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Helsinki-NLP/opus-mt-kl-en | 1a55c53e0315586661456929a8e102bfcdb90a63 | 2021-09-10T13:53:56.000Z | [
"pytorch",
"marian",
"text2text-generation",
"kl",
"en",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-kl-en | 39 | null | transformers | 6,500 | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-kl-en
* source languages: kl
* target languages: en
* OPUS readme: [kl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kl-en/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/kl-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kl-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kl-en/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.kl.en | 26.4 | 0.432 |
| Tatoeba.kl.en | 35.5 | 0.443 |
|
Mathking/bert-base-german-cased-gnad10 | c3993046d7580335ca1c52b2e543fb449b4be00b | 2021-11-07T09:07:25.000Z | [
"pytorch",
"bert",
"text-classification",
"de",
"dataset:gnad10",
"transformers",
"german-news-classification"
] | text-classification | false | Mathking | null | Mathking/bert-base-german-cased-gnad10 | 39 | null | transformers | 6,501 | ---
language:
- de
datasets:
- gnad10
tags:
- text-classification
- german-news-classification
metrics:
- accuracy
- precision
- recall
- f1
---
# German BERT for News Classification
This a bert-base-german-cased model finetuned for text classification on german news articles
## Training data
Used the training set from the 10KGNAD dataset (gnad10 on HuggingFace Datasets). |
SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune | 26a80447a84ffc28be0bb7d562a2c6911adee9a7 | 2021-06-23T04:03:25.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune | 39 | null | transformers | 6,502 | ---
tags:
- summarization
widget:
- text: "parse the uses licence node of this package , if any , and returns the license definition if theres"
---
# CodeTrans model for api recommendation generation
Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans).
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the api recommendation generation task for the java apis.
## Intended uses & limitations
The model could be used to generate api usage for the java programming tasks.
### How to use
Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune", skip_special_tokens=True),
device=0
)
tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/transfer%20learning%20fine-tuning/api%20generation/base_model.ipynb).
## Training data
The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
## Training procedure
### Transfer-learning Pretraining
The model was trained on a single TPU Pod V3-8 for 240,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
### Fine-tuning
This model was then fine-tuned on a single TPU Pod V3-8 for 1,400,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data.
## Evaluation results
For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | Java |
| -------------------- | :------------: |
| CodeTrans-ST-Small | 68.71 |
| CodeTrans-ST-Base | 70.45 |
| CodeTrans-TF-Small | 68.90 |
| CodeTrans-TF-Base | 72.11 |
| CodeTrans-TF-Large | 73.26 |
| CodeTrans-MT-Small | 58.43 |
| CodeTrans-MT-Base | 67.97 |
| CodeTrans-MT-Large | 72.29 |
| CodeTrans-MT-TF-Small | 69.29 |
| CodeTrans-MT-TF-Base | 72.89 |
| CodeTrans-MT-TF-Large | **73.39** |
| State of the art | 54.42 |
> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
|
WangZeJun/roformer-sim-base-chinese | 5da827822935d53285c1103f7e72cef2dae84749 | 2022-06-14T09:17:25.000Z | [
"pytorch",
"transformers"
] | null | false | WangZeJun | null | WangZeJun/roformer-sim-base-chinese | 39 | 1 | transformers | 6,503 | https://github.com/zejunwang1/bert4vec |
addy88/gptj8 | f07b3aaf67ce4053852bf45a307bd58dc2f4b39f | 2022-01-02T06:33:57.000Z | [
"pytorch",
"gptj",
"text-generation",
"arxiv:2106.09685",
"arxiv:2110.02861",
"transformers"
] | text-generation | false | addy88 | null | addy88/gptj8 | 39 | 1 | transformers | 6,504 | This Model is 8bit Version of EleutherAI/gpt-j-6B. It is converted by Facebook's bitsandbytes library. The original GPT-J takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. So for finetuning on single GPU This model is converted into 8bit.
Here's how to run it: [](https://colab.research.google.com/drive/1KNf5siQdM7ILQM-pHsP6gNVPKl1SJdU1)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://colab.research.google.com/drive/1FxGeYQyE7cx9VNCBC4gUyRVZGORW7c6g) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Can I use this technique with other models?
The model was converted using [this notebook](https://colab.research.google.com/drive/1rwxh0XRdVi8VEbTx97l9xXr4JbRhZaq5#scrollTo=CX3VHn-J1Zer). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
aseifert/distilbert-casing | 1c532d673e156dba74da474add70775e44d7989a | 2020-10-29T09:44:22.000Z | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | aseifert | null | aseifert/distilbert-casing | 39 | null | transformers | 6,505 | Entry not found |
benjaminbeilharz/bart-base-empatheticdialogues | 81a073af4a4c402a5340ff6410fcd445001f8eec | 2022-01-24T11:29:02.000Z | [
"pytorch",
"tensorboard",
"bart",
"text-generation",
"transformers"
] | text-generation | false | benjaminbeilharz | null | benjaminbeilharz/bart-base-empatheticdialogues | 39 | null | transformers | 6,506 | Entry not found |
blizrys/biobert-v1.1-finetuned-pubmedqa | d36bc441473d389521c400a4814722b4a084673b | 2021-09-13T17:56:32.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | blizrys | null | blizrys/biobert-v1.1-finetuned-pubmedqa | 39 | null | transformers | 6,507 | ---
tags:
- generated_from_trainer
datasets:
- null
metrics:
- accuracy
model-index:
- name: biobert-v1.1-finetuned-pubmedqa
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7
---
<!-- 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. -->
# biobert-v1.1-finetuned-pubmedqa
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7737
- Accuracy: 0.7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 57 | 0.8810 | 0.56 |
| No log | 2.0 | 114 | 0.8139 | 0.62 |
| No log | 3.0 | 171 | 0.7963 | 0.68 |
| No log | 4.0 | 228 | 0.7709 | 0.66 |
| No log | 5.0 | 285 | 0.7931 | 0.64 |
| No log | 6.0 | 342 | 0.7420 | 0.7 |
| No log | 7.0 | 399 | 0.7654 | 0.7 |
| No log | 8.0 | 456 | 0.7756 | 0.68 |
| 0.5849 | 9.0 | 513 | 0.7605 | 0.68 |
| 0.5849 | 10.0 | 570 | 0.7737 | 0.7 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
castorini/ance-dpr-context-multi | adb8465629826220f773030de9e06e463e486f1e | 2021-09-22T09:41:18.000Z | [
"pytorch",
"dpr",
"arxiv:2007.00808",
"transformers"
] | null | false | castorini | null | castorini/ance-dpr-context-multi | 39 | null | transformers | 6,508 | This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
|
flax-community/roberta-base-thai | 3d400e88b72d7765267bd63a59eec6f34cca4f13 | 2021-07-17T09:43:54.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/roberta-base-thai | 39 | null | transformers | 6,509 | Entry not found |
flax-sentence-embeddings/all_datasets_v3_distilroberta-base | a02c9dc41679249af6ad7df1228c49026ec490be | 2021-07-23T15:43:19.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"arxiv:2104.08727",
"arxiv:1810.09305",
"arxiv:2102.07033",
"arxiv:1904.06472",
"sentence-transformers",
"feature-extraction",
"sentence-similarity"
] | sentence-similarity | false | flax-sentence-embeddings | null | flax-sentence-embeddings/all_datasets_v3_distilroberta-base | 39 | 2 | sentence-transformers | 6,510 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language: en
---
# Model description
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 [`distilroberta-base`](https://huggingface.co/distilroberta-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 Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures
the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence
similarity tasks.
