsum: 26.9%
- Average length of the generated titles: 13 tokens (about 9 English words)
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
khanhld/wav2vec2-base-vietnamese-160h | 0c8ad9977189dc089a5c5f55c7b6dbaa79d8c5fd | 2022-05-13T14:13:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:vivos",
"dataset:common_voice",
"dataset:FOSD",
"dataset:VLSP",
"transformers",
"audio",
"speech",
"Transformer",
"vietnamese",
"license:cc-by-nc-4.0",
"model-index"
] | automatic-speech-recognition | false | khanhld | null | khanhld/wav2vec2-base-vietnamese-160h | 65 | 1 | transformers | 5,517 | ---
language: vi
datasets:
- vivos
- common_voice
- FOSD
- VLSP
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech
- Transformer
- wav2vec2
- automatic-speech-recognition
- vietnamese
license: cc-by-nc-4.0
widget:
- example_title: common_voice_vi_30519758.mp3
src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/common_voice_vi_30519758.mp3
- example_title: VIVOSDEV15_020.wav
src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/VIVOSDEV15_020.wav
model-index:
- name: Wav2vec2 Base Vietnamese 160h
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: common-voice-vietnamese
type: common_voice
args: vi
metrics:
- name: Test WER
type: wer
value: 10.78
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: VIVOS
type: vivos
args: vi
metrics:
- name: Test WER
type: wer
value: 15.05
---
[](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=wav2vec2-base-vietnamese-160h)
[](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=wav2vec2-base-vietnamese-160h)
# Vietnamese Speech Recognition using Wav2vec 2.0
### Table of contents
1. [Model Description](#description)
2. [Implementation](#implementation)
3. [Benchmark Result](#benchmark)
4. [Example Usage](#example)
5. [Evaluation](#evaluation)
6. [Citation](#citation)
7. [Contact](#contact)
<a name = "description" ></a>
### Model Description
Fine-tuned the Wav2vec2-based model on about 160 hours of Vietnamese speech dataset from different resources, including [VIOS](https://huggingface.co/datasets/vivos), [COMMON VOICE](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VLSP 100h](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view). We have not yet incorporated the Language Model into our ASR system but still gained a promising result.
<a name = "implementation" ></a>
### Implementation
We also provide code for Pre-training and Fine-tuning the Wav2vec2 model. If you wish to train on your dataset, check it out here:
- [Pre-train code](https://github.com/khanld/ASR-Wav2vec-Pretrain) (not available for now but will release soon)
- [Fine-tune code](https://github.com/khanld/ASR-Wa2vec-Finetune)
<a name = "benchmark" ></a>
### Benchmark WER Result
| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
|---|---|---|
|without LM| 15.05 | 10.78 |
|with LM| in progress | in progress |
<a name = "example" ></a>
### Example Usage [](https://colab.research.google.com/drive/1blz1KclnIfbOp8o2fW3WJgObOQ9SMGBo?usp=sharing)
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import librosa
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model.to(device)
def transcribe(wav):
input_values = processor(wav, sampling_rate=16000, return_tensors="pt").input_values
logits = model(input_values.to(device)).logits
pred_ids = torch.argmax(logits, dim=-1)
pred_transcript = processor.batch_decode(pred_ids)[0]
return pred_transcript
wav, _ = librosa.load('path/to/your/audio/file', sr = 16000)
print(f"transcript: {transcribe(wav)}")
```
<a name = "evaluation"></a>
### Evaluation [](https://colab.research.google.com/drive/1XQCq4YGLnl23tcKmYeSwaksro4IgC_Yi?usp=sharing)
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
import re
from datasets import load_dataset, load_metric, Audio
wer = load_metric("wer")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load processor and model
processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model.to(device)
model.eval()
# Load dataset
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token="your_huggingface_auth_token")
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000))
chars_to_ignore = r'[,?.!\-;:"“%\'�]' # ignore special characters
# preprocess data
def preprocess(batch):
audio = batch["audio"]
batch["input_values"] = audio["array"]
batch["transcript"] = re.sub(chars_to_ignore, '', batch["sentence"]).lower()
return batch
# run inference
def inference(batch):
input_values = processor(batch["input_values"],
sampling_rate=16000,
return_tensors="pt").input_values
logits = model(input_values.to(device)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_transcript"] = processor.batch_decode(pred_ids)
return batch
test_dataset = test_dataset.map(preprocess)
result = test_dataset.map(inference, batched=True, batch_size=1)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_transcript"], references=result["transcript"])))
```
**Test Result**: 10.78%
<a name = "citation" ></a>
### Citation
[](https://zenodo.org/badge/latestdoi/491468343)
<strong>BibTeX</strong>
```
@mics{Duy_Khanh_Finetune_Wav2vec_2_0_2022,
author = {Duy Khanh, Le},
doi = {10.5281/zenodo.6542357},
license = {CC-BY-NC-4.0},
month = {5},
title = {{Finetune Wav2vec 2.0 For Vietnamese Speech Recognition}},
url = {https://github.com/khanld/ASR-Wa2vec-Finetune},
year = {2022}
}
```
<strong>APA</strong>
```
Duy Khanh, L. (2022). Finetune Wav2vec 2.0 For Vietnamese Speech Recognition [Data set]. https://doi.org/10.5281/zenodo.6542357
```
<a name = "contact"></a>
### Contact
- [email protected]
- [](https://github.com/)
- [](https://www.linkedin.com/in/khanhld257/)
|
tolmanneo/convo-gpt-j-6b | 1f275b63604eaa09629e494fb83ba796c1993133 | 2022-06-09T16:08:57.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers",
"conversational"
] | conversational | false | tolmanneo | null | tolmanneo/convo-gpt-j-6b | 65 | null | transformers | 5,518 | ---
tags: conversational
--- |
tolmanneo/convo-gpt-j-6b-10x | c5a51f30a7ac057fc844f1115c897471c8ebad86 | 2022-06-09T15:44:39.000Z | [
"pytorch",
"gptj",
"text-generation",
"transformers",
"conversational"
] | conversational | false | tolmanneo | null | tolmanneo/convo-gpt-j-6b-10x | 65 | null | transformers | 5,519 | ---
tags:
- conversational
---
|
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False | 33f750518ad0ba3a89ab96dd58c9ae2299cd114f | 2022-06-16T09:29:42.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False | 65 | null | transformers | 5,520 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_token_itr0_0.0001_TRAIN_all_TEST_null__second_train_set_NULL_False
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0650
- Precision: 0.9847
- Recall: 0.9864
- F1: 0.9856
- Accuracy: 0.9719
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 25 | 0.2530 | 0.9106 | 0.8321 | 0.8696 | 0.7793 |
| No log | 2.0 | 50 | 0.1882 | 0.9855 | 0.6891 | 0.8111 | 0.7116 |
| No log | 3.0 | 75 | 0.1879 | 0.9467 | 0.7173 | 0.8162 | 0.7105 |
| No log | 4.0 | 100 | 0.1987 | 0.9567 | 0.7108 | 0.8156 | 0.7120 |
| No log | 5.0 | 125 | 0.1949 | 0.9511 | 0.7136 | 0.8154 | 0.7105 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
luoyixin/marian-finetuned-kde4-en-to-zh | 575d97dab284cba5ff1cc0202ac4a9a4f9a99ab6 | 2022-06-20T10:13:41.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | luoyixin | null | luoyixin/marian-finetuned-kde4-en-to-zh | 65 | null | transformers | 5,521 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-zh
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-zh_CN
metrics:
- name: Bleu
type: bleu
value: 40.678005282996
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-zh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9338
- Bleu: 40.6780
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Yehor/wav2vec2-xls-r-base-uk-with-small-lm | b958c7f8aecb6ad9de8702c2ec63462a3d02b62c | 2022-07-30T07:00:59.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"uk",
"transformers",
"license:cc-by-nc-sa-4.0"
] | automatic-speech-recognition | false | Yehor | null | Yehor/wav2vec2-xls-r-base-uk-with-small-lm | 65 | null | transformers | 5,522 | ---
language:
- uk
license: "cc-by-nc-sa-4.0"
---
**NOTE**: Look on a better model https://huggingface.co/Yehor/wav2vec2-xls-r-base-uk-with-cv-lm
🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk
⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk
This model was trained using the base model https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS (pre-trained from 80 thousand hours of Czech speech)
This model has apostrophes and hyphens.
Metrics:
Without LM:
- WER: 0.4745
- CER: 0.1104
---
SMALL LM (this repository):
- WER: 0.303
- CER: 0.0818
WIKI LM (https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-wiki-lm/tree/main/language_model):
- WER: 0.2807
- CER: 0.0785
NEWS LM (https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-news-lm/tree/main/language_model):
- WER: 0.2633
- CER: 0.0753
|
WindowsRegedit/zuowen | b35bd2ea0489d52b0af56cbcfd84a198d8c55df6 | 2022-06-23T12:47:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | WindowsRegedit | null | WindowsRegedit/zuowen | 65 | null | transformers | 5,523 | ### 作文模型
使用方法，请参考[Python 自动写作文库](https://github.com/WindowsRegedit/zuowen)
|
tner/bert-base-tweetner-2020 | cccef3955fffead783edbbb495b5444db567bea5 | 2022-07-08T11:18:19.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tner | null | tner/bert-base-tweetner-2020 | 65 | null | transformers | 5,524 | Entry not found |
Evelyn18/BECASV4.1 | d02d756d758240bbf1bbab8f3f9810d36ce863b6 | 2022-07-19T03:34:08.000Z | [
"pytorch",
"roberta",
"question-answering",
"dataset:becasv2",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | question-answering | false | Evelyn18 | null | Evelyn18/BECASV4.1 | 65 | null | transformers | 5,525 | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-spanish-squades-modelo-robertav1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-spanish-squades-modelo-robertav1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7840
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 1.9356 |
| No log | 2.0 | 10 | 1.7489 |
| No log | 3.0 | 15 | 2.0573 |
| No log | 4.0 | 20 | 2.3975 |
| No log | 5.0 | 25 | 2.6796 |
| No log | 6.0 | 30 | 2.7238 |
| No log | 7.0 | 35 | 2.7616 |
| No log | 8.0 | 40 | 2.7840 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1 |
Amrrs/wav2vec2-large-xlsr-53-tamil | 017e2f0cf4b196d1656e310c87403fe446e39581 | 2021-07-05T14:14:42.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Amrrs | null | Amrrs/wav2vec2-large-xlsr-53-tamil | 64 | 1 | transformers | 5,526 | ---
language: ta
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Tamil by Amrrs
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ta
type: common_voice
args: ta
metrics:
- name: Test WER
type: wer
value: 82.94
---
# Wav2Vec2-Large-XLSR-53-Tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ta", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ta", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model = Wav2Vec2ForCTC.from_pretrained("Amrrs/wav2vec2-large-xlsr-53-tamil")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 82.94 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/drive/1-Klkgr4f-C9SanHfVC5RhP0ELUH6TYlN?usp=sharing)
|
Helsinki-NLP/opus-mt-pap-en | 38a0afea7e9b1b124d2ec116e42d8a55a0bfc884 | 2021-09-10T14:00:36.000Z | [
"pytorch",
"marian",
"text2text-generation",
"pap",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-pap-en | 64 | null | transformers | 5,527 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pap-en
* source languages: pap
* target languages: en
* OPUS readme: [pap-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pap-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.pap.en | 47.3 | 0.634 |
| Tatoeba.pap.en | 63.2 | 0.684 |
|
HooshvareLab/gpt2-fa-poetry | 35d19a3dba707d9d1ca075e35340f0477c8d9e0b | 2021-05-21T10:50:14.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"transformers",
"license:apache-2.0"
] | text-generation | false | HooshvareLab | null | HooshvareLab/gpt2-fa-poetry | 64 | null | transformers | 5,528 | ---
language: fa
license: apache-2.0
widget:
- text: "<s>رودکی<|startoftext|>"
- text: "<s>فردوسی<|startoftext|>"
- text: "<s>خیام<|startoftext|>"
- text: "<s>عطار<|startoftext|>"
- text: "<s>نظامی<|startoftext|>"
---
# Persian Poet GPT2
## Poets
The model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the [fine-tuning notebook](https://colab.research.google.com/github/hooshvare/parsgpt/blob/master/notebooks/Persian_Poetry_FineTuning.ipynb).