## 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 = SentenceTransformer('flax-sentence-embeddings/all_datasets_v3_distilroberta-base')
text = "Replace me by any text you'd like."
text_embbedding = model.encode(text)
# array([-0.01559514, 0.04046123, 0.1317083 , 0.00085931, 0.04585106,
# -0.05607086, 0.0138078 , 0.03569756, 0.01420381, 0.04266302 ...],
# dtype=float32)
```
# Training procedure
## Pre-training
We use the pretrained [`distilroberta-base`](https://huggingface.co/distilroberta-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 ou model on a TPU v3-8. We train the model during 540k 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 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 |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [COCO 2020](COCO 2020) | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [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 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| 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 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [SPECTER](https://github.com/allenai/specter) | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [S2ORC](https://github.com/allenai/s2orc) Title/Abstract | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [S2ORC](https://github.com/allenai/s2orc) Citation/Citation | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) Citation/Abstract | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [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 |
| SearchQA | - | 582,261 |
| [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 |
| [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 |
| [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 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [Reddit conversationnal](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| total | | 1,097,953,922 |
|
huggingtweets/kaikothesharko | 76d8c4b143e655009d8bb5925611444fa46d39be | 2021-12-02T04:58:11.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/kaikothesharko | 39 | null | transformers | 6,511 | ---
language: en
thumbnail: http://www.huggingtweets.com/kaikothesharko/1638421086822/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1463379249578987527/OUX9AGXt_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Kaiko TF (RAFFLE IN PINNED)</div>
<div style="text-align: center; font-size: 14px;">@kaikothesharko</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Kaiko TF (RAFFLE IN PINNED).
| Data | Kaiko TF (RAFFLE IN PINNED) |
| --- | --- |
| Tweets downloaded | 2169 |
| Retweets | 259 |
| Short tweets | 529 |
| Tweets kept | 1381 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/18zt3o3w/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 @kaikothesharko's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ajrcjpz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ajrcjpz/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/kaikothesharko')
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)
|
ismaelfaro/gpt2-poems.es | 4817ebe99130415355897998baa848fb1fd2f4ea | 2021-10-12T14:23:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"es",
"transformers",
"GPT",
"license:mit"
] | text-generation | false | ismaelfaro | null | ismaelfaro/gpt2-poems.es | 39 | 1 | transformers | 6,512 | ---
language: es
tags:
- GPT
license: mit
---
# GTP2-Poems Spanish
This model is part of the Poems+AI experiment
more info https://poems-ai.github.io/art/
# Original Dataset
- https://www.kaggle.com/andreamorgar/spanish-poetry-dataset
- Marcos de la Fuente's poems
|
it5/it5-small-wiki-summarization | 470e69837f740891d8f35e12f209acbb0caadbba | 2022-03-09T07:50:42.000Z | [
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"it",
"dataset:wits",
"arxiv:2203.03759",
"transformers",
"italian",
"sequence-to-sequence",
"wikipedia",
"summarization",
"wits",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible"
] | summarization | false | it5 | null | it5/it5-small-wiki-summarization | 39 | null | transformers | 6,513 | ---
language:
- it
license: apache-2.0
datasets:
- wits
tags:
- italian
- sequence-to-sequence
- wikipedia
- summarization
- wits
widget:
- text: "La 5ª Commissione ha competenza per i disegni di legge riguardanti le specifiche materie del bilancio, del personale e dei servizi del Ministero dell'economia, nonché per i disegni di legge riguardanti la materia finanziaria. La Commissione è composta da 26 senatori (di cui 2 segretari, 2 vicepresidenti di cui 1 componente esterno, e un presidente) scelti in modo omogeneo tra i componenti di quel ramo del Parlamento, in modo da rispecchiarne le forze politiche presenti. Essi sono scelti dai gruppi parlamentari (e non dal Presidente, come invece accade per l'organismo della Giunta parlamentare): per la nomina dei membri ciascun Gruppo, entro cinque giorni dalla propria costituzione, procede, dandone comunicazione alla Presidenza del Senato, alla designazione dei propri rappresentanti nelle singole Commissioni permanenti. Ogni senatore chiamato a far parte del governo o eletto presidente della Commissione è, per la durata della carica, sostituito dal suo gruppo nella Commissione con un altro senatore, che continuerà ad appartenere anche alla Commissione di provenienza. Tranne in rari casi nessun Senatore può essere assegnato a più di una Commissione permanente. Le Commissioni permanenti sono rinnovate dopo il primo biennio della legislatura ed i loro componenti possono essere confermati."
- text: "Interni della chiesa Si pensa che già ai tempi di Gediminas vi fosse una piccola chiesa, probabilmente in legno. Nel 1408 circa Vitoldo costruì la chiesa dello Spirito Santo che andò in seguito ampliata. Nel 1501 Alessandro Jagellone lo donò al monastero domenicano, il più antico della Lituania, che nel 1679-88 fu ampliato e ricostruito. Di quel periodo sopravvivono le mura della chiesa, mentre l'arredamento interno fu realizzato nel 1749-1770 e la cupola affrontò dei lavori di restauro nel 1752-1760. Nel 1844 le autorità zariste chiusero il monastero e la chiesa divenne parrocchiale. Oggi serve la comunità polacca di Vilnius. Su via Šv. Ignoto fu fondato un monastero domenicano nel 1501. Come molti altri edifici, questo monastero fu convertito in una prigione dalle autorità zariste nel 1807. Costituì un luogo di prigionia per molti patrioti lituani, nello specifico i Filareti, i quali parteciparono alle rivolte del 1831 e del 1863. Organo La chiesa si trova lateralmente rispetto alla strada e non ha una facciata principale ben disegnata. L'altezza, inclusa la cupola, è di 51 m. La parte inferiore della facciata (con piccole torri gemelle) è ricoperta da edifici conventuali e l'esterno presenta caratteristiche architettoniche tipiche del tardo barocco. Celebre per i fantasiosi ornamenti rococò, l'interno della chiesa è tra i più celebri della Lituania per via dei cartigli con vari stemmi e affreschi lungo la navata: vi sono 16 altari nella chiesa. Gli altari e il pulpito sono assai decorati con sculture e ornamenti rotondi e in rilievo. Tra gli affreschi barocchi, si pensi alla composizione multi-figurale intitolata ''Apoteosi dello Spirito Santo'' (neobarocco, XIX secolo) nella cupola, 45 dipinti nella chiesa (tra cui un'immagine di Santa Barbara con un'ambientazione del XVII o XVIII secolo, una di Santa Caterina da Siena in stile rococò di Szymon Czechowicz, un ritratto di Alessandro Jagellone di un artista sconosciuto della seconda metà del XVIII secolo). Un ingresso sotto l'altare conduce alle grandi volte, labirintiche, con molte stanze e cripte: i sotterranei ospitano i resti di centinaia di residenti di Vilnius, alcuni dei quali mummificatisi naturalmente, e sono circondati da leggende metropolitane. Sebbene l'esistenza dei sotterranei fosse nota, i primi sforzi per esplorare e mappare le cripte furono abbandonate nonostante lo sforzo degli studenti dell'Università di Vilnius negli anni '30. Tuttavia, questi ultimi non avevano osservato le corrette procedure archeologiche e causarono infatti molti danni: il modus operandi prevedeva lo smistamento delle ossa ponendo tutti i teschi sugli scaffali e rimuovendoli le tombe. Da allora, i resti sono stati spostati molte volte lasciandoli in uno stato casuale e disorganizzato. Stando alle leggende che aleggiano sul luogo, i resti sarebbero di soldati francesi recatisi in città nel corso della campagna di Russia del 1812 avviata da Napoleone Bonaparte, di vittime dell'Inquisizione o della peste nera. Più romantiche risultano le affermazioni di chi sostiene che i corridoi sotterranei facevano parte di una rete di passaggi più ampia che consentiva agli amanti leggendari Barbara Radziwiłł e Sigismondo II Augusto di incontrarsi in segreto. Nel 2011, gli antropologi dell'Università di Vilnius, guidati da Rimantas Jankauskas, avviarono uno studio sui corpi mummificati, stimando settimane dopo che le volte conservassero i resti di circa 600 persone, tra cui molte donne e bambini dalla metà del XVIII secolo all'inizio del XIX secolo. Il team ha selezionato i cadaveri meglio conservati e ha eseguito la loro tomografia. I risultati mostrano che molte persone erano in sovrappeso e avevano l'alluce valgo, il che ha portato alla conclusione che si trattava di alti borghesi o comunque di cittadini abbienti. "