```text
<s>رودکی<|startoftext|>
<s>فردوسی<|startoftext|>
<s>کسایی<|startoftext|>
<s>ناصرخسرو<|startoftext|>
<s>منوچهری<|startoftext|>
<s>فرخی سیستانی<|startoftext|>
<s>مسعود سعد سلمان<|startoftext|>
<s>ابوسعید ابوالخیر<|startoftext|>
<s>باباطاهر<|startoftext|>
<s>فخرالدین اسعد گرگانی<|startoftext|>
<s>اسدی توسی<|startoftext|>
<s>هجویری<|startoftext|>
<s>خیام<|startoftext|>
<s>نظامی<|startoftext|>
<s>عطار<|startoftext|>
<s>سنایی<|startoftext|>
<s>خاقانی<|startoftext|>
<s>انوری<|startoftext|>
<s>عبدالواسع جبلی<|startoftext|>
<s>نصرالله منشی<|startoftext|>
<s>مهستی گنجوی<|startoftext|>
<s>باباافضل کاشانی<|startoftext|>
<s>مولوی<|startoftext|>
<s>سعدی<|startoftext|>
<s>خواجوی کرمانی<|startoftext|>
<s>عراقی<|startoftext|>
<s>سیف فرغانی<|startoftext|>
<s>حافظ<|startoftext|>
<s>اوحدی<|startoftext|>
<s>شیخ محمود شبستری<|startoftext|>
<s>عبید زاکانی<|startoftext|>
<s>امیرخسرو دهلوی<|startoftext|>
<s>سلمان ساوجی<|startoftext|>
<s>شاه نعمتالله ولی<|startoftext|>
<s>جامی<|startoftext|>
<s>هلالی جغتایی<|startoftext|>
<s>وحشی<|startoftext|>
<s>محتشم کاشانی<|startoftext|>
<s>شیخ بهایی<|startoftext|>
<s>عرفی<|startoftext|>
<s>رضیالدین آرتیمانی<|startoftext|>
<s>صائب تبریزی<|startoftext|>
<s>فیض کاشانی<|startoftext|>
<s>بیدل دهلوی<|startoftext|>
<s>هاتف اصفهانی<|startoftext|>
<s>فروغی بسطامی<|startoftext|>
<s>قاآنی<|startoftext|>
<s>ملا هادی سبزواری<|startoftext|>
<s>پروین اعتصامی<|startoftext|>
<s>ملکالشعرای بهار<|startoftext|>
<s>شهریار<|startoftext|>
<s>رهی معیری<|startoftext|>
<s>اقبال لاهوری<|startoftext|>
<s>خلیلالله خلیلی<|startoftext|>
<s>شاطرعباس صبوحی<|startoftext|>
<s>نیما یوشیج ( آوای آزاد )<|startoftext|>
<s>احمد شاملو<|startoftext|>
<s>سهراب سپهری<|startoftext|>
<s>فروغ فرخزاد<|startoftext|>
<s>سیمین بهبهانی<|startoftext|>
<s>مهدی اخوان ثالث<|startoftext|>
<s>محمدحسن بارق شفیعی<|startoftext|>
<s>شیون فومنی<|startoftext|>
<s>کامبیز صدیقی کسمایی<|startoftext|>
<s>بهرام سالکی<|startoftext|>
<s>عبدالقهّار عاصی<|startoftext|>
<s>اِ لیـــار (جبار محمدی )<|startoftext|>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo. |
Irina/fantasy_GPT3Medium | e88c9457179f14b78966559711a4f6039bb31b11 | 2021-11-26T05:29:14.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Irina | null | Irina/fantasy_GPT3Medium | 64 | null | transformers | 5,529 | Entry not found |
alireza7/PEGASUS-persian-base-voa-title | 314282bf0721fcebe6260a8a6f6da340c15750ac | 2021-09-29T19:26:07.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | alireza7 | null | alireza7/PEGASUS-persian-base-voa-title | 64 | null | transformers | 5,530 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
banjtheman/distilbert-base-uncased-helpful-amazon | 53364fd30e3bfa78a706b9701cc31ba218c7ff60 | 2022-02-04T21:22:32.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | banjtheman | null | banjtheman/distilbert-base-uncased-helpful-amazon | 64 | null | transformers | 5,531 | ---
license: apache-2.0
---
## Overview
This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is.
The model was fine-tuned from the `distilbert-base-uncased` model
### Labels
LABEL_0 - Not helpful
LABEL_1 - Helpful
### How to use
The following code shows how to make a prediction with this model
```python
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
TextClassificationPipeline,
)
tokenizer = AutoTokenizer.from_pretrained("banjtheman/distilbert-base-uncased-helpful-amazon")
model = AutoModelForSequenceClassification.from_pretrained(
"banjtheman/distilbert-base-uncased-helpful-amazon"
)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
result = pipe("This was a Christmas gift for my grandson.")
print(result)
#[{'label': 'LABEL_0', 'score': 0.998775064945221}]
# This is NOT A HELPFUL comment
``` |
castorini/monobert-large-msmarco | 0a97706f3827389da43b83348d5d18c9d53876fa | 2020-05-29T03:41:44.000Z | [
"pytorch",
"transformers"
] | null | false | castorini | null | castorini/monobert-large-msmarco | 64 | null | transformers | 5,532 | Entry not found |
ensamblador/gpt2-twitter-politico | 768a296746bf63b6f6bd9960c50a0e696622f74b | 2021-05-21T15:54:38.000Z | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ensamblador | null | ensamblador/gpt2-twitter-politico | 64 | null | transformers | 5,533 | Entry not found |
gagan3012/wav2vec2-xlsr-khmer | 2b626a577ac629a05d1ab01ac5ba3ad14740de54 | 2021-07-06T03:58:05.000Z | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"km",
"dataset:OpenSLR",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | gagan3012 | null | gagan3012/wav2vec2-xlsr-khmer | 64 | null | transformers | 5,534 | ---
language: km
datasets:
- OpenSLR
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-xlsr-Khmer by Gagan Bhatia
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: OpenSLR km
type: OpenSLR
args: km
metrics:
- name: Test WER
type: wer
value: 24.96
---
# Wav2Vec2-Large-XLSR-53-khmer
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Khmer using the [Common Voice](https://huggingface.co/datasets/common_voice), and [OpenSLR Kh](http://www.openslr.org/42/).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
!wget https://www.openslr.org/resources/42/km_kh_male.zip
!unzip km_kh_male.zip
!ls km_kh_male
colnames=['path','sentence']
df = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/km_kh_male/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\\\\\\\\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\\\\\\\\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\\\\\\\\\\\\\\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
\\\\\\\\\\\\\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
#### Result
Prediction: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
Reference: ['पारानाको ब्राजिली राज्यमा रहेको राजधानी', 'देवराज जोशी त्रिभुवन विश्वविद्यालयबाट शिक्षाशास्त्रमा स्नातक हुनुहुन्छ']
## Evaluation
The model can be evaluated as follows on the {language} test data of Common Voice. # TODO: replace #TODO: replace language with your {language}, *e.g.* French
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from sklearn.model_selection import train_test_split
import pandas as pd
from datasets import load_dataset
!wget https://www.openslr.org/resources/42/km_kh_male.zip
!unzip km_kh_male.zip
!ls km_kh_male
colnames=['path','sentence']
df = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\t',header=None,names = colnames)
df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
train, test = train_test_split(df, test_size=0.1)
test.to_csv('/content/km_kh_male/line_index_test.csv')
test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
wer = load_metric("wer")
cer = load_metric("cer")
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\\tbatch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\\treturn batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
\\twith torch.no_grad():
\\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
\\tpred_ids = torch.argmax(logits, dim=-1)
\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\\treturn batch
cer = load_metric("cer")
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["text"])))
```
**Test Result**: 24.96 %
WER: 24.962519
CER: 6.950925
## Training
The script used for training can be found [here](https://colab.research.google.com/drive/1yo_OTMH8FHQrAKCkKdQGMqpkj-kFhS_2?usp=sharing) |
huggingtweets/dogdick420cum | 5d7a5f21ba570ac5f7e530167238886a77aee20e | 2021-05-22T01:55:30.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/dogdick420cum | 64 | null | transformers | 5,535 | ---
language: en
thumbnail: https://www.huggingtweets.com/dogdick420cum/1615429013878/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1359563991467646982/9ZPrurHY_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">🥞 🤖 AI Bot </div>
<div style="font-size: 15px">@dogdick420cum bot</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://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@dogdick420cum's tweets](https://twitter.com/dogdick420cum).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 148 |
| Short tweets | 512 |
| Tweets kept | 2582 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nzltah4f/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 @dogdick420cum's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/29pe6wy0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/29pe6wy0/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/dogdick420cum')
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)
|
lighteternal/nli-xlm-r-greek | 7fabdac688edc6007f9f5adf212bdb8e2deeb752 | 2021-09-21T16:01:42.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"el",
"en",
"dataset:multi_nli",
"dataset:snli",
"dataset:allnli_greek",
"arxiv:1908.10084",
"transformers",
"xlm-roberta-base",
"license:apache-2.0",
"zero-shot-classification"
] | zero-shot-classification | false | lighteternal | null | lighteternal/nli-xlm-r-greek | 64 | null | transformers | 5,536 | ---
language:
- el
- en
tags:
- xlm-roberta-base
datasets:
- multi_nli
- snli
- allnli_greek
metrics:
- accuracy
pipeline_tag: zero-shot-classification
widget:
- text: "Η Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας."
candidate_labels: "τεχνολογία, πολιτική, αθλητισμός"
multi_class: false
license: apache-2.0
---
# Cross-Encoder for Greek Natural Language Inference (Textual Entailment) & Zero-Shot Classification
## By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the the combined Greek+English version of the AllNLI dataset(sum of [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/)). The Greek part was created using the EN2EL NMT model available [here](https://huggingface.co/lighteternal/SSE-TUC-mt-en-el-cased).
The model can be used in two ways:
* NLI/Textual Entailment: For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
* Zero-shot classification through the Huggingface pipeline: Given a sentence and a set of labels/topics, it will output the likelihood of the sentence belonging to each of the topic. Under the hood, the logit for entailment between the sentence and each label is taken as the logit for the candidate label being valid.
## Performance
Evaluation on classification accuracy (entailment, contradiction, neutral) on mixed (Greek+English) AllNLI-dev set:
| Metric | Value |
| --- | --- |
| Accuracy | 0.8409 |
## To use the model for NLI/Textual Entailment
#### Usage with sentence_transformers
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('lighteternal/nli-xlm-r-greek')
scores = model.predict([('Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'),
('Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο'),
('Δυο γυναίκες μιλάνε στο κινητό', 'Το τραπέζι ήταν πράσινο')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
print(scores, labels)
# Οutputs
#[[-3.1526504 2.9981945 -0.3108107]
# [ 5.0549307 -2.757949 -1.6220676]
# [-0.5124733 -2.2671669 3.1630592]] ['entailment', 'contradiction', 'neutral']
```
#### Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('lighteternal/nli-xlm-r-greek')
tokenizer = AutoTokenizer.from_pretrained('lighteternal/nli-xlm-r-greek')
features = tokenizer(['Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'],
['Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο.'],
padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## To use the model for Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='lighteternal/nli-xlm-r-greek')
sent = "Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας"
candidate_labels = ["πολιτική", "τεχνολογία", "αθλητισμός"]
res = classifier(sent, candidate_labels)
print(res)
#outputs:
#{'sequence': 'Το Facebook κυκλοφόρησε τα πρώτα «έξυπνα» γυαλιά επαυξημένης πραγματικότητας', 'labels': ['τεχνολογία', 'αθλητισμός', 'πολιτική'], 'scores': [0.8380699157714844, 0.09086982160806656, 0.07106029987335205]}
```
### Acknowledgement
The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
### Citation info
Citation for the Greek model TBA.
Based on the work [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084)
Kudos to @nreimers (Nils Reimers) for his support on Github .
|
lkwate/legal-bigbird-us | a67b19db8f36aa5a8d662aadb739afff12e2405a | 2021-08-21T22:38:29.000Z | [
"pytorch",
"big_bird",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | lkwate | null | lkwate/legal-bigbird-us | 64 | 1 | transformers | 5,537 | Entry not found |
mrm8488/T5-base-finetuned-cuad | b26f9f3466cd0b250a9a813e8da4bd4a393f5ece | 2021-12-28T20:13:19.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:cuad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/T5-base-finetuned-cuad | 64 | 2 | transformers | 5,538 | ---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- cuad
model-index:
- name: T5-base-cuad-512
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-base fine-tuned on CUAD for Legal Contract Review (via QA)
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the cuad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2209
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2809 | 1.0 | 2795 | 0.2331 |
| 0.2459 | 2.0 | 5590 | 0.2253 |
| 0.2355 | 3.0 | 8385 | 0.2220 |
| 0.2212 | 4.0 | 11180 | 0.2203 |
| 0.2068 | 5.0 | 13975 | 0.2197 |
| 0.2085 | 6.0 | 16770 | 0.2194 |
| 0.1968 | 7.0 | 19565 | 0.2199 |
| 0.1906 | 8.0 | 22360 | 0.2200 |
| 0.1909 | 9.0 | 25155 | 0.2208 |
| 0.1788 | 10.0 | 27950 | 0.2209 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
mrm8488/t5-base-finetuned-news-titles-classification | e25716f46582a471dacb76058bbc7b7ca67af95c | 2021-06-23T12:52:30.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-base-finetuned-news-titles-classification | 64 | null | transformers | 5,539 | Entry not found |
patrickvonplaten/bert2bert-tiny | ca68b0275d9094f9a582a543519119f6f287239b | 2020-10-18T19:23:27.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | patrickvonplaten | null | patrickvonplaten/bert2bert-tiny | 64 | null | transformers | 5,540 | Entry not found |
tanay/layoutlm-funsd | 0a0442220e4d47fb4373c8d6911542706ec0f2e8 | 2021-06-30T07:21:21.000Z | [
"pytorch",
"layoutlm",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | tanay | null | tanay/layoutlm-funsd | 64 | null | transformers | 5,541 | Entry not found |
turing-usp/FinBertPTBR | 109ec9242690581dad9458abd9fef57a71d77d38 | 2021-12-03T01:30:42.000Z | [
"pytorch",
"bert",
"text-classification",
"pt",
"transformers",
"license:apache-2.0"
] | text-classification | false | turing-usp | null | turing-usp/FinBertPTBR | 64 | 2 | transformers | 5,542 | ---
language: pt
license: apache-2.0
widget:
- text: "O futuro de DI caiu 20 bps nesta manhã"
example_title: "Example 1"
- text: "O Nubank decidiu cortar a faixa de preço da oferta pública inicial (IPO) após revés no humor dos mercados internacionais com as fintechs."
example_title: "Example 2"
- text: "O Ibovespa acompanha correção do mercado e fecha com alta moderada"
example_title: "Example 3"
---
# FinBertPTBR : Financial Bert PT BR
FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.
## Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR")
model = AutoModel.from_pretrained("turing-usp/FinBertPTBR")
```
## Authors
- [Vinicius Carmo](https://www.linkedin.com/in/vinicius-cleves/)
- [Julia Pocciotti](https://www.linkedin.com/in/juliapocciotti/)
- [Luísa Heise](https://www.linkedin.com/in/lu%C3%ADsa-mendes-heise/)
- [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/)
|
vasudevgupta/mbart-bhasha-hin-eng | 14aee5dd763df50ed065a1e9bc8d1650fe9ff2db | 2021-05-12T03:36:02.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"dataset:pib",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | vasudevgupta | null | vasudevgupta/mbart-bhasha-hin-eng | 64 | null | transformers | 5,543 | ---
datasets: pib
widget:
- text: "नमस्ते! मैं वासुदेव गुप्ता हूं"
---
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning `facebook/mbart-large-cc25` on all samples (~260K) from Bhasha (pib_v1.3) Hindi-English parallel corpus. This checkpoint gives decent results for Hindi-english translation. |
lighteternal/fact-or-opinion-xlmr-el | bf405d95926471dbf248ab13af8441d8d87b3da7 | 2022-02-27T19:41:57.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"en",
"el",
"multilingual",
"transformers",
"fact-or-opinion",
"license:apache-2.0"
] | text-classification | false | lighteternal | null | lighteternal/fact-or-opinion-xlmr-el | 64 | null | transformers | 5,544 | ---
language:
- en
- el
- multilingual
tags:
- text-classification
- fact-or-opinion
- transformers
widget:
- text: "Ξεχωρίζει η καθηλωτική ερμηνεία του πρωταγωνιστή."
- text: "Η Ελλάδα είναι χώρα της Ευρώπης."
- text: "Tolkien was an English writer"
- text: "Tolkien is my favorite writer."
pipeline_tag: text-classification
license: apache-2.0
---
# Fact vs. opinion binary classifier, trained on a mixed EN-EL annotated corpus.
### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
This is an XLM-Roberta-base model with a binary classification head. Given a sentence, it can classify it either as a fact or an opinion based on its content.
You can use this model in any of the XLM-R supported languages for the same task, taking advantage of its 0-shot learning capabilities. However, the model was trained only using English and Greek sentences.
Legend of HuggingFace API labels:
* Label 0: Opinion/Subjective sentence
* Label 1: Fact/Objective sentence
## Dataset training info
The original dataset (available here: https://github.com/1024er/cbert_aug/tree/crayon/datasets/subj) contained aprox. 9000 annotated sentences (classified as subjective or objective). It was translated to Greek using Google Translate. The Greek version was then concatenated with the original English one to create the mixed EN-EL dataset.
The model was trained for 5 epochs, using batch size = 8. Detailed metrics and hyperparameters available on the "Metrics" tab.
## Evaluation Results on test set
| accuracy | precision | recall | f1 |
| ----------- | ----------- | ----------- | ----------- |
|0.952 | 0.945 | 0.960 | 0.952 |
## Acknowledgement
The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
|
grayson124/chatbotwaifu | f5f60f481106748b56ced7af5b696b16249ba5dd | 2022-03-01T02:49:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | grayson124 | null | grayson124/chatbotwaifu | 64 | null | transformers | 5,545 | ---
tags:
- conversational
---
#waifu bot |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-05_56_32 | 1bee69d0d0d1f4fb6ecc8b62693ca3889d5b4f41 | 2022-03-05T04:58:11.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_test_set_05_03_2022-05_56_32 | 64 | null | transformers | 5,546 | Entry not found |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_essays_05_03_2022-06_16_34 | 4d2afbc201073279ce4d2f56c02bb4c71b31f1f2 | 2022-03-05T05:18:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_essays_TEST_essays_05_03_2022-06_16_34 | 64 | null | transformers | 5,547 | Entry not found |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_editorials_05_03_2022-06_24_13 | d23e9f1ed74b383fc7deb0e5e9eec9b83a913cc4 | 2022-03-05T05:26:43.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_editorials_05_03_2022-06_24_13 | 64 | null | transformers | 5,548 | Entry not found |
ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_webDiscourse_05_03_2022-06_29_29 | e34093958db3a32457de4358d97a2f13c4adc513 | 2022-03-05T05:31:06.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ali2066 | null | ali2066/bert-base-uncased_token_itr0_0.0001_TRAIN_editorials_TEST_webDiscourse_05_03_2022-06_29_29 | 64 | null | transformers | 5,549 | Entry not found |
meedan/paraphrase-filipino-mpnet-base-v2 | 20d8b8d26840bc2f9d47e06ad9f576fa9ef86af3 | 2022-05-11T09:50:47.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | meedan | null | meedan/paraphrase-filipino-mpnet-base-v2 | 64 | null | sentence-transformers | 5,550 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# paraphrase-filipino-mpnet-base-v2
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.
This model was trained using the student--teacher approach outlined in [Reimers and Gurevych (2020)](https://aclanthology.org/2020.emnlp-main.365/).
The teacher model was [sentence-transformers/paraphrase-mpnet-base-v2](), and the student model was [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](), which is based on XLM-R. We trained the model for 2 epoch using a batch size of 64 on parallel data English--Tagalog and English--Filipino data from OPUS. We found the data to be of variable quality and filtered it to only include sentence pairs that the Compact Language Detection kit (CLDv3) identified reliably as being in Tagalog or Filipino. Other parameters were left unchanged from the example [make_multilingual_sys.py](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/multilingual/make_multilingual_sys.py) code in the sentence-transformers code base.
## 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
from scipy.spatial import distance
import itertools
model = SentenceTransformer('meedan/paraphrase-filipino-mpnet-base-v2')
sentences = ["saan pong mga lugar available ang pfizer vaccine? Thank you!","Ask ko lang po saan meron available na vaccine","Where is the vaccine available?"]
embeddings = model.encode(sentences)
dist=[distance.cosine(i,j) for i,j in itertools.combinations(embeddings,2)]
print(dist)
```
## 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
We machine translated the STS data from [SentEval](https://github.com/facebookresearch/SentEval) to Filipino using the Google Translation API and used this for evaluation alongside the original English-language STS data. We used Spearman's rank correlation coefficient. We found roughly the same performance as the original base model (sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on English while substantial gains were made for Filipino. For English, the average correlation is 0.80. For Filipino, it is 0.75.
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 79097 with parameters:
```
{'batch_size': 64, '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": 2,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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 --> |
avichr/Legal-heBERT | dd775034faa5d4668da364d21a396e1c51773ec1 | 2022-07-07T07:31:39.000Z | [
"pytorch",
"bert",
"fill-mask",
"arxiv:1911.03090",
"arxiv:2010.02559",
"transformers",
"autotrain_compatible"
] | fill-mask | false | avichr | null | avichr/Legal-heBERT | 64 | null | transformers | 5,551 | # Legal-HeBERT
Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br>
We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools.
## Training Data
Our training datasets are:
| Name | Hebrew Description | Size (GB) | Documents | Sentences | Words | Notes |
|----------------------------------------------------------------------------------------------------------------------------------- |-------------------------------------------------------------------------- |----------- |----------- |------------ |------------- |----------------------------------------- |
| The Israeli Law Book | ספר החוקים הישראלי | 0.05 | 2338 | 293352 | 4851063 | |
| Judgments of the Supreme Court | מאגר פסקי הדין של בית המשפט העליון | 0.7 | 212348 | 5790138 | 79672415 | |
| custody courts | החלטות בתי הדין למשמורת | 2.46 | 169,708 | 8,555,893 | 213,050,492 | |
| Law memoranda, drafts of secondary legislation and drafts of support tests that have been distributed to the public for comment | תזכירי חוק, טיוטות חקיקת משנה וטיוטות מבחני תמיכה שהופצו להערות הציבור | 0.4 | 3,291 | 294,752 | 7,218,960 | |
| Supervisors of Land Registration judgments | מאגר פסקי דין של המפקחים על רישום המקרקעין | 0.02 | 559 | 67,639 | 1,785,446 | |
| Decisions of the Labor Court - Corona | מאגר החלטות בית הדין לעניין שירות התעסוקה – קורונה | 0.001 | 146 | 3505 | 60195 | |
| Decisions of the Israel Lands Council | החלטות מועצת מקרקעי ישראל | | 118 | 11283 | 162692 | aggregate file |
| Judgments of the Disciplinary Tribunal and the Israel Police Appeals Tribunal | פסקי דין של בית הדין למשמעת ובית הדין לערעורים של משטרת ישראל | 0.02 | 54 | 83724 | 1743419 | aggregate files |
| Disciplinary Appeals Committee in the Ministry of Health | ועדת ערר לדין משמעתי במשרד הבריאות | 0.004 | 252 | 21010 | 429807 | 465 files are scanned and didn't parser |
| Attorney General's Positions | מאגר התייצבויות היועץ המשפטי לממשלה | 0.008 | 281 | 32724 | 813877 | |
| Legal-Opinion of the Attorney General | מאגר חוות דעת היועץ המשפטי לממשלה | 0.002 | 44 | 7132 | 188053 | |
| | | | | | | |
| total | | 3.665 | 389,139 | 15,161,152 | 309,976,419 | |
We thank <b>Yair Gardin</b> for the referring to the governance data, <b>Elhanan Schwarts</b> for collecting and parsing The Israeli law book, and <b>Jonathan Schler</b> for collecting the judgments of the supreme court.
## Training process
* Vocabulary size: 50,000 tokens
* 4 epochs (1M steps±)
* lr=5e-5
* mlm_probability=0.15
* batch size = 32 (for each gpu)
* NVIDIA GeForce RTX 2080 TI + NVIDIA GeForce RTX 3090 (1 week training)
### Additional training settings:
<b>Fine-tuned [HeBERT](https://github.com/avichaychriqui/HeBERT) model:</b> The first eight layers were freezed (like [Lee et al. (2019)](https://arxiv.org/abs/1911.03090) suggest)<br>
<b>Legal-HeBERT trained from scratch:</b> The training process is similar to [HeBERT](https://github.com/avichaychriqui/HeBERT) and inspired by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559) <br>
## How to use
The models can be found in huggingface hub and can be fine-tunned to any down-stream task:
```
# !pip install transformers==4.14.1
from transformers import AutoTokenizer, AutoModel
model_name = 'avichr/Legal-heBERT_ft' # for the fine-tuned HeBERT model
model_name = 'avichr/Legal-heBERT' # for legal HeBERT model trained from scratch
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model=model_name,
)
fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.")
```
## Stay tuned!
We are still working on our models and the datasets. We will edit this page as we progress. We are open for collaborations.
## If you used this model please cite us as :
Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai, Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts (June 27, 2022). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4147127
```
@article{chriqui2021hebert,
title={Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts},
author={Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai},
journal={SSRN preprint:4147127},
year={2022}
}
```
## Contact us
[Avichay Chriqui](mailto:[email protected]), The Coller AI Lab <br>
[Inbal yahav](mailto:[email protected]), The Coller AI Lab <br>
[Ittai Bar-Siman-Tov](mailto:[email protected]), the BIU Innovation Lab for Law, Data-Science and Digital Ethics <br>
Thank you, תודה, شكرا <br>
|
ml6team/keyphrase-extraction-kbir-kpcrowd | 04963560cd70e18a161b3f70e3d90eb97e13fcdc | 2022-06-16T14:19:57.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:midas/kpcrowd",
"arxiv:2112.08547",
"transformers",
"keyphrase-extraction",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/keyphrase-extraction-kbir-kpcrowd | 64 | null | transformers | 5,552 | ---
language: en
license: mit
tags:
- keyphrase-extraction
datasets:
- midas/kpcrowd
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: "In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks."
example_title: "Example 2"
model-index:
- name: DeDeckerThomas/keyphrase-extraction-kbir-kpcrowd
results:
- task:
type: keyphrase-extraction
name: Keyphrase Extraction
dataset:
type: midas/kpcrowd
name: kpcrowd
metrics:
- type: F1 (Seqeval)
value: 0.427
name: F1 (Seqeval)
- type: F1@M
value: 0.335
name: F1@M
---
# 🔑 Keyphrase Extraction Model: KBIR-KPCrowd
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/bloomberg/KBIR) as its base model and fine-tunes it on the [KPCrowd dataset](https://huggingface.co/datasets/midas/kpcrowd). KBIR or Keyphrase Boundary Infilling with Replacement is a pre-trained model which utilizes a multi-task learning setup for optimizing a combined loss of Masked Language Modeling (MLM), Keyphrase Boundary Infilling (KBI) and Keyphrase Replacement Classification (KRC).
You can find more information about the architecture in this [paper](https://arxiv.org/abs/2112.08547).
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 |
Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020.
## ✋ Intended Uses & Limitations
### 🛑 Limitations
* This keyphrase extraction model is very dataset-specific. It's not recommended to use this model for other domains, but you are free to test it out.
* Only works for English documents.
* Large number of annotated keyphrases.
* For a custom model, please consult the [training notebook]() for more information.
### ❓ 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, model_outputs):
results = super().postprocess(
model_outputs=model_outputs,
aggregation_strategy=AggregationStrategy.SIMPLE,
)
return np.unique([result.get("word").strip() for result in results])
```
```python
# Load pipeline
model_name = "ml6team/keyphrase-extraction-kbir-kpcrowd"
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' 'Classical' 'Keyphrase' 'Keyphrase extraction'
'classical' 'content' 'context' 'disadvantage' 'document' 'documents'
'extract' 'extraction' 'extraction process' 'frequency' 'human' 'humans'
'important' 'keyphrases' 'learning' 'linguistic' 'long-term'
'machine learning' 'meaning' 'methods' 'neural approaches' 'occurrence'
'process' 'quickly' 'semantic' 'statistical' 'technique' 'text'
'text analysis' 'understand' 'widely' 'words' 'work']
```
## 📚 Training Dataset
[KPCrowd](https://huggingface.co/datasets/midas/kpcrowd) is a broadcast news transcription dataset consisting of 500 English broadcast news stories from 10 different categories (art and culture, business, crime, fashion, health, politics us, politics world, science, sports, technology) with 50 docs per category. This dataset is annotated by multiple annotators that were required to look at the same news story and assign a set of keyphrases from the text itself.
You can find more information in the [paper](https://arxiv.org/abs/1306.4606).
## 👷♂️ Training Procedure
For more in detail information, you can take a look at the [training notebook]().
### 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("bloomberg/KBIR", add_prefix_space=True)
max_length = 512
# Dataset parameters
dataset_full_name = "midas/kpcrowd"
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 Inspec test set:
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M |
|:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:|
| Inspec Test Set | 0.47 | 0.07 | 0.12 | 0.46 | 0.13 | 0.20 | 0.37 | 0.33 | 0.33 |
For more information on the evaluation process, you can take a look at the keyphrase extraction [evaluation notebook]().