- text: "Le dimensioni dell'isola sono di 8 km di lunghezza e di 3,2 km di larghezza. Si trova a 1,6 km a sud-est dell'isola di Renaud, dalla quale è separata dal passaggio Rodman. La sua altezza è di 100 m. Fu scoperta dall'esploratore e baleniere britannico John Biscoe nel 1832 e venne mappata durante una spedizione antartica francese realizzata nel primo decennio del XX secolo. Al comando della spedizione era Jean-Baptiste Charcot e il nome fu scelto per onorare l'esploratore e geografo francese Charles Rabot. === Rivendicazioni territoriali === * Secondo l'Argentina appartiene al dipartimento dell'Antartide Argentina nella provincia della Terra del Fuoco. * Secondo il Cile appartiene al comune antartico della provincia cilena antartica nella regione di Magallanes e dell'Antartico cileno. * Secondo il Regno Unito fa parte del territorio antartico britannico. Per il Trattato Antartico tali rivendicazioni sono sospese. Sull'isola è presente il rifugio Guillochon, sito storico antartico. "
- text: "Vanni ha la sua prima mostra personale nel 1948, alla Galleria Margherita di Roma. Nel 1949 vince una borsa di studio che lo porterà a studiare ad Amsterdam sotto la guida del pittore neoplastico Friedrich Vordemberge-Gildewart. Nel 1952 vince una Fulbright Scholarship che lo porterà a studiare in America, alla Yale University, sotto la guida di Josef Albers. Dal 1953 al 1960 si stabilisce a Parigi, dove illustra alcuni libri per bambini che in seguito vinceranno il premio del Club des Editeurs. Nel 1954 lavora come consulente del colore per il documentario su Picasso di Luciano Emmer, e nel 1955 comincia la sua lunga collaborazione con la Galleria Schneider, affiancando artisti come Corrado Cagli. Dal 1969 al 1974 lavora su dei bassorilievi in vetro resina sui quali vengono proiettati dei film astratti da lui creati, per creare dei quadri che si trasformino continuamente nel tempo. Nel 1979 lascia Roma per stabilirsi a New York, dove alla carriera di pittore affiancherà quella di professore per la prestigiosa Cooper Union School of Art, dove insegnerà ininterrottamente dal 1984 al 2014. L'opera pittorica di Vanni è segnata da una visione estremamente personale, lontana dalle correnti e dai movimenti che hanno caratterizzato la seconda metà del XX secolo. Memore delle lunghe conversazioni avute da Vanni nella sua primissima gioventù, con il filosofo e pittore futurista Alberto Bragaglia, le sue opere sono contrassegnate da un “eclettismo” formale programmatico, alla base del quale resta costante una conoscenza profonda delle molteplici tecniche artistiche utilizzate (tra cui il mosaico, l’affresco e la tempera ad uovo). Pur esprimendosi per lo più in cicli di opere dove l’astrazione formale è la principale componente figurativa, sono da sottolineare alcune opere dove Vanni ha dato prova di una importante padronanza dell’arte figurativa. Importanti e numerose sono le sue realizzazioni anche nel campo dell’illustrazione. Sue sono le illustrazioni per la novella ''Agostino'' di Alberto Moravia, per il libro ''Love'' di Lowell A. Siff e delle ''Contes de Cristal'' di Alice Coléno. Ha tenuto mostre personali in Italia e all’estero ed esposto in mostre collettive di rappresentanza italiana nei musei e nelle gallerie di ogni parte del mondo. "
metrics:
- rouge
- bertscore
model-index:
- name: it5-small-wiki-summarization
results:
- task:
type: wiki-summarization
name: "Wikipedia Summarization"
dataset:
type: wits
name: "WITS"
metrics:
- type: rouge1
value: 0.337
name: "Test Rouge1"
- type: rouge2
value: 0.191
name: "Test Rouge2"
- type: rougeL
value: 0.306
name: "Test RougeL"
- type: bertscore
value: 0.504
name: "Test BERTScore"
args:
- model_type: "dbmdz/bert-base-italian-xxl-uncased"
- lang: "it"
- num_layers: 10
- rescale_with_baseline: True
- baseline_path: "bertscore_baseline_ita.tsv"
co2_eq_emissions:
emissions: "8g"
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# IT5 Small for Wikipedia Summarization ✂️📑 🇮🇹
This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on Wikipedia summarization on the [WITS](https://www.semanticscholar.org/paper/WITS%3A-Wikipedia-for-Italian-Text-Summarization-Casola-Lavelli/ad6c83122e721c7c0db4a40727dac3b4762cd2b1) dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
wikisum = pipeline("summarization", model='it5/it5-small-wiki-summarization')
wikisum("Le dimensioni dell'isola sono di 8 km di lunghezza e di 3,2 km di larghezza. Si trova a 1,6 km a sud-est dell'isola di Renaud, dalla quale è separata dal passaggio Rodman. La sua altezza è di 100 m. Fu scoperta dall'esploratore e baleniere britannico John Biscoe nel 1832 e venne mappata durante una spedizione antartica francese realizzata nel primo decennio del XX secolo. Al comando della spedizione era Jean-Baptiste Charcot e il nome fu scelto per onorare l'esploratore e geografo francese Charles Rabot. === Rivendicazioni territoriali === * Secondo l'Argentina appartiene al dipartimento dell'Antartide Argentina nella provincia della Terra del Fuoco. * Secondo il Cile appartiene al comune antartico della provincia cilena antartica nella regione di Magallanes e dell'Antartico cileno. * Secondo il Regno Unito fa parte del territorio antartico britannico. Per il Trattato Antartico tali rivendicazioni sono sospese. Sull'isola è presente il rifugio Guillochon, sito storico antartico. ")
>>> [{"generated_text": "L' '''isola di Rabot''' si trova in prossimità dell'isola di Renaud, a sud dell'Argentina."}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-small-wiki-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-small-wiki-summarization")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
izumi-lab/electra-small-japanese-discriminator | d453e1f3b45100dc246c7645b18803f2e8824126 | 2022-03-19T09:38:49.000Z | [
"pytorch",
"electra",
"pretraining",
"ja",
"dataset:wikipedia",
"arxiv:2003.10555",
"transformers",
"license:cc-by-sa-4.0"
] | null | false | izumi-lab | null | izumi-lab/electra-small-japanese-discriminator | 39 | null | transformers | 6,514 | ---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: 東京大学で[MASK]の研究をしています。
---
# ELECTRA small Japanese discriminator
This is a [ELECTRA](https://github.com/google-research/electra) model pretrained on texts in the Japanese language.
The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0).
## Model architecture
The model architecture is the same as ELECTRA small in the [original ELECTRA implementation](https://github.com/google-research/electra); 12 layers, 256 dimensions of hidden states, and 4 attention heads.
## Training Data
The models are trained on the Japanese version of Wikipedia.
The training corpus is generated from the Japanese version of Wikipedia, using Wikipedia dump file as of June 1, 2021.
The corpus file is 2.9GB, consisting of approximately 20M sentences.
## Tokenization
The texts are first tokenized by MeCab with IPA dictionary and then split into subwords by the WordPiece algorithm.
The vocabulary size is 32768.
## Training
The models are trained with the same configuration as ELECTRA small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555) except size; 128 tokens per instance, 128 instances per batch, and 1M training steps.
The size of the generator is the same of the discriminator.
## Citation
**There will be another paper for this pretrained model. Be sure to check here again when you cite.**
```
@inproceedings{suzuki2021fin-bert-electra,
title={金融文書を用いた事前学習言語モデルの構築と検証},
% title={Construction and Validation of a Pre-Trained Language Model Using Financial Documents},
author={鈴木 雅弘 and 坂地 泰紀 and 平野 正徳 and 和泉 潔},
% author={Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi},
booktitle={人工知能学会第27回金融情報学研究会(SIG-FIN)},
% booktitle={Proceedings of JSAI Special Interest Group on Financial Infomatics (SIG-FIN) 27},
pages={5-10},
year={2021}
}
```
## Licenses
The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
## Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP21K12010.
|
ml6team/distilbart-tos-summarizer-tosdr | 5c4b53b6b876b4a8b861b24901c4ba2793d7b0e7 | 2022-01-20T15:21:41.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:tosdr",
"transformers",
"summarization",
"t&c",
"tos",
"distilbart",
"distilbart-6-6",
"autotrain_compatible"
] | summarization | false | ml6team | null | ml6team/distilbart-tos-summarizer-tosdr | 39 | 12 | transformers | 6,515 | ---
language:
- en
tags:
- summarization
- t&c
- tos
- distilbart
- distilbart-6-6
datasets:
- tosdr
metrics:
- rouge1
- rouge2
- rougel
inference:
parameters:
min_length: 5
max_length: 512
do_sample: False
widget:
- text: "In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions. Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws. Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use. Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities. Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides."
---
# T&C Summarization Model
T&C Summarization Model based on [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6),
This abstractive summarization model is a part of a bigger end-to-end T&C summarizer pipeline
which is preceded by LSA (Latent Semantic Analysis) extractive summarization. The extractive
summarization shortens the T&C to be further summarized by this model.
## Finetuning Corpus
We collaborated with [TOSDR](https://tosdr.org/) to work with their data, and the model is finetuned accordingly. The article and
summarization text is reduced via extractive summarization before it is finetuned to the model.
## Contact Us
https://ml6.eu/ .
This abstractive model finetuning is the continuation of the Christmas Project 2021 done in ML6: https://bit.ly/XmasProjects .
## Load Finetuned Model
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
```
## Code Sample
This sample requires [sumy](https://pypi.org/project/sumy/), the LSA Extractive Summarization library, as additional package to
run.
```
import re
import nltk
nltk.download('punkt')
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.nlp.stemmers import Stemmer
from sumy.summarizers.lsa import LsaSummarizer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
LANGUAGE = "english"
EXTRACTED_ARTICLE_SENTENCES_LEN = 12
stemmer = Stemmer(LANGUAGE)
lsa_summarizer = LsaSummarizer(stemmer)
tokenizer = AutoTokenizer.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
model = AutoModelForSeq2SeqLM.from_pretrained("ml6team/distilbart-tos-summarizer-tosdr")
def get_extractive_summary(text, sentences_count):
parser = PlaintextParser.from_string(text, Tokenizer(LANGUAGE))
summarized_info = lsa_summarizer(parser.document, sentences_count)
summarized_info = [element._text for element in summarized_info]
return ' '.join(summarized_info)
def get_summary(dict_summarizer_model, dict_tokenizer, text_content):
text_content = get_extractive_summary(text_content, EXTRACTED_ARTICLE_SENTENCES_LEN)
tokenizer = dict_tokenizer['tokenizer']
model = dict_summarizer_model['model']
inputs = tokenizer(text_content, max_length=dict_tokenizer['max_length'], truncation=True, return_tensors="pt")
outputs = model.generate(
inputs["input_ids"], max_length=dict_summarizer_model['max_length'], min_length=dict_summarizer_model['min_length'],
)
summarized_text = tokenizer.decode(outputs[0])
match = re.search(r"<s>(.*)</s>", summarized_text)
if match is not None: summarized_text = match.group(1)
return summarized_text.replace('<s>', '').replace('</s>', '')
test_tos = """
In addition, certain portions of the Web Site may be subject to additional terms of use that we make available for your review or otherwise link to that portion of the Web Site to which such additional terms apply. By using such portions, or any part thereof, you agree to be bound by the additional terms of use applicable to such portions.
Age Restrictions The Web Site may be accessed and used only by individuals who can form legally binding contracts under applicable laws, who are at least 18 years of age or the age of majority in their state or territory of residence (if higher than 18), and who are not barred from using the Web Site under applicable laws.
Our Technology may not be copied, modified, reproduced, republished, posted, transmitted, sold, offered for sale, or redistributed in any way without our prior written permission and the prior written permission of our applicable licensors. Nothing in these Site Terms of Use grants you any right to receive delivery of a copy of Our Technology or to obtain access to Our Technology except as generally and ordinarily permitted through the Web Site according to these Site Terms of Use.
Furthermore, nothing in these Site Terms of Use will be deemed to grant you, by implication, estoppel or otherwise, a license to Our Technology. Certain of the names, logos, and other materials displayed via the Web site constitute trademarks, tradenames, service marks or logos (“Marks”) of us or other entities. You are not authorized to use any such Marks. Ownership of all such Marks and the goodwill associated therewith remains with us or those other entities.
Any use of third party software provided in connection with the Web Site will be governed by such third parties’ licenses and not by these Site Terms of Use. Information on this Web Site may contain technical inaccuracies or typographical errors. Lenovo provides no assurances that any reported problems may be resolved with the use of any information that Lenovo provides
"""
model_dict = {
'model': model,
'max_length': 512,
'min_length': 4
}
tokenizer_dict = {
'tokenizer': tokenizer,
'max_length': 1024
}
print(get_summary(model_dict, tokenizer_dict, test_tos))
```
|
mrm8488/squeezebert-finetuned-squadv2 | a45563f9d8689d843cf8c45742e58b161e905c4f | 2020-12-11T21:55:26.000Z | [
"pytorch",
"squeezebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2006.11316",
"arxiv:2004.02984",
"transformers",
"autotrain_compatible"
] | question-answering | false | mrm8488 | null | mrm8488/squeezebert-finetuned-squadv2 | 39 | null | transformers | 6,516 | ---
language: en
datasets:
- squad_v2
---
# SqueezeBERT + SQuAD v2
[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v2](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of SqueezeBERT
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/).
The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone.
More about the model [here](https://arxiv.org/abs/2004.02984)
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
**SQuAD2.0** combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python /content/transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path squeezebert/squeezebert-uncased \
--do_train \
--do_eval \
--do_lower_case \
--train_file /content/dataset/train-v2.0.json \
--predict_file /content/dataset/dev-v2.0.json \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 15 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output_dir \
--overwrite_output_dir \
--version_2_with_negative \
--save_steps 2000
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **69.98** |
| **F1** | **74.14** |
Model Size: **195 MB**
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-finetuned-squadv2')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'Who did identified it ?'
})
# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.9768241047859192, 'start': 96}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
nielsr/coref-roberta-base | d193901a739fcc27cfbf1b25fd712d99bb1b69e1 | 2021-01-21T08:18:55.000Z | [
"pytorch",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"transformers",
"exbert",
"license:apache-2.0"
] | null | false | nielsr | null | nielsr/coref-roberta-base | 39 | null | transformers | 6,517 | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- wikipedia
- quoref
- docred
- fever
- gap
- winograd_wsc
- winogender
- glue
---
# CorefRoBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me.
## Model description
CorefRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Mention reference prediction (MRP): this is a novel training task which is proposed to enhance coreferential reasoning ability. MRP utilizes the
mention reference masking strategy to mask one of the repeated mentions and then employs a copybased training objective to predict the masked tokens by copying from other tokens in the sequence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks, especially those that involve coreference resolution. If you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CorefRoBERTa model as inputs.
### BibTeX entry and citation info
```bibtex
@misc{ye2020coreferential,
title={Coreferential Reasoning Learning for Language Representation},
author={Deming Ye and Yankai Lin and Jiaju Du and Zhenghao Liu and Peng Li and Maosong Sun and Zhiyuan Liu},
year={2020},
eprint={2004.06870},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
panggi/t5-base-indonesian-summarization-cased | 0fb07c370837e01d71085ec681fb945ab3fed823 | 2021-06-23T13:18:18.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"id",
"dataset:indosum",
"transformers",
"pipeline:summarization",
"summarization",
"autotrain_compatible"
] | summarization | false | panggi | null | panggi/t5-base-indonesian-summarization-cased | 39 | null | transformers | 6,518 | ---
language: id
tags:
- pipeline:summarization
- summarization
- t5
datasets:
- indosum
---
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [indosum](https://github.com/kata-ai/indosum) dataset.
## Load Finetuned Model
```python
from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
model = T5ForConditionalGeneration.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
```
## Code Sample
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
model = T5ForConditionalGeneration.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
# https://www.sehatq.com/artikel/apa-itu-dispepsia-fungsional-ketahui-gejala-dan-faktor-risikonya
ARTICLE_TO_SUMMARIZE = "Secara umum, dispepsia adalah kumpulan gejala pada saluran pencernaan seperti nyeri, sensasi terbakar, dan rasa tidak nyaman pada perut bagian atas. Pada beberapa kasus, dispepsia yang dialami seseorang tidak dapat diketahui penyebabnya. Jenis dispepsia ini disebut dengan dispepsia fungsional. Apa saja gejala dispepsia fungsional? Apa itu dispepsia fungsional? Dispepsia fungsional adalah kumpulan gejala tanpa sebab pada saluran pencernaan bagian atas. Gejala tersebut dapat berupa rasa sakit, nyeri, dan tak nyaman pada perut bagian atas atau ulu hati. Penderita dispepsia fungsional juga akan merasakan kenyang lebih cepat dan sensasi perut penuh berkepanjangan. Gejala-gejala tersebut bisa berlangsung selama sebulan atau lebih. Dispepsia ini memiliki nama “fungsional” karena kumpulan gejalanya tidak memiliki penyebab yang jelas. Dilihat dari fungsi dan struktur saluran pencernaan, dokter tidak menemukan hal yang salah. Namun, gejalanya bisa sangat mengganggu dan menyiksa. Dispepsia fungsional disebut juga dengan dispepsia nonulkus. Diperkirakan bahwa 20% masyarakat dunia menderita dispepsia fungsional. Kondisi ini berisiko tinggi dialami oleh wanita, perokok, dan orang yang mengonsumsi obat anti-peradangan nonsteroid (NSAID). Dispepsia fungsional bisa bersifat kronis dan mengganggu