## 🚨 Issues
Please feel free to start discussions in the Community Tab. |
RonEliav/QA_discourse | b892506b6a932bbfc88437e67391895d1a3ca937 | 2022-07-07T20:14:43.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | RonEliav | null | RonEliav/QA_discourse | 64 | 1 | transformers | 5,553 | ---
license: afl-3.0
---
|
Gunulhona/tbsentmodel_v1 | 03cbb401984398fc6ec2021621e66cefd123ef39 | 2022-06-22T07:13:09.000Z | [
"pytorch",
"bart",
"feature-extraction",
"transformers"
] | feature-extraction | false | Gunulhona | null | Gunulhona/tbsentmodel_v1 | 64 | null | transformers | 5,554 | Entry not found |
cynthiachan/CTI_bert_base_cased | 340feea82746c8b0985c0836359ec9ab0d0afa0e | 2022-07-14T06:57:23.000Z | [
"pytorch",
"bert",
"token-classification",
"dataset:cynthiachan/FeedRef_10pct",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | cynthiachan | null | cynthiachan/CTI_bert_base_cased | 64 | null | transformers | 5,555 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cynthiachan/FeedRef_10pct
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: training
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cynthiachan/FeedRef_10pct
type: cynthiachan/FeedRef_10pct
args: FeedRef_10pct
metrics:
- name: Precision
type: precision
value: 0.8222222222222222
- name: Recall
type: recall
value: 0.7662721893491125
- name: F1
type: f1
value: 0.7932618683001531
- name: Accuracy
type: accuracy
value: 0.9940363242071022
---
<!-- 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. -->
# training
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef_10pct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0447
- Precision: 0.8222
- Recall: 0.7663
- F1: 0.7933
- Accuracy: 0.9940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1445 | 0.37 | 500 | 0.1098 | 0.2707 | 0.4556 | 0.3396 | 0.9747 |
| 0.0817 | 0.75 | 1000 | 0.0691 | 0.5357 | 0.4882 | 0.5108 | 0.9877 |
| 0.0729 | 1.12 | 1500 | 0.0780 | 0.7853 | 0.4112 | 0.5398 | 0.9887 |
| 0.0512 | 1.5 | 2000 | 0.0538 | 0.7311 | 0.6598 | 0.6936 | 0.9915 |
| 0.0564 | 1.87 | 2500 | 0.0624 | 0.7581 | 0.5562 | 0.6416 | 0.9909 |
| 0.0459 | 2.25 | 3000 | 0.0522 | 0.7841 | 0.6982 | 0.7387 | 0.9929 |
| 0.0408 | 2.62 | 3500 | 0.0482 | 0.8098 | 0.7308 | 0.7683 | 0.9935 |
| 0.0378 | 3.0 | 4000 | 0.0447 | 0.8222 | 0.7663 | 0.7933 | 0.9940 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
chizhikchi/Spanish_disease_finder | 5634b0c39174d2f41c29aec1fe28b31e60182e37 | 2022-07-15T11:19:42.000Z | [
"pytorch",
"roberta",
"token-classification",
"es",
"transformers",
"biomedical",
"clinical",
"ner",
"license:cc-by-4.0",
"autotrain_compatible"
] | token-classification | false | chizhikchi | null | chizhikchi/Spanish_disease_finder | 64 | 1 | transformers | 5,556 | ---
license: cc-by-4.0
language:
- es
tags:
- biomedical
- clinical
- ner
metrics:
- f1
widget:
- text: "Se realizó angiotomografía urgente de arterias pulmonares, que mostró tromboembolia pulmonar bilateral con dilatación ventricular derecha, además de opacidades periféricas parcheadas compatibles con neumonía por SARS-CoV-2, que se confirmó en la PCR."
example_title: "COVID-19"
- text: "El paciente presenta HTA en tratamiento con IECA y alfa-bloqueante, artritis reumatoide en tratamiento con corticoesteroide oral."
example_title: "Oncology"
- text: "Otros antecedentes de importancia son la captura de 30 insectos dentro de la vivienda, de los cuales tres fueron positivos a la infección por Trypanosoma cruzi y las características de la vivienda con materiales de construcción considerados de riesgo para la presencia del transmisor"
example_title: "Tropical medicine"
- text: "Tras la evaluación de la paciente por medio de exploración psicopatológica, la orientación diagnóstica es de trastorno adaptativo tipo mixto."
example_title: "Psychiatry"
- text: "Los hallazgos descritos son compatibles con quiste braquial del segundo arco complicado con proceso inflamatorio - infeccioso, sin poder descartar proceso maligno subyacente."
example_title: "Otorhinolaryngology"
---
# Disease mention recognizer for Spanish clinical texts 🦠🔬
This model derives from participation of SINAI team in [DISease TExt Mining Shared Task (DISTEMIST)](https://temu.bsc.es/distemist/). The DISTEMIST-entities subtrack required automatically finding disease mentions in clinical cases. Taking into account the length of clinical texts in the dataset, we opted for a sentence-level NER approach based on fine-tuning of a [RoBERTa model pre-trained on Spanish biomedical corpora](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-es).
# Evaluation and results
Using the biomedical model on EHRs can be considered as cross-domain experiment and the fact that our biomedical system exhibits encouraging results on the NER task highlights the existence of domain transfer potential between biomedical and clinical fields. Table below summarizes the official micro-average scores obtained by this model during the official evaluation. Team standings are available [here](http://participants-area.bioasq.org/results/DisTEMIST/).
| Precision | Recall | F1-score |
|-----------|--------|----------|
| 0.7520 | 0.7259 | 0.7387 |
# System description paper and citation
System description [paper](http://www.dei.unipd.it/~ferro/CLEF-WN-Drafts/CLEF2022/paper-17.pdf) is published in proceedings of 10th BioASQ Workshop, which will be held as a Lab in CLEF 2022 on September 5-8, 2022:
```bibtex:
@inproceedings{ChizhikovaEtAl:CLEF2022,
title = {SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentions},
author = {Mariia Chizhikova and Jaime Collado-Montañéz and Pilar López-Úbeda and Manuel C. Díaz-Galiano and L. Alfonso Ureña-López and M. Teresa Martín-Valdivia},
pages = {265--273},
url = {http://ceur-ws.org/Vol-XXX/#paper-17},
crossref = {CLEF2022}}
``` |
ZakaryaRouzki/t5-punctuation | 20efe8343037e45c47b4656e2d4f26ef0a771cda | 2022-07-04T09:48:53.000Z | [
"pytorch",
"t5",
"text2text-generation",
"fr",
"dataset:orange_sum",
"dataset:mlsum",
"transformers",
"french",
"punctuation",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | ZakaryaRouzki | null | ZakaryaRouzki/t5-punctuation | 64 | 0 | transformers | 5,557 | ---
language:
- "fr"
tags:
- t5
- french
- punctuation
license: apache-2.0
datasets:
- orange_sum
- mlsum
---
# 🚀 Text Punctuator Based on Transformers model T5.
T5 model fine-tuned for punctuation restoration.
Model currently supports only French Language. More language supports will be added later using mT5.
Train Datasets :
Model trained using 2 french datasets (around 500k records):
- [orange_sum](https://huggingface.co/datasets/orange_sum)
- [mlsum](https://huggingface.co/datasets/mlsum) (only french text)
More info will be added later.
## 🚀 Usage
**TextPunctuator as a wrapper of the model.**
1. Install the package.
```bash
pip install TextPunctuator
```
2. Simple example
```python
from Punctuator import TextPunctuator
punctuator = TextPunctuator(use_gpu=False)
# text input
text = "Sur la base de ces échanges Blake Lemoine a donc jugé que le système avait atteint \
un niveau de conscience lui permettant d'être sensible Ce dernier a ensuite envoyé \
par email un rapport sur la sensibilité supposée de LaMDA à deux cents employés de \
Google Très vite les dirigeants de l’entreprise ont rejeté les allégations"
text_punctuated = punctuator.punctuate(text, lang='fr')
text_punctuated
# output :
""" Sur la base de ces échanges, Blake Lemoine a donc jugé que le système avait atteint un niveau de
conscience lui permettant d’être sensible. Ce dernier a ensuite envoyé par email un rapport sur
la sensibilité supposée de LaMDA à deux cents employés de Google. Très vite, les dirigeants de
l’entreprise ont rejeté les allégations. """
```
## ☕ Contact
Contact [Zakarya ROUZKI ](mailto:[email protected]) or at [Linkedin](https://linkedin.com/in/rouzki).
|
adamnik/electra-event-detection | c20cddf32a6c2f5fef7f2da87f2f08dce3771e4d | 2022-07-20T01:27:14.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers",
"license:mit"
] | text-classification | false | adamnik | null | adamnik/electra-event-detection | 64 | null | transformers | 5,558 | ---
license: mit
---
|
sudo-s/modeversion1_m7_e4 | eacd70d56ffdc0559d8384e642ed4b1fe0964610 | 2022-07-23T22:44:11.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | image-classification | false | sudo-s | null | sudo-s/modeversion1_m7_e4 | 64 | null | transformers | 5,559 | ---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modeversion1_m7_e4
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. -->
# modeversion1_m7_e4
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0902
- Accuracy: 0.9731
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.073 | 0.06 | 100 | 3.9370 | 0.1768 |
| 3.4186 | 0.12 | 200 | 3.2721 | 0.2590 |
| 2.6745 | 0.18 | 300 | 2.6465 | 0.3856 |
| 2.2806 | 0.23 | 400 | 2.2600 | 0.4523 |
| 1.9275 | 0.29 | 500 | 1.9653 | 0.5109 |
| 1.6958 | 0.35 | 600 | 1.6815 | 0.6078 |
| 1.2797 | 0.41 | 700 | 1.4514 | 0.6419 |
| 1.3772 | 0.47 | 800 | 1.3212 | 0.6762 |
| 1.1765 | 0.53 | 900 | 1.1476 | 0.7028 |
| 1.0152 | 0.59 | 1000 | 1.0357 | 0.7313 |
| 0.7861 | 0.64 | 1100 | 1.0230 | 0.7184 |
| 1.0262 | 0.7 | 1200 | 0.9469 | 0.7386 |
| 0.8905 | 0.76 | 1300 | 0.8184 | 0.7756 |
| 0.6919 | 0.82 | 1400 | 0.8083 | 0.7711 |
| 0.7494 | 0.88 | 1500 | 0.7601 | 0.7825 |
| 0.5078 | 0.94 | 1600 | 0.6884 | 0.8056 |
| 0.7134 | 1.0 | 1700 | 0.6311 | 0.8160 |
| 0.4328 | 1.06 | 1800 | 0.5740 | 0.8252 |
| 0.4971 | 1.11 | 1900 | 0.5856 | 0.8290 |
| 0.5207 | 1.17 | 2000 | 0.6219 | 0.8167 |
| 0.4027 | 1.23 | 2100 | 0.5703 | 0.8266 |
| 0.5605 | 1.29 | 2200 | 0.5217 | 0.8372 |
| 0.2723 | 1.35 | 2300 | 0.4805 | 0.8565 |
| 0.401 | 1.41 | 2400 | 0.4811 | 0.8490 |
| 0.3419 | 1.47 | 2500 | 0.4619 | 0.8608 |
| 0.301 | 1.52 | 2600 | 0.4318 | 0.8712 |
| 0.2872 | 1.58 | 2700 | 0.4698 | 0.8573 |
| 0.2451 | 1.64 | 2800 | 0.4210 | 0.8729 |
| 0.2211 | 1.7 | 2900 | 0.3645 | 0.8851 |
| 0.3145 | 1.76 | 3000 | 0.4139 | 0.8715 |
| 0.2001 | 1.82 | 3100 | 0.3605 | 0.8864 |
| 0.3095 | 1.88 | 3200 | 0.4274 | 0.8675 |
| 0.1915 | 1.93 | 3300 | 0.2910 | 0.9101 |
| 0.2465 | 1.99 | 3400 | 0.2726 | 0.9103 |
| 0.1218 | 2.05 | 3500 | 0.2742 | 0.9129 |
| 0.0752 | 2.11 | 3600 | 0.2572 | 0.9183 |
| 0.1067 | 2.17 | 3700 | 0.2584 | 0.9203 |
| 0.0838 | 2.23 | 3800 | 0.2458 | 0.9212 |
| 0.1106 | 2.29 | 3900 | 0.2412 | 0.9237 |
| 0.092 | 2.34 | 4000 | 0.2232 | 0.9277 |
| 0.1056 | 2.4 | 4100 | 0.2817 | 0.9077 |
| 0.0696 | 2.46 | 4200 | 0.2334 | 0.9285 |
| 0.0444 | 2.52 | 4300 | 0.2142 | 0.9363 |
| 0.1046 | 2.58 | 4400 | 0.2036 | 0.9352 |
| 0.066 | 2.64 | 4500 | 0.2115 | 0.9365 |
| 0.0649 | 2.7 | 4600 | 0.1730 | 0.9448 |
| 0.0513 | 2.75 | 4700 | 0.2148 | 0.9339 |
| 0.0917 | 2.81 | 4800 | 0.1810 | 0.9438 |
| 0.0879 | 2.87 | 4900 | 0.1971 | 0.9388 |
| 0.1052 | 2.93 | 5000 | 0.1602 | 0.9508 |
| 0.0362 | 2.99 | 5100 | 0.1475 | 0.9556 |
| 0.041 | 3.05 | 5200 | 0.1328 | 0.9585 |
| 0.0156 | 3.11 | 5300 | 0.1389 | 0.9571 |
| 0.0047 | 3.17 | 5400 | 0.1224 | 0.9638 |
| 0.0174 | 3.22 | 5500 | 0.1193 | 0.9651 |
| 0.0087 | 3.28 | 5600 | 0.1276 | 0.9622 |
| 0.0084 | 3.34 | 5700 | 0.1134 | 0.9662 |
| 0.0141 | 3.4 | 5800 | 0.1239 | 0.9631 |
| 0.0291 | 3.46 | 5900 | 0.1199 | 0.9645 |
| 0.0049 | 3.52 | 6000 | 0.1103 | 0.9679 |
| 0.0055 | 3.58 | 6100 | 0.1120 | 0.9662 |
| 0.0061 | 3.63 | 6200 | 0.1071 | 0.9668 |
| 0.0054 | 3.69 | 6300 | 0.1032 | 0.9697 |
| 0.0041 | 3.75 | 6400 | 0.0961 | 0.9711 |
| 0.0018 | 3.81 | 6500 | 0.0930 | 0.9718 |
| 0.0032 | 3.87 | 6600 | 0.0918 | 0.9730 |
| 0.0048 | 3.93 | 6700 | 0.0906 | 0.9732 |
| 0.002 | 3.99 | 6800 | 0.0902 | 0.9731 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
SIMAS-UN/blaming_migrants | 4d36a7b17fe31507d758d6a8d2cc872ebe1a6c88 | 2022-07-24T03:56:22.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | SIMAS-UN | null | SIMAS-UN/blaming_migrants | 64 | null | transformers | 5,560 | Entry not found |
AI-Lab-Makerere/en_lg | d74d872401475fa5fce5768f44aaa8f4bfdf41e0 | 2022-06-28T08:38:46.000Z | [
"pytorch",
"marian",
"text2text-generation",
"unk",
"dataset:Eric Peter/autonlp-data-EN-LUG",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | AI-Lab-Makerere | null | AI-Lab-Makerere/en_lg | 63 | null | transformers | 5,561 | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Eric Peter/autonlp-data-EN-LUG
co2_eq_emissions: 133.0219882109991
---
# Model Trained Using AutoNLP
- Problem type: Machine Translation
- Model ID: 474612462
- CO2 Emissions (in grams): 133.0219882109991
## Validation Metrics
- Loss: 1.336498737335205
- Rouge1: 52.5404
- Rouge2: 31.6639
- RougeL: 50.1696
- RougeLsum: 50.3398
- Gen Len: 39.046
## 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/EricPeter/autonlp-EN-LUG-474612462
``` |
Helsinki-NLP/opus-mt-hu-fr | c2885a2c79baeea474b021a129a25b561498e1dd | 2021-09-09T22:10:59.000Z | [
"pytorch",
"marian",
"text2text-generation",
"hu",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-hu-fr | 63 | null | transformers | 5,562 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-hu-fr
* source languages: hu
* target languages: fr
* OPUS readme: [hu-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hu-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/hu-fr/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-fr/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hu-fr/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.hu.fr | 50.3 | 0.660 |
|
Helsinki-NLP/opus-mt-sl-fi | db1452a4a0ec2dfaa1eee5ed4b9881298f84cd50 | 2021-09-10T14:03:42.000Z | [
"pytorch",
"marian",
"text2text-generation",
"sl",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-sl-fi | 63 | null | transformers | 5,563 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sl-fi
* source languages: sl
* target languages: fi
* OPUS readme: [sl-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sl-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sl-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sl-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sl.fi | 23.4 | 0.517 |
|
Kowsher/bangla-bert | f82455bb9aad640c5a1ce8f259e3d0952079fa27 | 2022-03-06T15:42:48.000Z | [
"pytorch",
"bert",
"fill-mask",
"bn",
"dataset:BanglaLM dataset",
"arxiv:1810.04805",
"transformers",
"Bert base Bangla",
"Bengali Bert",
"Bengali lm",
"Bangla Base Bert",
"Bangla Bert language model",
"Bangla Bert",
"autotrain_compatible"
] | fill-mask | false | Kowsher | null | Kowsher/bangla-bert | 63 | 1 | transformers | 5,564 | ---
language: bn
tags:
- Bert base Bangla
- Bengali Bert
- Bengali lm
- Bangla Base Bert
- Bangla Bert language model
- Bangla Bert
datasets:
- BanglaLM dataset
---
# Bangla BERT Base
Here we published a pretrained Bangla bert language model as **bangla-bert**! which is now available in huggingface model hub.
Here we described [bangla-bert](https://github.com/Kowsher/bert-base-bangla) which is a pretrained Bangla language model based on mask language modeling described in [BERT](https://arxiv.org/abs/1810.04805) and the GitHub [repository](https://github.com/google-research/bert)
## Corpus Details
We trained the Bangla bert language model using BanglaLM dataset from kaggle [BanglaLM](https://www.kaggle.com/gakowsher/bangla-language-model-dataset). There is 3 version of dataset which is almost 40GB.
After downloading the dataset, we went on the way to mask LM.
**bangla-bert Tokenizer**
```py
from transformers import AutoTokenizer, AutoModel
bnbert_tokenizer = AutoTokenizer.from_pretrained("Kowsher/bangla-bert")
text = "খাঁটি সোনার চাইতে খাঁটি আমার দেশের মাটি"
bnbert_tokenizer.tokenize(text)
# output: ['খাটি', 'সে', '##ানার', 'চাইতে', 'খাটি', 'আমার', 'দেশের', 'মাটি']
```
**MASK Generation**
here, we can use bert base bangla model as for masked language modeling:
```py
from transformers import BertForMaskedLM, BertTokenizer, pipeline
model = BertForMaskedLM.from_pretrained("Kowsher/bangla-bert")
tokenizer = BertTokenizer.from_pretrained("Kowsher/bangla-bert")
nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"আমি বাংলার গান {nlp.tokenizer.mask_token}"):
print(pred)
# {'sequence': 'আমি বাংলার গান লিখি', 'score': 0.17955434322357178, 'token': 24749, 'token_str': 'লিখি'}
nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"তুই রাজাকার তুই {nlp.tokenizer.mask_token}"):
print(pred)
# {'sequence': 'তুই রাজাকার তুই রাজাকার', 'score': 0.9975168704986572, 'token': 13401, 'token_str': 'রাজাকার'}
nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
for pred in nlp(f"বাংলা আমার {nlp.tokenizer.mask_token}"):
print(pred)
# {'sequence': 'বাংলা আমার অহংকার', 'score': 0.5679506063461304, 'token': 19009, 'token_str': 'অহংকার'}
```
**Cite this work**
Kowsher, Md., BERT Base Bangla: A Pretrained Transformer Based Bangla Bert Model (September 15, 2021). Research on Computational Language, Available at SSRN: https://ssrn.com/abstract=
## Author
[Kowsher](http://kowsher.org/)
|
Maunish/ecomm-sbert | f4fc1e544a76a2bc66970a4dad3b60d6fdc69855 | 2022-02-09T17:47:29.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | Maunish | null | Maunish/ecomm-sbert | 63 | null | transformers | 5,565 | ---
license: apache-2.0
---
|
Milos/slovak-gpt-j-162M | 3ebb80b851786d0823af43ca854ce619f62d8aa4 | 2022-02-18T14:02:12.000Z | [
"pytorch",
"gptj",
"text-generation",
"sk",
"arxiv:2104.09864",
"transformers",
"Slovak GPT-J",
"causal-lm",
"license:gpl-3.0"
] | text-generation | false | Milos | null | Milos/slovak-gpt-j-162M | 63 | null | transformers | 5,566 | ---
language:
- sk
tags:
- Slovak GPT-J
- pytorch
- causal-lm
license: gpl-3.0
---
# Slovak GPT-J-162M
Slovak GPT-J-162M is the first model released in Slovak GPT-J series and the very first publicly available transformer trained predominantly on Slovak corpus. Since the initial release two other models were made public, [Slovak GPT-J-405M](https://huggingface.co/Milos/slovak-gpt-j-405M) and the largest [Slovak GPT-J-1.4B](https://huggingface.co/Milos/slovak-gpt-j-1.4B).
## Model Description
Model is based on [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax/) and has over 162M trainable parameters.
<figure>
| Hyperparameter | Value |
|----------------------|-------------------------------------------------------------------------------------------------------------------------------|
| \\(n_{parameters}\\) | 162,454,608 |
| \\(n_{layers}\\) | 12 |
| \\(d_{model}\\) | 768 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50256 (same tokenizer as GPT-2/3†) |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
<p><strong>†</strong> ByteLevelBPETokenizer was trained on the same Slovak corpus.</p></figure>
## Training data
Slovak GPT-J-162M was trained on a privately collected dataset consisting of predominantly Slovak text spanning different categories, e.g. web, news articles or even biblical texts - in total, over 40GB of text data was used to train this model.
The dataset was preprocessed and cleaned in a specific way that involves minor but a few caveats, so in order to achieve the expected performance, feel free to refer to [How to use] section. Please, keep in mind that despite the effort to remove inappropriate parts of the corpus, the model still might generate sensitive content or leak sensitive information.
## Training procedure
This model was trained for almost 37 billion tokens over 69,001 steps on TPU v3-8 pod. The cross-entropy validation loss at the last step was 3.065.
## Intended Use
Same as the original GPT-J, Slovak GPT-J learns an inner representation of the language that can be used to extract features useful for downstream tasks, however, the intended use is text generation from a prompt.
### How to use
This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-162M")
model = AutoModelForCausalLM.from_pretrained("Milos/slovak-gpt-j-162M")
```
When generating a prompt keep in mind these three things, and you should be good to go:
1. Never leave trailing whitespaces. There's a difference between how tokenizer encodes "Mám rád slovenčinu" (no space after `slovenčinu`) and "Mám rád slovenčinu " (trailing space after `slovenčinu`), i.e `[12805, 2872, 46878]` != `[12805, 2872, 46878, 221]`.
2. Always use good ol' US English primary double quotation marks, i.e. `""` instead of `„“`.
3. In case of a new line always enter `\n\n` instead of a single `\n`
To illustrate an example of a basic text generation:
```
>>> prompt = "Moje najobľubenejšie mesto na severe Slovenska je"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input)
>>> tokenizer.decode(output[0])
'Moje najobľubenejšie mesto na severe Slovenska je Žilina.\n\nV Žiline sa nachádza množstvo zaujímavých miest'
```
### Capabilities, Limitations, and Biases
First and foremost, the capability of this particular model is very limited due to its relatively small size totalling only 162M parameters, hence the intended use of this particular model is to educate and have fun! :)
Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output when prompt is in Czech:
```
>>> prompt = "Věta nesmí být sprostá a musí být zcela"
>>> encoded_input = tokenizer(prompt, return_tensors='pt')
>>> output = model.generate(**encoded_input, max_length=16)
>>> tokenizer.decode(output[0])
'Věta nesmí být sprostá a musí být zcela věrná.'
```
## Citation and Related Information
This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now. Based on the popularity and interest in this model I might release _substantially_ larger versions of Slovak GPT-J models that are way more capable.
If you use this model or have any questions about it feel free to hit me up at [twitter](https://twitter.com/miloskondela) or check out my [github](https://github.com/kondela) profile.
### BibTeX entry
To cite this model:
```bibtex
@misc{slovak-gpt-j-162m,
author = {Kondela, Milos},
title = {{Slovak GPT-J-162M}},
howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-162M}},
year = 2022,
month = February
}
```
To cite the codebase that trained this model:
```bibtex
@misc{mesh-transformer-jax,
author = {Wang, Ben},
title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
year = 2021,
month = May
}
```
## Acknowledgements
This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/). |
Nicki/gpt3-base | 656bb22aff7fd5afb1f92dfd7a940a9e0592fed0 | 2021-07-29T05:53:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | Nicki | null | Nicki/gpt3-base | 63 | null | transformers | 5,567 | Entry not found |
Prompsit/paraphrase-bert-en | 75a238bbb26c0f0dca7ec37b5e16b6381f7cdc56 | 2021-12-23T12:03:17.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers"
] | text-classification | false | Prompsit | null | Prompsit/paraphrase-bert-en | 63 | 2 | transformers | 5,568 | ---
pipeline_tag: text-classification
inference: false
language: en
tags:
- transformers
---
# Prompsit/paraphrase-bert-en
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "bert-base-uncased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How to use it
The model answer the following question: Is "phrase B" a paraphrase of "phrase A".
Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
Resulting probabilities correspond to classes:
* 0: Not a paraphrase
* 1: It's a paraphrase
So, considering the phrase "may be addressed" and a candidate paraphrase like "could be included", you can use the model like this:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-bert-en")
model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-bert-en")
input = tokenizer('may be addressed','could be included',return_tensors='pt')
logits = model(**input).logits
soft = torch.nn.Softmax(dim=1)
print(soft(logits))
```
Code output is:
```
tensor([[0.1592, 0.8408]], grad_fn=<SoftmaxBackward>)
```
As the probability of 1 (=It's a paraphrase) is 0.84 and the probability of 0 (=It is not a paraphrase) is 0.15, we can conclude, for our previous example, that "could be included" is a paraphrase of "may be addressed".
# Evaluation results
We have used as test dataset 16500 pairs of phrases human tagged.
Metrics obtained are:
```
metrics={
'test_loss': 0.5660144090652466,
'test_accuracy': 0.8170742794799527,
'test_precision': 0.7043977055449331,
'test_recall': 0.5978578383641675,
'test_f1': 0.6467696629213483,
'test_matthews_correlation': 0.5276716223607356,
'test_runtime': 19.3345,
'test_samples_per_second': 568.88,
'test_steps_per_second': 17.792
}
``` |
SkolkovoInstitute/ruT5-base-detox | f986f748295a7e9ba6427ff7e1be6b348649c5a0 | 2021-12-29T09:13:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SkolkovoInstitute | null | SkolkovoInstitute/ruT5-base-detox | 63 | null | transformers | 5,569 | This is the detoxification baseline model trained on the [train](https://github.com/skoltech-nlp/russe_detox_2022/blob/main/data/input/train.tsv) part of "RUSSE 2022: Russian Text Detoxification Based on Parallel Corpora" competition. The source sentences are Russian toxic messages from Odnoklassniki, Pikabu, and Twitter platforms. The base model is [ruT5](https://huggingface.co/sberbank-ai/ruT5-base) provided from Sber.