kehidupan penderitanya. Namun beruntung, ada beberapa strategi yang bisa diterapkan untuk mengendalikan gejala dispepsia ini. Strategi tersebut termasuk perubahan gaya hidup, obat-obatan, dan terapi.Ragam gejala dispepsia fungsional Gejala dispepsia fungsional dapat bervariasi antara satu pasien dengan pasien lain. Beberapa tanda yang bisa dirasakan seseorang, yaitu: Sensasi terbakar atau nyeri di saluran pencernaan bagian atas Perut kembung Cepat merasa kenyang walau baru makan sedikit Mual Muntah Bersendawa Rasa asam di mulut Penurunan berat badan Tekanan psikologis terkait dengan kondisi yang dialami Apa sebenarnya penyebab dispepsia fungsional? Sebagai penyakit fungsional, dokter mengkategorikan dispepsia ini sebagai penyakit yang tidak diketahui penyebabnya. Hanya saja, beberapa faktor bisa meningkatkan risiko seseorang terkena dispepsia fungsional. Faktor risiko tersebut, termasuk: Alergi terhadap zat tertentu Perubahan mikrobioma usus Infeksi, seperti yang dipicu oleh bakteriHelicobacter pylori Sekresi asam lambung yang tidak normal Peradangan pada saluran pencernaan bagian atas Gangguan pada fungsi lambung untuk mencerna makanan Pola makan tertentu Gaya hidup tidak sehat Stres Kecemasan atau depresi Efek samping pemakaian obat seperti obat antiinflamasi nonsteroid Penanganan untuk dispepsia fungsional Ada banyak pilihan pengobatan untuk dispepsia fungsional. Seperti yang disampaikan di atas, tidak ada penyebab tunggal dispepsia ini yang bisa diketahui. Gejala yang dialami antara satu pasien juga mungkin amat berbeda dari orang lain. Dengan demikian, jenis pengobatan dispepsia fungsional juga akan bervariasi. Beberapa pilihan strategi penanganan untuk dispepsia fungsional, meliputi: 1. Obat-obatan Ada beberapa jenis obat yang mungkin akan diberikan dokter, seperti Obat penetral asam lambung yang disebut penghambat reseptor H2 Obat penghambat produksi asam lambung yang disebut proton pump inhibitors Obat untuk mengendalikan gas di perut yang mengandung simetikon Antidepresan seperti amitriptyline Obat penguat kerongkongan yang disebut agen prokinetik Obat untuk pengosongan isi lambung seperti metoclopramide Antibiotik jika dokter mendeteksi adanya infeksi bakteri H. pylori 2. Anjuran terkait perubahan gaya hidup Selain obat-obatan, dokter akan memberikan rekomendasi perubahan gaya hidup yang harus diterapkan pasien. Tips terkait perubahan gaya hidup termasuk: Makan lebih sering namun dengan porsi yang lebih sedikit Menjauhi makanan berlemak karena memperlambat pengosongan makanan di lambung Menjauhi jenis makanan lain yang memicu gejala dispepsia, seperti makanan pedas, makanan tinggi asam, produk susu, dan produk kafein Menjauhi rokok Dokter juga akan meminta pasien untuk mencari cara untuk mengendalikan stres, tidur dengan kepala lebih tinggi, dan menjalankan usaha untuk mengendalikan berat badan. Apakah penyakit dispepsia itu berbahaya? Dispepsia, termasuk dispepsia fungsional, dapat menjadi kronis dengan gejala yang menyiksa. Jika tidak ditangani, dispepsia tentu dapat berbahaya dan mengganggu kehidupan pasien. Segera hubungi dokter apabila Anda merasakan gejala dispepsia, terlebih jika tidak merespons obat-obatan yang dijual bebas. Catatan dari SehatQ Dispepsia fungsional adalah kumpulan gejala pada saluran pencernaan bagian atas yang tidak diketahui penyebabnya. Dispepsia fungsional dapat ditangani dengan kombinasi obat-obatan dan perubahan gaya hidup. Jika masih memiliki pertanyaan terkait dispepsia fungsional, Anda bisa menanyakan ke dokter di aplikasi kesehatan keluarga SehatQ. Aplikasi SehatQ bisa diunduh gratis di Appstore dan Playstore yang berikan informasi penyakit terpercaya."
# generate summary
input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
summary_ids = model.generate(input_ids,
max_length=100,
num_beams=2,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
no_repeat_ngram_size=2,
use_cache=True)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```
Output:
```
'Dispepsia fungsional adalah kumpulan gejala tanpa sebab pada saluran pencernaan bagian atas. Gejala tersebut dapat berupa rasa sakit, nyeri, dan tak nyaman pada perut bagian atas. Penderita dispepsia fungsional juga akan merasakan kenyang lebih cepat dan sensasi perut penuh berkepanjangan. Gejala-gejala tersebut bisa berlangsung selama sebulan atau lebih.
```
## Acknowledgement
Thanks to Immanuel Drexel for his article [Text Summarization, Extractive, T5, Bahasa Indonesia, Huggingface’s Transformers](https://medium.com/analytics-vidhya/text-summarization-t5-bahasa-indonesia-huggingfaces-transformers-ee9bfe368e2f)
|
pere/norwegian-roberta-base | 08afd54fbcfe247ba66bbe5024facfafad5c633e | 2021-11-29T20:32:14.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | pere | null | pere/norwegian-roberta-base | 39 | null | transformers | 6,519 | Entry not found |
vslaykovsky/roberta-news-duplicates | 046b363bc6747ed13a6b176468d2700fd7acdbd0 | 2021-05-20T23:07:11.000Z | [
"pytorch",
"jax",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | vslaykovsky | null | vslaykovsky/roberta-news-duplicates | 39 | null | transformers | 6,520 | Entry not found |
ghadeermobasher/BC5CDR-Disease-Modified_scibert_scivocab_uncased | a86a29368b40742102fa8e5554ed1b664408b4ab | 2022-02-25T18:11:07.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Disease-Modified_scibert_scivocab_uncased | 39 | null | transformers | 6,521 | Entry not found |
dbmdz/bert-base-historic-multilingual-64k-td-cased | cdd9ea90daac4e436a3df382966a5da8ab6f2ff2 | 2022-06-03T09:48:41.000Z | [
"pytorch",
"bert",
"fill-mask",
"multilingual",
"arxiv:2205.15575",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | dbmdz | null | dbmdz/bert-base-historic-multilingual-64k-td-cased | 39 | null | transformers | 6,522 | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "Täkäläinen sanomalehdistö [MASK] erit - täin"
- text: "Det vore [MASK] häller nödvändigt att be"
- text: "Comme, à cette époque [MASK] était celle de la"
- text: "In [MASK] an atmosphärischen Nahrungsmitteln"
---
# hmBERT: Historical Multilingual Language Models for Named Entity Recognition
More information about our hmBERT model can be found in our new paper:
["hmBERT: Historical Multilingual Language Models for Named Entity Recognition"](https://arxiv.org/abs/2205.15575).
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ------------- | ----
| German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtered)
| French | [Europeana](http://www.europeana-newspapers.eu/) | 11-31GB (filtered)
| English | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered)
| Finnish | [Europeana](http://www.europeana-newspapers.eu/) | 1.2GB
| Swedish | [Europeana](http://www.europeana-newspapers.eu/) | 1.1GB
# Corpora Stats
## German Europeana Corpus
We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size
and use less-noisier data:
| OCR confidence | Size
| -------------- | ----
| **0.60** | 28GB
| 0.65 | 18GB
| 0.70 | 13GB
For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:
## French Europeana Corpus
Like German, we use different ocr confidence thresholds:
| OCR confidence | Size
| -------------- | ----
| 0.60 | 31GB
| 0.65 | 27GB
| **0.70** | 27GB
| 0.75 | 23GB
| 0.80 | 11GB
For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:
## British Library Corpus
Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). Stats incl. year filtering:
| Years | Size
| ----------------- | ----
| ALL | 24GB
| >= 1800 && < 1900 | 24GB
We use the year filtered variant. The following plot shows a tokens per year distribution:
## Finnish Europeana Corpus
| OCR confidence | Size
| -------------- | ----
| 0.60 | 1.2GB
The following plot shows a tokens per year distribution:
## Swedish Europeana Corpus
| OCR confidence | Size
| -------------- | ----
| 0.60 | 1.1GB
The following plot shows a tokens per year distribution:
## All Corpora
The following plot shows a tokens per year distribution of the complete training corpus:
# Multilingual Vocab generation
For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB.
The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:
| Language | Size
| -------- | ----
| German | 10GB
| French | 10GB
| English | 10GB
| Finnish | 9.5GB
| Swedish | 9.7GB
We then calculate the subword fertility rate and portion of `[UNK]`s over the following NER corpora:
| Language | NER corpora
| -------- | ------------------
| German | CLEF-HIPE, NewsEye
| French | CLEF-HIPE, NewsEye
| English | CLEF-HIPE
| Finnish | NewsEye
| Swedish | NewsEye
Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:
| Language | Subword fertility | Unknown portion
| -------- | ------------------ | ---------------
| German | 1.43 | 0.0004
| French | 1.25 | 0.0001
| English | 1.25 | 0.0
| Finnish | 1.69 | 0.0007
| Swedish | 1.43 | 0.0
Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:
| Language | Subword fertility | Unknown portion
| -------- | ------------------ | ---------------
| German | 1.31 | 0.0004
| French | 1.16 | 0.0001
| English | 1.17 | 0.0
| Finnish | 1.54 | 0.0007
| Swedish | 1.32 | 0.0
# Final pretraining corpora
We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:
| Language | Size
| -------- | ----
| German | 28GB
| French | 27GB
| English | 24GB
| Finnish | 27GB
| Swedish | 27GB
Total size is 130GB.