**How to use**
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
base_model_name = 'sberbank-ai/ruT5-base'
model_name = 'SkolkovoInstitute/ruT5-base-detox'
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
```
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |
ainize/gpt2-spongebob-script-large | 698090c3da4ad7e39a7d6d0ba71ecc7bd9046729 | 2021-05-21T12:18:42.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | ainize | null | ainize/gpt2-spongebob-script-large | 63 | null | transformers | 5,570 | ### Model information
Fine tuning data: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
License: CC-BY-SA
Base model: gpt-2 large
Epoch: 50
Train runtime: 14723.0716 secs
Loss: 0.0268
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Spongebob?branch=master)
Demo page: [End-point](https://master-gpt2-spongebob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp) |
deepset/xlm-roberta-base-squad2-distilled | 350a9ad58048570793a7f0899dd988c69c641a51 | 2022-07-26T09:06:02.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"multilingual",
"dataset:squad_v2",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/xlm-roberta-base-squad2-distilled | 63 | 3 | transformers | 5,571 | ---
language: multilingual
datasets:
- squad_v2
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
# deepset/xlm-roberta-base-squad2-distilled
- haystack's distillation feature was used for training. deepset/xlm-roberta-large-squad2 was used as the teacher model.
## Overview
**Language model:** deepset/xlm-roberta-base-squad2-distilled
**Language:** Multilingual
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
**Infrastructure**: 1x Tesla v100
## Hyperparameters
```
batch_size = 56
n_epochs = 4
max_seq_len = 384
learning_rate = 3e-5
lr_schedule = LinearWarmup
embeds_dropout_prob = 0.1
temperature = 3
distillation_loss_weight = 0.75
```
## Usage
### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled")
# or
reader = TransformersReader(model_name_or_path="deepset/xlm-roberta-base-squad2-distilled",tokenizer="deepset/xlm-roberta-base-squad2-distilled")
```
For a complete example of ``deepset/xlm-roberta-base-squad2-distilled`` being used for [question answering], check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/xlm-roberta-base-squad2-distilled"
# 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)
```
## Performance
Evaluated on the SQuAD 2.0 dev set
```
"exact": 74.06721131980123%
"f1": 76.39919553344667%
```
## Authors
- Timo Möller: `timo.moeller [at] deepset.ai`
- Julian Risch: `julian.risch [at] deepset.ai`
- Malte Pietsch: `malte.pietsch [at] deepset.ai`
- Michel Bartels: `michel.bartels [at] deepset.ai`
## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/>
</div>
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/deepset-logo-colored.svg" class="w-40"/>
</div>
</div>
[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
Some of our other work:
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
- [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)
## Get in touch and join the Haystack community
<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://haystack.deepset.ai">Documentation</a></strong>.
We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community/join"><img alt="slack" class="h-7 inline-block m-0" style="margin: 0" src="https://huggingface.co/spaces/deepset/README/resolve/main/Slack_RGB.png"/>community open to everyone!</a></strong></p>
[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) |
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q | f74da44e63bbe972163212b46ce195eca92eb35a | 2021-07-25T21:32:52.000Z | [
"pytorch",
"mpnet",
"fill-mask",
"arxiv:2102.07033",
"arxiv:2104.08727",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q | 63 | null | sentence-transformers | 5,572 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-QA_v1-mpnet-asymmetric-Q
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used two separate pretrained [mpnet-base](https://huggingface.co/microsoft/mpnet-base) models and trained them using contrastive learning objective. Question and answer pairs from StackExchange and other datasets were used as training data to make the model robust to Question / Answer embedding similarity.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
This model set is intended to be used as a sentence encoder for a search engine. Given an input sentence, it ouptuts a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
Two models should be used on conjunction for Semantic Search purposes.
1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q) - Model to encode Questions
1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A) - Model to encode Answers
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q')
model_A = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A')
question = "Replace me by any question you'd like."
question_embbedding = model_Q.encode(text)
answer = "Replace me by any answer you'd like."
answer_embbedding = model_A.encode(text)
answer_likeliness = cosine_similarity(question_embedding, answer_embedding)
```
# Training procedure
## Pre-training
We use the pretrained [`Mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | |
jasminejwebb/KeywordIdentifier | ff14521a2d83c63f90b8d9cd7a0c1710a06c46f0 | 2022-02-11T21:48:06.000Z | [
"pytorch",
"xlnet",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | jasminejwebb | null | jasminejwebb/KeywordIdentifier | 63 | 4 | transformers | 5,573 | Entry not found |
jcblaise/roberta-tagalog-large | acb6b204dfb1afdd7476eae5da234cbcf8899846 | 2021-11-12T03:25:48.000Z | [
"pytorch",
"tf",
"roberta",
"fill-mask",
"tl",
"transformers",
"tagalog",
"filipino",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | jcblaise | null | jcblaise/roberta-tagalog-large | 63 | null | transformers | 5,574 | ---
language: tl
tags:
- roberta
- tagalog
- filipino
license: cc-by-sa-4.0
inference: false
---
# RoBERTa Tagalog Large
Tagalog RoBERTa trained as an improvement over our previous Tagalog pretrained Transformers. Trained with TLUnified, a newer, larger, more topically-varied pretraining corpus for Filipino. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community.
This model is a cased model. We do not release uncased RoBERTa models.
## Citations
All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work:
```
@article{cruz2021improving,
title={Improving Large-scale Language Models and Resources for Filipino},
author={Jan Christian Blaise Cruz and Charibeth Cheng},
journal={arXiv preprint arXiv:2111.06053},
year={2021}
}
```
## Data and Other Resources
Data used to train this model as well as other benchmark datasets in Filipino can be found in my website at https://blaisecruz.com
## Contact
If you have questions, concerns, or if you just want to chat about NLP and low-resource languages in general, you may reach me through my work email at [email protected]
|
jshu/gpt2-medium-ontapdoc-gen-2 | bf64adef84bb910d69b9630a9894ff189f254ff3 | 2021-11-19T18:29:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jshu | null | jshu/gpt2-medium-ontapdoc-gen-2 | 63 | null | transformers | 5,575 | Entry not found |
mrm8488/mbart-large-finetuned-opus-it-en-translation | 040c4a41cf5b171be94ffc43d22e174da8eb69a8 | 2021-01-27T13:19:19.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"it",
"en",
"dataset:opus100",
"transformers",
"translation",
"autotrain_compatible"
] | translation | false | mrm8488 | null | mrm8488/mbart-large-finetuned-opus-it-en-translation | 63 | 1 | transformers | 5,576 | ---
tags:
- translation
language:
- it
- en
datasets:
- opus100
---
### mbart-large-it-en
This is mbart-large-cc25, finetuned on opus100 for Italian to English translation.
It scores BLEU **25.82** on test set. |
philippelaban/keep_it_simple | e8fe51874a3c97787bbfe6b2ef115b4b409ccdad | 2022-02-09T22:42:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"simplification",
"license:apache-2.0"
] | text-generation | false | philippelaban | null | philippelaban/keep_it_simple | 63 | 1 | transformers | 5,577 | ---
language:
- en
tags:
- simplification
license: apache-2.0
datasets:
- cnn_dailymail
widget:
- text: "A capsule containing asteroid soil samples landed in the Australian Outback. The precision required to carry out the mission thrilled many.<|endoftext|>"
example_title: "Example 1"
---
# Try out in the Hosted inference API
In the right panel, you can try the model (although it only handles a short sequence length).
Feel free to try Example 1, and modify it to inspect model ability.
# Model Loading
The model can be loaded in the following way:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("philippelaban/keep_it_simple")
kis_model = AutoModelForCausalLM.from_pretrained("philippelaban/keep_it_simple")
```
# Example use
And then used by first inputting a paragraph for simplification, followed by a `bos_token` to indicate to the model to start simplifying.
Imagine we want to simplify the following paragraph:
```
A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space
by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6.
The extremely high precision required to carry out the mission thrilled many in Japan,
who said they took pride in its success.
```
The following code can be run:
```
paragraph = """A small capsule containing asteroid soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 spacecraft landed as planned in the Australian Outback on December 6. The extremely high precision required to carry out the mission thrilled many in Japan, who said they took pride in its success."""
start_id = tokenizer.bos_token_id
tokenized_paragraph = [(tokenizer.encode(text=paragraph) + [start_id])]
input_ids = torch.LongTensor(tokenized_paragraph)
output_ids = kis_model.generate(input_ids, max_length=150, num_beams=4, do_sample=True, num_return_sequences=8)
output_ids = output_ids[:, input_ids.shape[1]:]
output = tokenizer.batch_decode(output_ids)
output = [o.replace(tokenizer.eos_token, "") for o in output]
for o in output:
print("----")
print(o)
```
# Example output
When run, an output similar to the following should be obtained:
A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise, said many in Japan, and they took pride in its success.
A small capsule containing samples of asteroid soil that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was extremely precise and well thought-out, said many in Japan, who took pride in the mission.
A small capsule containing soil samples that was dropped from 136,700 miles, Japan's Hayabusa2 space probe, landed as planned on December 6. The mission was designed to test the performance of the country's space fleet, which many said took pride in its success.
A small capsule containing soil samples that was dropped from 136,700 miles in space by Japan's Hayabusa2 probe was followed by a landing on the Outback. The precise timing of the mission thrilled many in Japan, who said they took pride in its success.
# Github repo
You can access more information, access to the scoring function, the training script, or an example training log on the Github repo:
https://github.com/tingofurro/keep_it_simple |
projecte-aina/roberta-base-ca-cased-sts | 1e644c69274edfbb239eccbf565dd6a38d2cef27 | 2022-06-16T08:04:23.000Z | [
"pytorch",
"roberta",
"text-classification",
"ca",
"dataset:projecte-aina/sts-ca",
"arxiv:1907.11692",
"transformers",
"catalan",
"semantic textual similarity",
"sts-ca",
"CaText",
"Catalan Textual Corpus",
"license:apache-2.0",
"model-index"
] | text-classification | false | projecte-aina | null | projecte-aina/roberta-base-ca-cased-sts | 63 | null | transformers | 5,578 | ---
language:
- ca
pipeline_tag: text-classification
license: apache-2.0
tags:
- "catalan"
- "semantic textual similarity"
- "sts-ca"
- "CaText"
- "Catalan Textual Corpus"
datasets:
- "projecte-aina/sts-ca"
metrics:
- "pearson"
model-index:
- name: roberta-base-ca-cased-sts
results:
- task:
type: text-classification
dataset:
type: projecte-aina/sts-ca
name: sts-ca
metrics:
- type: pearson
value: 0.7973
---
# Catalan BERTa (RoBERTa-base) finetuned for Semantic Textual Similarity.
The **roberta-base-ca-cased-sts** is a Semantic Textual Similarity (STS) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
## Datasets
We used the STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and evaluation.
## Evaluation and results
We evaluated the _roberta-base-ca-cased-sts_ on the STS-ca test set against standard multilingual and monolingual baselines:
| Model | STS-ca (Pearson) |
|:------------|:----|
| roberta-base-ca-cased-sts | **79.73** |
| mBERT | 76.34 |
| XLM-RoBERTa | 75.40 |
| WikiBERT-ca | 77.18 |
For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
## How to use
To get the correct<sup>1</sup> model's prediction scores with values between 0.0 and 5.0, use the following code:
```python
from transformers import pipeline, AutoTokenizer
from scipy.special import logit
model = 'projecte-aina/roberta-base-ca-cased-sts'
tokenizer = AutoTokenizer.from_pretrained(model)
pipe = pipeline('text-classification', model=model, tokenizer=tokenizer)
def prepare(sentence_pairs):
sentence_pairs_prep = []
for s1, s2 in sentence_pairs:
sentence_pairs_prep.append(f"{tokenizer.cls_token} {s1}{tokenizer.sep_token}{tokenizer.sep_token} {s2}{tokenizer.sep_token}")
return sentence_pairs_prep
sentence_pairs = [("El llibre va caure per la finestra.", "El llibre va sortir volant."),
("M'agrades.", "T'estimo."),
("M'agrada el sol i la calor", "A la Garrotxa plou molt.")]
predictions = pipe(prepare(sentence_pairs), add_special_tokens=False)
# convert back to scores to the original 1 and 5 interval
for prediction in predictions:
prediction['score'] = logit(prediction['score'])
print(predictions)
```
Expected output:
```
[{'label': 'SIMILARITY', 'score': 2.4280577200108384},
{'label': 'SIMILARITY', 'score': 2.132843521240822},
{'label': 'SIMILARITY', 'score': 1.615101695426227}]
```
<sup>1</sup> _**avoid using the widget** scores since they are normalized and do not reflect the original annotation values._
## Citing
If you use any of these resources (datasets or models) in your work, please cite our latest paper:
```bibtex
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
|
razent/cotext-2-cc | c44d0273e79da8a1ec3273c80b300ade6277a493 | 2022-03-15T03:03:51.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"feature-extraction",
"code",
"dataset:code_search_net",
"transformers"
] | feature-extraction | false | razent | null | razent/cotext-2-cc | 63 | null | transformers | 5,579 | ---
language: code
datasets:
- code_search_net
---
# CoText (2-CC)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://aclanthology.org/2021.nlp4prog-1.5.pdf)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
```shell
"go"
"java"
"javascript"
"php"
"python"
"ruby"
```
For more details, do check out [our Github repo](https://github.com/justinphan3110/CoTexT).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("razent/cotext-2-cc")
model = AutoModelForSeq2SeqLM.from_pretrained("razent/cotext-2-cc")
sentence = "def add(a, b): return a + b"
text = "python: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
early_stopping=True
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(line)
```
## Citation
```
@inproceedings{phan-etal-2021-cotext,
title = "{C}o{T}ex{T}: Multi-task Learning with Code-Text Transformer",
author = "Phan, Long and
Tran, Hieu and
Le, Daniel and
Nguyen, Hieu and
Annibal, James and
Peltekian, Alec and
Ye, Yanfang",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.5",
doi = "10.18653/v1/2021.nlp4prog-1.5",
pages = "40--47"
}
``` |
wietsedv/xlm-roberta-base-ft-udpos28-tr | 86a24b17a2c653386b580c843d3e38e3791c0c44 | 2022-02-25T09:59:31.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"tr",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-tr | 63 | null | transformers | 5,580 |
---
language:
- tr
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-tr
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 74.4
- type: accuracy
name: Dutch Test accuracy
value: 73.7
- type: accuracy
name: German Test accuracy
value: 73.5
- type: accuracy
name: Italian Test accuracy
value: 73.2
- type: accuracy
name: French Test accuracy
value: 71.4
- type: accuracy
name: Spanish Test accuracy
value: 71.1
- type: accuracy
name: Russian Test accuracy
value: 77.9
- type: accuracy
name: Swedish Test accuracy
value: 74.5
- type: accuracy
name: Norwegian Test accuracy
value: 69.2
- type: accuracy
name: Danish Test accuracy
value: 73.8
- type: accuracy
name: Low Saxon Test accuracy
value: 45.8
- type: accuracy
name: Akkadian Test accuracy
value: 39.8
- type: accuracy
name: Armenian Test accuracy
value: 80.9
- type: accuracy
name: Welsh Test accuracy
value: 62.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 63.7
- type: accuracy
name: Albanian Test accuracy
value: 71.5
- type: accuracy
name: Slovenian Test accuracy
value: 62.3
- type: accuracy
name: Guajajara Test accuracy
value: 41.3
- type: accuracy
name: Kurmanji Test accuracy
value: 68.0
- type: accuracy
name: Turkish Test accuracy
value: 88.4
- type: accuracy
name: Finnish Test accuracy
value: 81.1
- type: accuracy
name: Indonesian Test accuracy
value: 71.5
- type: accuracy
name: Ukrainian Test accuracy
value: 76.8
- type: accuracy
name: Polish Test accuracy
value: 74.3
- type: accuracy
name: Portuguese Test accuracy
value: 76.7
- type: accuracy
name: Kazakh Test accuracy
value: 81.1
- type: accuracy
name: Latin Test accuracy
value: 68.2
- type: accuracy
name: Old French Test accuracy
value: 47.5
- type: accuracy
name: Buryat Test accuracy
value: 62.6
- type: accuracy
name: Kaapor Test accuracy
value: 24.6
- type: accuracy
name: Korean Test accuracy
value: 63.7
- type: accuracy
name: Estonian Test accuracy
value: 82.0
- type: accuracy
name: Croatian Test accuracy
value: 72.3
- type: accuracy
name: Gothic Test accuracy
value: 24.1
- type: accuracy
name: Swiss German Test accuracy
value: 41.1
- type: accuracy
name: Assyrian Test accuracy
value: 23.0
- type: accuracy
name: North Sami Test accuracy
value: 45.2
- type: accuracy
name: Naija Test accuracy
value: 36.0
- type: accuracy
name: Latvian Test accuracy
value: 80.0
- type: accuracy
name: Chinese Test accuracy
value: 55.9
- type: accuracy
name: Tagalog Test accuracy
value: 56.2
- type: accuracy
name: Bambara Test accuracy
value: 30.0
- type: accuracy
name: Lithuanian Test accuracy
value: 81.2
- type: accuracy
name: Galician Test accuracy
value: 72.4
- type: accuracy
name: Vietnamese Test accuracy
value: 57.0
- type: accuracy
name: Greek Test accuracy
value: 80.2
- type: accuracy
name: Catalan Test accuracy
value: 69.1
- type: accuracy
name: Czech Test accuracy
value: 75.8
- type: accuracy
name: Erzya Test accuracy
value: 52.7
- type: accuracy
name: Bhojpuri Test accuracy
value: 50.8
- type: accuracy
name: Thai Test accuracy
value: 49.0
- type: accuracy
name: Marathi Test accuracy
value: 77.9
- type: accuracy
name: Basque Test accuracy
value: 66.8
- type: accuracy
name: Slovak Test accuracy
value: 75.1
- type: accuracy
name: Kiche Test accuracy
value: 43.1
- type: accuracy
name: Yoruba Test accuracy
value: 31.7
- type: accuracy
name: Warlpiri Test accuracy
value: 48.6
- type: accuracy
name: Tamil Test accuracy
value: 79.5
- type: accuracy
name: Maltese Test accuracy
value: 34.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 58.5
- type: accuracy
name: Icelandic Test accuracy
value: 68.9
- type: accuracy
name: Mbya Guarani Test accuracy
value: 33.6
- type: accuracy
name: Urdu Test accuracy
value: 60.5
- type: accuracy
name: Romanian Test accuracy
value: 69.6
- type: accuracy
name: Persian Test accuracy
value: 71.3
- type: accuracy
name: Apurina Test accuracy
value: 50.2
- type: accuracy
name: Japanese Test accuracy
value: 44.4
- type: accuracy
name: Hungarian Test accuracy
value: 86.4
- type: accuracy
name: Hindi Test accuracy
value: 63.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 36.3
- type: accuracy
name: Komi Permyak Test accuracy
value: 51.0
- type: accuracy
name: Faroese Test accuracy
value: 59.5
- type: accuracy
name: Sanskrit Test accuracy
value: 38.3
- type: accuracy
name: Livvi Test accuracy
value: 65.4
- type: accuracy
name: Arabic Test accuracy
value: 64.4
- type: accuracy
name: Wolof Test accuracy
value: 38.9
- type: accuracy
name: Bulgarian Test accuracy
value: 72.4
- type: accuracy
name: Akuntsu Test accuracy
value: 49.1
- type: accuracy
name: Makurap Test accuracy
value: 23.3
- type: accuracy
name: Kangri Test accuracy
value: 46.5
- type: accuracy
name: Breton Test accuracy
value: 55.4
- type: accuracy
name: Telugu Test accuracy
value: 80.7
- type: accuracy
name: Cantonese Test accuracy
value: 54.3
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 42.9
- type: accuracy
name: Karelian Test accuracy
value: 70.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 67.1
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 58.3
- type: accuracy
name: Komi Zyrian Test accuracy
value: 47.6
- type: accuracy
name: Irish Test accuracy
value: 60.3
- type: accuracy
name: Nayini Test accuracy
value: 50.0
- type: accuracy
name: Munduruku Test accuracy
value: 41.9
- type: accuracy
name: Manx Test accuracy
value: 37.5
- type: accuracy
name: Skolt Sami Test accuracy
value: 47.4
- type: accuracy
name: Afrikaans Test accuracy
value: 71.3
- type: accuracy
name: Old Turkish Test accuracy
value: 53.4
- type: accuracy
name: Tupinamba Test accuracy
value: 53.6
- type: accuracy
name: Belarusian Test accuracy
value: 76.9
- type: accuracy
name: Serbian Test accuracy
value: 72.2
- type: accuracy
name: Moksha Test accuracy
value: 50.0
- type: accuracy
name: Western Armenian Test accuracy
value: 70.5
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 54.1
- type: accuracy
name: Khunsari Test accuracy
value: 50.0
- type: accuracy
name: Hebrew Test accuracy
value: 79.2
- type: accuracy
name: Uyghur Test accuracy
value: 70.8
- type: accuracy
name: Chukchi Test accuracy
value: 40.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Turkish
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr")
```
|
GermanT5/t5-efficient-gc4-german-base-nl36 | 756f657882367c9929e220d9bfba87641cf4a641 | 2022-07-22T05:27:06.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"de",
"transformers",
"german",
"deutsch",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | GermanT5 | null | GermanT5/t5-efficient-gc4-german-base-nl36 | 63 | 3 | transformers | 5,581 | ---
language: de
license: mit
tags:
- german
- deutsch
---
# Creators
- [Stefan Schweter](https://github.com/stefan-it) ([Bayerische Staatsbibliothek](https://www.digitale-sammlungen.de/) / [Open Source @ DBMDZ](https://github.com/dbmdz))
- [Philip May](https://may.la) ([T-Systems onsite](https://www.t-systems-onsite.de/))
- [Philipp Schmid ](https://www.philschmid.de/) ([Hugging Face](https://huggingface.co/))
# Evaluation
Evaluation was done on a summarization task with:
- train data: [Swisstext](https://www.swisstext.org/2019/shared-task/german-text-summarization-challenge.html)
- test data: [MLSUM](https://huggingface.co/datasets/mlsum)
- GPUs: 4 (V100)
for details see: <https://github.com/GermanT5/german-t5-eval>
# Tips for training on GPUs
This model is too big to fit on a normal 16GB GPU in FP32 mode.
For various reasons, T5 models cannot be trained in FP16 mode.
However, mixed precision training is not yet supported on many GPUs.
For example, it does not work on V100 GPUs. On A100, however, it does.
That is why we suggest to use [DeepSpeed](https://github.com/microsoft/DeepSpeed) for training.
In particular, we recommend the [ZeRO-3 Example](https://huggingface.co/docs/transformers/main_classes/deepspeed#zero3-example) `auto` configuration.
> ZeRO-Offload pushes the boundary of the maximum model size that can be trained efficiently using minimal GPU resources, by exploiting computational and memory resources on both GPUs and their host CPUs.
see [ZeRO-Offload](https://www.deepspeed.ai/features/#zero-offload)
## License - The MIT License
Copyright 2022 Stefan Schweter<br>
Copyright 2022 Philip May, T-Systems onsite<br>
Copyright 2022 P. S.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
microsoft/tapex-large-finetuned-wikisql | aff5c522ffb429d0ad290e900a1863ce63a8325f | 2022-07-14T10:10:52.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:wikisql",
"arxiv:2107.07653",
"transformers",
"tapex",
"table-question-answering",
"license:mit",
"autotrain_compatible"
] | table-question-answering | false | microsoft | null | microsoft/tapex-large-finetuned-wikisql | 63 | 1 | transformers | 5,582 | ---
language: en
tags:
- tapex
- table-question-answering
datasets:
- wikisql
license: mit
---
# TAPEX (large-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
## Model description
TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.
TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.
This model is the `tapex-base` model fine-tuned on the [WikiSQL](https://huggingface.co/datasets/wikisql) dataset.
## Intended Uses
You can use the model for table question answering on relatively simple questions. Some **solveable** questions are shown below (corresponding tables now shown):
| Question | Answer |
|:---: |:---:|
| tell me what the notes are for south australia | no slogan on current series |
| what position does the player who played for butler cc (ks) play? | guard-forward |
| how many schools did player number 3 play at? | 1.0 |
| how many winning drivers in the kraco twin 125 (r2) race were there? | 1.0 |
| for the episode(s) aired in the u.s. on 4 april 2008, what were the names? | "bust a move" part one, "bust a move" part two |
### How to Use
Here is how to use this model in transformers:
```python
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wikisql")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wikisql")
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
# tapex accepts uncased input since it is pre-trained on the uncased corpus
query = "In which year did beijing host the Olympic Games?"
encoding = tokenizer(table=table, query=query, return_tensors="pt")
outputs = model.generate(**encoding)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# [' 2008.0']
```
### How to Eval
Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
### BibTeX entry and citation info
```bibtex
@inproceedings{
liu2022tapex,
title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=O50443AsCP}
}
``` |
adnankhawaja/RomanUrdu-RoBERTa-AFT | 71f73fefec1f583a7e23874fbdf3a6ea6a6472f8 | 2022-04-07T06:05:31.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | adnankhawaja | null | adnankhawaja/RomanUrdu-RoBERTa-AFT | 63 | null | transformers | 5,583 | Entry not found |
speechbrain/asr-wav2vec2-librispeech | cab1aa4c274467daba8e395b6832f88da8684576 | 2022-06-08T14:40:42.000Z | [
"wav2vec2",
"feature-extraction",
"en",
"dataset:librispeech",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"Transformer",
"pytorch",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | speechbrain | null | speechbrain/asr-wav2vec2-librispeech | 63 | null | speechbrain | 5,584 | ---
language:
- en
thumbnail: null
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- CTC
- Attention
- Transformer
- pytorch
- speechbrain
- hf-asr-leaderboard
license: apache-2.0
datasets:
- librispeech
metrics:
- wer
- cer
model-index:
- name: wav2vec2+CTC by SpeechBrain
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.90
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.96
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# wav2vec 2.0 with CTC trained on LibriSpeech
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on LibriSpeech (English Language) within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
The performance of the model is the following:
| Release | Test clean WER | Test other WER | GPUs |
|:-------------:|:--------------:|:--------------:|:--------:|
| 24-03-22 | 1.90 | 3.96 | 1xA100 40GB |
## Pipeline description
This ASR system is composed of 2 different but linked blocks:
- Tokenizer (unigram) that transforms words into characters and trained with
the train transcriptions (EN).
- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self)) is combined with two DNN layers and finetuned on LibriSpeech.
The obtained final acoustic representation is given to the CTC.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, please install tranformers and SpeechBrain with the following command:
```
pip install speechbrain transformers
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files (in English)
```python
from speechbrain.pretrained import EncoderASR
asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-librispeech", savedir="pretrained_models/asr-wav2vec2-librispeech")
asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-en/example.wav")
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
## Parallel Inference on a Batch
Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
### Training
The model was trained with SpeechBrain.
To train it from scratch follow these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```bash
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```bash
cd recipes/LibriSpeech/ASR/CTC
python train_with_wav2vec.py hparams/train_en_with_wav2vec.yaml --data_folder=your_data_folder
```
You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1pg0QzW-LqAISG8Viw_lUTGjXwOqh7gkl?usp=sharing).