# Pretraining
Details about the pretraining are coming soon.
# Acknowledgments
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as
TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️
Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗 |
JoofytheBloofy/T5LargeTest | 11497a056734d3b703739f53ba4cb5084cdca45c | 2022-03-27T16:26:03.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"summarization",
"autotrain_compatible"
] | summarization | false | JoofytheBloofy | null | JoofytheBloofy/T5LargeTest | 39 | null | transformers | 6,523 | ---
tags:
- summarization
--- |
NbAiLab/nb-bert-ncc-male2female | 44016b54dc71ea3b34b8d8e25a392244b9cbb518 | 2022-04-27T18:33:23.000Z | [
"pytorch",
"jax",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | NbAiLab | null | NbAiLab/nb-bert-ncc-male2female | 39 | null | transformers | 6,524 | Entry not found |
UrukHan/t5-russian-summarization | c300ff14fea8ecfbf9a7e73a4d30cceeffa01d07 | 2022-04-04T09:51:50.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | UrukHan | null | UrukHan/t5-russian-summarization | 39 | null | transformers | 6,525 | ---
tags:
- generated_from_trainer
model-index:
- name: t5-russian-summarization
results: []
widget:
- text: "Запад после начала российской специальной операции по демилитаризации Украины ввел несколько раундов новых экономических санкций. В Кремле новые ограничения назвали серьезными, но отметили, что Россия готовилась к ним заранее."
---
<!-- 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-russian-summarization
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио https://huggingface.co/UrukHan/wav2vec2-russian и его результаты можно закидывать в эту модель. тестил на видео случайном с ютюба
<table border="0">
<tr>
<td><b style="font-size:30px">Input</b></td>
<td><b style="font-size:30px">Output</b></td>
</tr>
<tr>
<td>Запад после начала российской специальной операции по демилитаризации Украины ввел несколько раундов новых экономических санкций. В Кремле новые ограничения назвали серьезными, но отметили, что Россия готовилась к ним заранее.</td>
<td>Запад ввел новые санкции против России</td>
</tr>
</table>
#
---
Датасеты для обучения:
UrukHan/t5-russian-summarization : https://huggingface.co/datasets/UrukHan/t5-russian-summarization
---
# Запуск на вывод результатов пример работы с комментариями в колабе https://colab.research.google.com/drive/1ame2va9_NflYqy4RZ07HYmQ0moJYy7w2?usp=sharing :
#
```python
# Установим библиотеку трансформеров
!pip install transformers
# Импортируем библиотеки
from transformers import AutoModelForSeq2SeqLM, T5TokenizerFast
# Зададим название выбронной модели из хаба
MODEL_NAME = 'UrukHan/t5-russian-summarization'
MAX_INPUT = 256
# Загрузка модели и токенизатора
tokenizer = T5TokenizerFast.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
# Входные данные (можно массив фраз или текст)
input_sequences = ['Запад после начала российской специальной операции по демилитаризации Украины ввел несколько раундов новых экономических санкций. В Кремле новые ограничения назвали серьезными, но отметили, что Россия готовилась к ним заранее.'] # или можно использовать одиночные фразы: input_sequences = 'сеглдыя хорош ден'
task_prefix = "Spell correct: " # Токенизирование данных
if type(input_sequences) != list: input_sequences = [input_sequences]
encoded = tokenizer(
[task_prefix + sequence for sequence in input_sequences],
padding="longest",
max_length=MAX_INPUT,
truncation=True,
return_tensors="pt",
)
predicts = model.generate(encoded) # # Прогнозирование
tokenizer.batch_decode(predicts, skip_special_tokens=True) # Декодируем данные
```
#
---
#Настроенный блокнот для запуска обучения и сохранения модели в свой репозиторий на huggingface hub:
#https://colab.research.google.com/drive/1H4IoasDqa2TEjGivVDp-4Pdpm0oxrCWd?usp=sharing
#
```python
# Установка библиотек
!pip install datasets
!apt install git-lfs
!pip install transformers
!pip install sentencepiece
!pip install rouge_score
# Импорт библиотек
import numpy as np
from datasets import Dataset
import tensorflow as
import nltk
from transformers import T5TokenizerFast, Seq2SeqTrainingArguments, Seq2SeqTrainer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq
import torch
from transformers.optimization import Adafactor, AdafactorSchedule
from datasets import load_dataset, load_metric
# загрузка параметров
raw_datasets = load_dataset("xsum")
metric = load_metric("rouge")
nltk.download('punkt')
# Ввести свой ключ huggingface hyb
from huggingface_hub import notebook_login
notebook_login()
# Определение параметров
REPO = "t5-russian-summarization" # Введите наазвание название репозитория
MODEL_NAME = "UrukHan/t5-russian-summarization" # Введите наазвание выбранной модели из хаба
MAX_INPUT = 256 # Введите максимальную длинну входных данных в токенах (длинна входных фраз в словах (можно считать полслова токен))
MAX_OUTPUT = 64 # Введите максимальную длинну прогнозов в токенах (можно уменьшить для задач суммризации или других задач где выход короче)
BATCH_SIZE = 8
DATASET = 'UrukHan/t5-russian-summarization' # Введите наазвание название датасета
# Загрузка датасета использование других типов данных опишу ниже
data = load_dataset(DATASET)
# Загрузка модели и токенизатора
tokenizer = T5TokenizerFast.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
model.config.max_length = MAX_OUTPUT # по умолчанию 20, поэтому во всех моделях прогнозы обрезаются выходные последовательности
# Закоментить после первого соъранения в репозиторий свой необъязательно
tokenizer.push_to_hub(repo_name)
train = data['train']
test = data['test'].train_test_split(0.02)['test'] # Уменьшил так тестовыу. выборку чтоб не ждать долго расчет ошибок между эпохами
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model) #return_tensors="tf"
def compute_metrics(eval_pred):
predictions, labels = eval_pred
decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
# Replace -100 in the labels as we can't decode them.
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Rouge expects a newline after each sentence
decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
# Extract a few results
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
# Add mean generated length
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
result["gen_len"] = np.mean(prediction_lens)
return {k: round(v, 4) for k, v in result.items()}
training_args = Seq2SeqTrainingArguments(
output_dir = REPO,
#overwrite_output_dir=True,
evaluation_strategy='steps',
#learning_rate=2e-5,
eval_steps=5000,
save_steps=5000,
num_train_epochs=1,
predict_with_generate=True,
per_device_train_batch_size=BATCH_SIZE,
per_device_eval_batch_size=BATCH_SIZE,
fp16=True,
save_total_limit=2,
#generation_max_length=256,
#generation_num_beams=4,
weight_decay=0.005,
#logging_dir='logs',
push_to_hub=True,
)
# Выберем вручную оптимизатор. Т5 в оригинальной архитектуре использует Адафактор оптимизатор
optimizer = Adafactor(
model.parameters(),
lr=1e-5,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
relative_step=False,
scale_parameter=False,
warmup_init=False,
)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset = train,
eval_dataset = test,
optimizers = (optimizer, lr_scheduler),
tokenizer = tokenizer,
compute_metrics=compute_metrics
)
trainer.train()
trainer.push_to_hub()
```
#
---
# Пример конвертации массивов для данной сети
#
```python
input_data = ['Запад после начала российской специальной операции по демилитаризации Украины ввел несколько раундов новых экономических санкций. В Кремле новые ограничения назвали серьезными, но отметили, что Россия готовилась к ним заранее.']