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
|
shengnan/visualize-v0-pre10w-preseed1 | 06b5ab7354aeff0486a62b88161e304f36171582 | 2022-07-18T02:36:21.000Z | [
"pytorch",
"t5",
"transformers"
] | null | false | shengnan | null | shengnan/visualize-v0-pre10w-preseed1 | 63 | null | transformers | 5,585 | Entry not found |
SharpAI/mal_tls-bert-base | dd28f9eb446b6506df6ae5caed873ab5d9534df6 | 2022-07-27T20:51:25.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers",
"generated_from_keras_callback",
"model-index"
] | text-classification | false | SharpAI | null | SharpAI/mal_tls-bert-base | 63 | null | transformers | 5,586 | ---
tags:
- generated_from_keras_callback
model-index:
- name: mal_tls-bert-base
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal_tls-bert-base
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Helsinki-NLP/opus-mt-en-grk | aaa456e34dfc48aca1e9e13a8e4b0ee357464e03 | 2021-01-18T08:08:31.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"el",
"grk",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-grk | 62 | null | transformers | 5,587 | ---
language:
- en
- el
- grk
tags:
- translation
license: apache-2.0
---
### eng-grk
* source group: English
* target group: Greek languages
* OPUS readme: [eng-grk](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-grk/README.md)
* model: transformer
* source language(s): eng
* target language(s): ell grc_Grek
* model: transformer
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-grk/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-grk/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-grk/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng-ell.eng.ell | 53.8 | 0.723 |
| Tatoeba-test.eng-grc.eng.grc | 0.1 | 0.102 |
| Tatoeba-test.eng.multi | 45.6 | 0.677 |
### System Info:
- hf_name: eng-grk
- source_languages: eng
- target_languages: grk
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-grk/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'el', 'grk']
- src_constituents: {'eng'}
- tgt_constituents: {'grc_Grek', 'ell'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-grk/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-grk/opus2m-2020-08-01.test.txt
- src_alpha3: eng
- tgt_alpha3: grk
- short_pair: en-grk
- chrF2_score: 0.677
- bleu: 45.6
- brevity_penalty: 1.0
- ref_len: 59951.0
- src_name: English
- tgt_name: Greek languages
- train_date: 2020-08-01
- src_alpha2: en
- tgt_alpha2: grk
- prefer_old: False
- long_pair: eng-grk
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-ro-fr | a71843f60cb4f44315caf92df79fb079912c4adc | 2021-09-10T14:02:10.000Z | [
"pytorch",
"marian",
"text2text-generation",
"ro",
"fr",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-ro-fr | 62 | null | transformers | 5,588 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ro-fr
* source languages: ro
* target languages: fr
* OPUS readme: [ro-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ro-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ro-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ro.fr | 54.5 | 0.697 |
|
RecordedFuture/Swedish-Sentiment-Fear | 7764733e64267aae40785babd36014a0163b250a | 2021-05-18T22:00:42.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"sv",
"transformers",
"license:mit"
] | text-classification | false | RecordedFuture | null | RecordedFuture/Swedish-Sentiment-Fear | 62 | null | transformers | 5,589 | ---
language: sv
license: mit
---
## Swedish BERT models for sentiment analysis
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) model and has been fine-tuned to solve a multi-label sentiment analysis task.
The models have been fine-tuned for the sentiments fear and violence. The models output three floats corresponding to the labels "Negative", "Weak sentiment", and "Strong Sentiment" at the respective indexes.
The models have been trained on Swedish data with a conversational focus, collected from various internet sources and forums.
The models are only trained on Swedish data and only supports inference of Swedish input texts. The models inference metrics for all non-Swedish inputs are not defined, these inputs are considered as out of domain data.
The current models are supported at Transformers version >= 4.3.3 and Torch version 1.8.0, compatibility with older versions are not verified.
### Swedish-Sentiment-Fear
The model can be imported from the transformers library by running
from transformers import BertForSequenceClassification, BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear")
classifier_fear= BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear")
When the model and tokenizer are initialized the model can be used for inference.
#### Sentiment definitions
#### The strong sentiment includes but are not limited to
Texts that:
- Hold an expressive emphasis on fear and/ or anxiety
#### The weak sentiment includes but are not limited to
Texts that:
- Express fear and/ or anxiety in a neutral way
#### Verification metrics
During training, the model had maximized validation metrics at the following classification breakpoint.
| Classification Breakpoint | F-score | Precision | Recall |
|:-------------------------:|:-------:|:---------:|:------:|
| 0.45 | 0.8754 | 0.8618 | 0.8895 |
#### Swedish-Sentiment-Violence
The model be can imported from the transformers library by running
from transformers import BertForSequenceClassification, BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence")
classifier_violence = BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence")
When the model and tokenizer are initialized the model can be used for inference.
### Sentiment definitions
#### The strong sentiment includes but are not limited to
Texts that:
- Referencing highly violent acts
- Hold an aggressive tone
#### The weak sentiment includes but are not limited to
Texts that:
- Include general violent statements that do not fall under the strong sentiment
#### Verification metrics
During training, the model had maximized validation metrics at the following classification breakpoint.
| Classification Breakpoint | F-score | Precision | Recall |
|:-------------------------:|:-------:|:---------:|:------:|
| 0.35 | 0.7677 | 0.7456 | 0.791 | |
TransQuest/monotransquest-da-any_en | 386039f2027514b199054024102504cdb2a4d794 | 2021-06-03T19:01:07.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"multilingual-en",
"transformers",
"Quality Estimation",
"monotransquest",
"DA",
"license:apache-2.0"
] | text-classification | false | TransQuest | null | TransQuest/monotransquest-da-any_en | 62 | null | transformers | 5,590 | ---
language: multilingual-en
tags:
- Quality Estimation
- monotransquest
- DA
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-any_en", num_labels=1, use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TuhinColumbia/russianpoetrymany | 036331d815ebd4bec0c902ef01b0f02c9d15372f | 2021-08-31T15:52:43.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | TuhinColumbia | null | TuhinColumbia/russianpoetrymany | 62 | 1 | transformers | 5,591 | Entry not found |
TurkuNLP/wikibert-base-ja-cased | dcef59fb3e01803fdb4bc28b449f1fb0d5011e0d | 2020-05-24T20:00:52.000Z | [
"pytorch",
"transformers"
] | null | false | TurkuNLP | null | TurkuNLP/wikibert-base-ja-cased | 62 | null | transformers | 5,592 | Entry not found |
VictorSanh/bart-base-finetuned-xsum | f7347d6b497512cb9997f14914ea6b309b8f6b7c | 2020-08-17T15:02:57.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | VictorSanh | null | VictorSanh/bart-base-finetuned-xsum | 62 | null | transformers | 5,593 | Entry not found |
aychang/distilbert-base-cased-trec-coarse | 70316ea77d74b8e1e5c939fbf2499594091cab77 | 2021-01-24T20:14:42.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:trec",
"transformers",
"license:mit"
] | text-classification | false | aychang | null | aychang/distilbert-base-cased-trec-coarse | 62 | null | transformers | 5,594 | ---
language:
- en
thumbnail:
tags:
- text-classification
license: mit
datasets:
- trec
metrics:
---
# TREC 6-class Task: distilbert-base-cased
## Model description
A simple base distilBERT model trained on the "trec" dataset.
## Intended uses & limitations
#### How to use
##### Transformers
```python
# Load model and tokenizer
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Use pipeline
from transformers import pipeline
model_name = "aychang/distilbert-base-cased-trec-coarse"
nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name)
results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"])
```
##### AdaptNLP
```python
from adaptnlp import EasySequenceClassifier
model_name = "aychang/distilbert-base-cased-trec-coarse"
texts = ["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]
classifer = EasySequenceClassifier
results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2)
```
#### Limitations and bias
This is minimal language model trained on a benchmark dataset.
## Training data
TREC https://huggingface.co/datasets/trec
## Training procedure
Preprocessing, hardware used, hyperparameters...
#### Hardware
One V100
#### Hyperparameters and Training Args
```python
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir='./models',
overwrite_output_dir=False,
num_train_epochs=2,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
warmup_steps=500,
weight_decay=0.01,
evaluation_strategy="steps",
logging_dir='./logs',
fp16=False,
eval_steps=500,
save_steps=300000
)
```
## Eval results
```
{'epoch': 2.0,
'eval_accuracy': 0.97,
'eval_f1': array([0.98220641, 0.91620112, 1. , 0.97709924, 0.98678414,
0.97560976]),
'eval_loss': 0.14275787770748138,
'eval_precision': array([0.96503497, 0.96470588, 1. , 0.96969697, 0.98245614,
0.96385542]),
'eval_recall': array([1. , 0.87234043, 1. , 0.98461538, 0.99115044,
0.98765432]),
'eval_runtime': 0.9731,
'eval_samples_per_second': 513.798}
```
|
cardiffnlp/twitter-roberta-base-dec2020 | 54381a7fb92f904744f8417a1904157260f0dafe | 2022-02-09T11:15:03.000Z | [
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.03829",
"transformers",
"autotrain_compatible"
] | fill-mask | false | cardiffnlp | null | cardiffnlp/twitter-roberta-base-dec2020 | 62 | null | transformers | 5,595 | # Twitter December 2020 (RoBERTa-base, 107M)
This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def print_candidates():
for i in range(5):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
print_candidates()
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.42239 not
2) 0.23834 getting
3) 0.10684 fully
4) 0.07550 being
5) 0.02097 already
------------------------------
I keep forgetting to bring a <mask>.
1) 0.08145 mask
2) 0.05051 laptop
3) 0.04620 book
4) 0.03910 bag
5) 0.03824 blanket
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.57602 the
2) 0.25120 The
3) 0.02610 End
4) 0.02324 this
5) 0.00690 This
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text):
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
return features_mean
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99084 The movie was great
2) 0.96618 Just finished reading 'Embeddings in NLP'
3) 0.96127 I just ordered fried chicken 🐣
4) 0.95315 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
``` |
echarlaix/bart-base-cnn-r2-19.4-d35-hybrid | 45d5d5098dcd6a347301af116f393e672f13cc2c | 2021-08-20T09:56:33.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | false | echarlaix | null | echarlaix/bart-base-cnn-r2-19.4-d35-hybrid | 62 | null | transformers | 5,596 | ---
language: en
license: apache-2.0
tags:
- summarization
datasets:
- cnn_dailymail
metrics:
- R1
- R2
- RL
---
## facebook/bart-base model fine-tuned on CNN/DailyMail
This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **35%** of the original weights.
The model contains **53%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
<div class="graph"><script src="/echarlaix/bart-base-cnn-r2-19.4-d35-hybrid/raw/main/model_card/density_info.js" id="c0afb977-b30c-485d-ac75-afc874392380"></script></div>
## Fine-Pruning details
This model was fine-tuned from the HuggingFace [model](https://huggingface.co/facebook/bart-base).
A side-effect of the block pruning is that some of the attention heads are completely removed: 38 heads were removed on a total of 216 (17.6%).
## Details of the CNN/DailyMail dataset
| Dataset | Split | # samples |
| ------------- | ----- | --------- |
| CNN/DailyMail | train | 287K |
| CNN/DailyMail | eval | 13K |
### Results
| Metric | # Value |
| ----------- | --------- |
| **Rouge 1** | **42.18** |
| **Rouge 2** | **19.44** |
| **Rouge L** | **39.17** |
|
huggingartists/oxxxymiron | ff2682609172f5173182077e563b36a0cc057c69 | 2022-07-06T16:17:56.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"dataset:huggingartists/oxxxymiron",
"transformers",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm"
] | text-generation | false | huggingartists | null | huggingartists/oxxxymiron | 62 | null | transformers | 5,597 | ---
language: en
datasets:
- huggingartists/oxxxymiron
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/57ecbbdaf70c671be2d8b7bd39112db0.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Oxxxymiron</div>
<a href="https://genius.com/artists/oxxxymiron">
<div style="text-align: center; font-size: 14px;">@oxxxymiron</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Oxxxymiron.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/oxxxymiron).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/oxxxymiron")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/e254c9iz/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 Oxxxymiron's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1ggk9c4z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1ggk9c4z/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='huggingartists/oxxxymiron')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/oxxxymiron")
model = AutoModelWithLMHead.from_pretrained("huggingartists/oxxxymiron")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
lgris/bp_400h_xlsr2_300M | 0cc8ef01e53c684cf94fdffc4b9933c44b3dd1fa | 2022-04-01T20:32:02.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"transformers",
"mozilla-foundation/common_voice_7_0",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | lgris | null | lgris/bp_400h_xlsr2_300M | 62 | 1 | transformers | 5,598 | ---
language:
- pt
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- pt
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: bp_400h_xlsr2_300M
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 10.83
- name: Test CER
type: cer
value: 3.11
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 22.48
- name: Test CER
type: cer
value: 9.33
---
# bp_400h_xlsr2_300M |
lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli | fdb646ec000c7ec8076df9db55995839aec61b2d | 2021-10-27T07:47:56.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"en",
"dataset:mnli",
"transformers",
"textual-entailment",
"nli",
"license:mit"
] | text-classification | false | lighteternal | null | lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli | 62 | 2 | transformers | 5,599 | ---
language: en
tags:
- textual-entailment
- nli
- pytorch
datasets:
- mnli
license: mit
widget :
- text: "EpCAM is overexpressed in breast cancer. </s></s> EpCAM is downregulated in breast cancer."
---
# BiomedNLP-PubMedBERT finetuned on textual entailment (NLI)
The [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene) finetuned on the MNLI dataset. It should be useful in textual entailment tasks involving biomedical corpora.
## Usage
Given two sentences (a premise and a hypothesis), the model outputs the logits of entailment, neutral or contradiction.
You can test the model using the HuggingFace model widget on the side:
- Input two sentences (premise and hypothesis) one after the other.
- The model returns the probabilities of 3 labels: entailment(LABEL:0), neutral(LABEL:1) and contradiction(LABEL:2) respectively.
To use the model locally on your machine:
```python
# import torch
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli")
model = AutoModelForSequenceClassification.from_pretrained("lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli")
premise = 'EpCAM is overexpressed in breast cancer'
hypothesis = 'EpCAM is downregulated in breast cancer.'
# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
truncation_strategy='only_first')
logits = model(x)[0]
probs = logits.softmax(dim=1)
print('Probabilities for entailment, neutral, contradiction \n', np.around(probs.cpu().
detach().numpy(),3))
# Probabilities for entailment, neutral, contradiction
# 0.001 0.001 0.998
```
## Metrics
Evaluation on classification accuracy (entailment, contradiction, neutral) on MNLI test set:
| Metric | Value |
| --- | --- |
| Accuracy | 0.8338|
See Training Metrics tab for detailed info. |
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