output_data = ['Запад ввел новые санкции против России']
# Токенизируем входные данные
task_prefix = "Spell correct: "
input_sequences = input_data
encoding = tokenizer(
[task_prefix + sequence for sequence in input_sequences],
padding="longest",
max_length=MAX_INPUT,
truncation=True,
return_tensors="pt",
)
input_ids, attention_mask = encoding.input_ids, encoding.attention_mask
# Токенизируем выходные данные
target_encoding = tokenizer(output_data, padding="longest", max_length=MAX_OUTPUT, truncation=True)
labels = target_encoding.input_ids
# replace padding token id's of the labels by -100
labels = torch.tensor(labels)
labels[labels == tokenizer.pad_token_id] = -100'''
# Конвертируем наши данные в формат dataset
data = Dataset.from_pandas(pd.DataFrame({'input_ids': list(np.array(input_ids)), 'attention_mask': list(np.array(attention_mask)), 'labels': list(np.array(labels))}))
data = data.train_test_split(0.02)
# и получим на вход сети для нашешго trainer: train_dataset = data['train'], eval_dataset = data['test']
|
yihsuan/albert-base-chinese-0407-ner | 7113e82d9ecbadb3045163efcd72984059602b89 | 2022-04-07T03:20:43.000Z | [
"pytorch",
"tensorboard",
"albert",
"token-classification",
"List of ISO 639-1 code for your language",
"zh",
"transformers",
"autotrain_compatible"
] | token-classification | false | yihsuan | null | yihsuan/albert-base-chinese-0407-ner | 39 | null | transformers | 6,526 | ---
language:
- "List of ISO 639-1 code for your language"
- zh
widget:
- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
example_title: "範例ㄧ"
- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
example_title: "範例二"
- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
example_title: "範例三"
---
---
license: gpl-3.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-chinese-0407-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-chinese-0407-ner
This model is a fine-tuned version of [ckiplab/albert-base-chinese](https://huggingface.co/ckiplab/albert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0948
- Precision: 0.8603
- Recall: 0.8871
- F1: 0.8735
- Accuracy: 0.9704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3484 | 0.05 | 500 | 0.5395 | 0.1841 | 0.1976 | 0.1906 | 0.8465 |
| 0.3948 | 0.09 | 1000 | 0.2910 | 0.6138 | 0.7113 | 0.6590 | 0.9263 |
| 0.2388 | 0.14 | 1500 | 0.2030 | 0.6628 | 0.7797 | 0.7165 | 0.9414 |
| 0.1864 | 0.18 | 2000 | 0.1729 | 0.7490 | 0.7935 | 0.7706 | 0.9498 |
| 0.1754 | 0.23 | 2500 | 0.1641 | 0.7415 | 0.7869 | 0.7635 | 0.9505 |
| 0.1558 | 0.28 | 3000 | 0.1532 | 0.7680 | 0.8002 | 0.7838 | 0.9530 |
| 0.1497 | 0.32 | 3500 | 0.1424 | 0.7865 | 0.8282 | 0.8068 | 0.9555 |
| 0.1488 | 0.37 | 4000 | 0.1373 | 0.7887 | 0.8111 | 0.7997 | 0.9553 |
| 0.1361 | 0.42 | 4500 | 0.1311 | 0.7942 | 0.8382 | 0.8156 | 0.9590 |
| 0.1335 | 0.46 | 5000 | 0.1264 | 0.7948 | 0.8423 | 0.8179 | 0.9596 |
| 0.1296 | 0.51 | 5500 | 0.1242 | 0.8129 | 0.8416 | 0.8270 | 0.9603 |
| 0.1338 | 0.55 | 6000 | 0.1315 | 0.7910 | 0.8588 | 0.8235 | 0.9586 |
| 0.1267 | 0.6 | 6500 | 0.1193 | 0.8092 | 0.8399 | 0.8243 | 0.9609 |
| 0.1207 | 0.65 | 7000 | 0.1205 | 0.8021 | 0.8469 | 0.8239 | 0.9601 |
| 0.1214 | 0.69 | 7500 | 0.1201 | 0.7969 | 0.8489 | 0.8220 | 0.9605 |
| 0.1168 | 0.74 | 8000 | 0.1134 | 0.8087 | 0.8607 | 0.8339 | 0.9620 |
| 0.1162 | 0.78 | 8500 | 0.1127 | 0.8177 | 0.8492 | 0.8331 | 0.9625 |
| 0.1202 | 0.83 | 9000 | 0.1283 | 0.7986 | 0.8550 | 0.8259 | 0.9580 |
| 0.1135 | 0.88 | 9500 | 0.1101 | 0.8213 | 0.8572 | 0.8389 | 0.9638 |
| 0.1121 | 0.92 | 10000 | 0.1097 | 0.8190 | 0.8588 | 0.8384 | 0.9635 |
| 0.1091 | 0.97 | 10500 | 0.1088 | 0.8180 | 0.8521 | 0.8347 | 0.9632 |
| 0.1058 | 1.02 | 11000 | 0.1085 | 0.8136 | 0.8716 | 0.8416 | 0.9630 |
| 0.0919 | 1.06 | 11500 | 0.1079 | 0.8309 | 0.8566 | 0.8436 | 0.9646 |
| 0.0914 | 1.11 | 12000 | 0.1079 | 0.8423 | 0.8542 | 0.8482 | 0.9656 |
| 0.0921 | 1.15 | 12500 | 0.1109 | 0.8312 | 0.8647 | 0.8476 | 0.9646 |
| 0.0926 | 1.2 | 13000 | 0.1240 | 0.8413 | 0.8488 | 0.8451 | 0.9637 |
| 0.0914 | 1.25 | 13500 | 0.1040 | 0.8336 | 0.8666 | 0.8498 | 0.9652 |
| 0.0917 | 1.29 | 14000 | 0.1032 | 0.8352 | 0.8707 | 0.8526 | 0.9662 |
| 0.0928 | 1.34 | 14500 | 0.1052 | 0.8347 | 0.8656 | 0.8498 | 0.9651 |
| 0.0906 | 1.38 | 15000 | 0.1032 | 0.8399 | 0.8619 | 0.8507 | 0.9662 |
| 0.0903 | 1.43 | 15500 | 0.1074 | 0.8180 | 0.8708 | 0.8436 | 0.9651 |
| 0.0889 | 1.48 | 16000 | 0.0990 | 0.8367 | 0.8713 | 0.8537 | 0.9670 |
| 0.0914 | 1.52 | 16500 | 0.1055 | 0.8508 | 0.8506 | 0.8507 | 0.9661 |
| 0.0934 | 1.57 | 17000 | 0.0979 | 0.8326 | 0.8740 | 0.8528 | 0.9669 |
| 0.0898 | 1.62 | 17500 | 0.1022 | 0.8393 | 0.8615 | 0.8502 | 0.9668 |
| 0.0869 | 1.66 | 18000 | 0.0962 | 0.8484 | 0.8762 | 0.8621 | 0.9682 |
| 0.089 | 1.71 | 18500 | 0.1008 | 0.8447 | 0.8714 | 0.8579 | 0.9674 |
| 0.0927 | 1.75 | 19000 | 0.0986 | 0.8379 | 0.8749 | 0.8560 | 0.9673 |
| 0.0883 | 1.8 | 19500 | 0.0965 | 0.8518 | 0.8749 | 0.8632 | 0.9688 |
| 0.0965 | 1.85 | 20000 | 0.0937 | 0.8412 | 0.8766 | 0.8585 | 0.9682 |
| 0.0834 | 1.89 | 20500 | 0.0920 | 0.8451 | 0.8862 | 0.8652 | 0.9687 |
| 0.0817 | 1.94 | 21000 | 0.0943 | 0.8439 | 0.8800 | 0.8616 | 0.9686 |
| 0.088 | 1.99 | 21500 | 0.0927 | 0.8483 | 0.8762 | 0.8620 | 0.9683 |
| 0.0705 | 2.03 | 22000 | 0.0993 | 0.8525 | 0.8783 | 0.8652 | 0.9690 |
| 0.0709 | 2.08 | 22500 | 0.0976 | 0.8610 | 0.8697 | 0.8653 | 0.9689 |
| 0.0655 | 2.12 | 23000 | 0.0997 | 0.8585 | 0.8665 | 0.8625 | 0.9683 |
| 0.0656 | 2.17 | 23500 | 0.0966 | 0.8569 | 0.8822 | 0.8694 | 0.9695 |
| 0.0698 | 2.22 | 24000 | 0.0955 | 0.8604 | 0.8775 | 0.8689 | 0.9696 |
| 0.065 | 2.26 | 24500 | 0.0971 | 0.8614 | 0.8780 | 0.8696 | 0.9697 |
| 0.0653 | 2.31 | 25000 | 0.0959 | 0.8600 | 0.8787 | 0.8692 | 0.9698 |
| 0.0685 | 2.35 | 25500 | 0.1001 | 0.8610 | 0.8710 | 0.8659 | 0.9690 |
| 0.0684 | 2.4 | 26000 | 0.0969 | 0.8490 | 0.8877 | 0.8679 | 0.9690 |
| 0.0657 | 2.45 | 26500 | 0.0954 | 0.8532 | 0.8832 | 0.8680 | 0.9696 |
| 0.0668 | 2.49 | 27000 | 0.0947 | 0.8604 | 0.8793 | 0.8698 | 0.9695 |
| 0.0644 | 2.54 | 27500 | 0.0989 | 0.8527 | 0.8790 | 0.8656 | 0.9696 |
| 0.0685 | 2.59 | 28000 | 0.0955 | 0.8596 | 0.8772 | 0.8683 | 0.9700 |
| 0.0702 | 2.63 | 28500 | 0.0937 | 0.8585 | 0.8837 | 0.8709 | 0.9700 |
| 0.0644 | 2.68 | 29000 | 0.0946 | 0.8605 | 0.8830 | 0.8716 | 0.9702 |
| 0.065 | 2.72 | 29500 | 0.0953 | 0.8617 | 0.8822 | 0.8719 | 0.9701 |
| 0.063 | 2.77 | 30000 | 0.0943 | 0.8597 | 0.8848 | 0.8721 | 0.9701 |
| 0.0638 | 2.82 | 30500 | 0.0941 | 0.8619 | 0.8846 | 0.8731 | 0.9702 |
| 0.066 | 2.86 | 31000 | 0.0942 | 0.8608 | 0.8847 | 0.8726 | 0.9701 |
| 0.0589 | 2.91 | 31500 | 0.0952 | 0.8632 | 0.8836 | 0.8733 | 0.9704 |
| 0.0568 | 2.95 | 32000 | 0.0948 | 0.8603 | 0.8871 | 0.8735 | 0.9704 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
GPL/quora-tsdae-msmarco-distilbert-margin-mse | 873c111d4d11af7983370f15675e57d5f2f9043a | 2022-04-19T16:45:50.000Z | [
"pytorch",
"distilbert",
"feature-extraction",
"transformers"
] | feature-extraction | false | GPL | null | GPL/quora-tsdae-msmarco-distilbert-margin-mse | 39 | null | transformers | 6,527 | Entry not found |
xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text | 0268512aaefa5d26d4c4eb8dd873b3f0aca51e0a | 2022-04-26T05:57:49.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"arxiv:2203.07836",
"transformers",
"AMRBART",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | xfbai | null | xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text | 39 | null | transformers | 6,528 | ---
language: en
tags:
- AMRBART
license: mit
---
## AMRBART-large-finetuned-AMR2.0-AMR2Text
This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR2.0 dataset. It achieves a sacre-bleu score of 45.7 on the evaluation set: More details are introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022.
## Model description
Same with AMRBART.
## Training data
The model is finetuned on [AMR2.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 36,521
training instances, 1,368 validation instances, and 1,371 test instances.
## Intended uses & limitations
You can use the model for AMR-to-text generation, but it's mostly intended to be used in the domain of News.
## How to use
Here is how to initialize this model in PyTorch:
```python
from transformers import BartForConditionalGeneration
model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR2.0-AMR2Text")
```
Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing.
## BibTeX entry and citation info
Please cite this paper if you find this model helpful
```bibtex
@inproceedings{bai-etal-2022-graph,
title = "Graph Pre-training for {AMR} Parsing and Generation",
author = "Bai, Xuefeng and
Chen, Yulong and
Zhang, Yue",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "todo",
doi = "todo",
pages = "todo"
}
``` |
hustvl/yolos-small-300 | 98945dea89fc0e6396dfc3fc10d6c4de169c773d | 2022-06-27T08:38:34.000Z | [
"pytorch",
"yolos",
"object-detection",
"dataset:coco",
"arxiv:2106.00666",
"transformers",
"vision",
"license:apache-2.0"
] | object-detection | false | hustvl | null | hustvl/yolos-small-300 | 39 | 1 | transformers | 6,529 | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
example_title: Airport
---
# YOLOS (small-sized) model (300 pre-train epochs)
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS).
Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models.
### How to use
Here is how to use this model:
```python
from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small-300')
model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small-300')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
### Training
The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO.
## Evaluation results
This model achieves an AP (average precision) of **36.1** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-00666,
author = {Yuxin Fang and
Bencheng Liao and
Xinggang Wang and
Jiemin Fang and
Jiyang Qi and
Rui Wu and
Jianwei Niu and
Wenyu Liu},
title = {You Only Look at One Sequence: Rethinking Transformer in Vision through
Object Detection},
journal = {CoRR},
volume = {abs/2106.00666},
year = {2021},
url = {https://arxiv.org/abs/2106.00666},
eprinttype = {arXiv},
eprint = {2106.00666},
timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
BlackSamorez/ebanko-large | f6b76dd3570ef2dcc2b1a18d3fe3aef7589c5ce0 | 2022-04-28T19:19:55.000Z | [
"pytorch",
"t5",
"text2text-generation",
"ru",
"transformers",
"PyTorch",
"Transformers",
"autotrain_compatible"
] | text2text-generation | false | BlackSamorez | null | BlackSamorez/ebanko-large | 39 | null | transformers | 6,530 | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ebanko-base
Model was finetuned by [black_samorez](https://github.com/BlackSamorez).
Based off [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base).
Finetuned on [Russian Language Toxic Comments](https://www.kaggle.com/datasets/blackmoon/russian-language-toxic-comments) and [
russe_detox_2022](https://github.com/skoltech-nlp/russe_detox_2022) train to toxify text.
* Task: `text2text generation`
* Type: `encoder-decoder`
* Tokenizer: `bpe`
* Dict size: `32 101 `
* Num Parameters: `737 M`
---
license: apache-2.0
---
|
HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg | edad04dfeba14cf700bc442318bd951f16256fc8 | 2022-05-08T23:10:17.000Z | [
"pytorch",
"roberta",
"text-classification",
"dataset:snli",
"dataset:anli",
"dataset:multi_nli",
"dataset:multi_nli_mismatch",
"dataset:fever",
"arxiv:2104.14690",
"arxiv:2203.13602",
"transformers",
"zero-shot-classification"
] | zero-shot-classification | false | HiTZ | null | HiTZ/A2T_RoBERTa_SMFA_WikiEvents-arg_ACE-arg | 39 | null | transformers | 6,531 | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers).
Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero-shot text classification problems [(Yin et al., 2019](https://aclanthology.org/D19-1404/); [Wang et al., 2021](https://arxiv.org/abs/2104.14690); [Sainz and Rigau, 2021)](https://aclanthology.org/2021.gwc-1.6/). Recent research addressed Information Extraction problems with the same idea [(Lyu et al., 2021](https://aclanthology.org/2021.acl-short.42/); [Sainz et al., 2021](https://aclanthology.org/2021.emnlp-main.92/); [Sainz et al., 2022a](), [Sainz et al., 2022b)](https://arxiv.org/abs/2203.13602). The A2T entailment models are first trained with NLI datasets such as MNLI [(Williams et al., 2018)](), SNLI [(Bowman et al., 2015)]() or/and ANLI [(Nie et al., 2020)]() and then fine-tuned to specific tasks that were previously converted to textual entailment format.
For more information please, take a look to the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library or the following published papers:
- [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/)
- [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]()
## About the model
The model name describes the configuration used for training as follows:
<!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ -->
<h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3>
- `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>.
- `NLI_datasets`: The NLI datasets used for pivot training.
- `S`: Standford Natural Language Inference (SNLI) dataset.
- `M`: Multi Natural Language Inference (MNLI) dataset.
- `F`: Fever-nli dataset.
- `A`: Adversarial Natural Language Inference (ANLI) dataset.
- `finetune_datasets`: The datasets used for fine tuning the entailment model. Note that for more than 1 dataset the training was performed sequentially. For example: ACE-arg.
Some models like `HiTZ/A2T_RoBERTa_SMFA_ACE-arg` have been trained marking some information between square brackets (`'[['` and `']]'`) like the event trigger span. Make sure you follow the same preprocessing in order to obtain the best results.
## Cite
If you use this model, consider citing the following publications:
```bibtex
@inproceedings{sainz-etal-2021-label,
title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction",
author = "Sainz, Oscar and
Lopez de Lacalle, Oier and
Labaka, Gorka and
Barrena, Ander and
Agirre, Eneko",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.92",
doi = "10.18653/v1/2021.emnlp-main.92",
pages = "1199--1212",
}
``` |
kornosk/polibertweet-political-twitter-roberta-mlm | 7ebea6e1dab8f54f95aec14e4d3e0ffa5a407da2 | 2022-06-17T23:45:14.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"transformers",
"twitter",
"masked-token-prediction",
"bertweet",
"election2020",
"politics",
"license:gpl-3.0",
"autotrain_compatible"
] | fill-mask | false | kornosk | null | kornosk/polibertweet-political-twitter-roberta-mlm | 39 | null | transformers | 6,532 | ---
language: "en"
tags:
- twitter
- masked-token-prediction
- bertweet
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Political Election 2020
Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022.
Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail.
We use the initialized weights from [BERTweet](https://huggingface.co/vinai/bertweet-base) or `vinai/bertweet-base`.
# Training Data
This model is pre-trained on over 83 million English tweets about the 2020 US Presidential Election.
# Training Objective
This model is initialized with BERTweet and trained with an MLM objective.
# Usage
This pre-trained language model **can be fine-tunned to any downstream task (e.g. classification)**.
```python
from transformers import AutoModel, AutoTokenizer, pipeline
import torch
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/polibertweet-mlm"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModel.from_pretrained(pretrained_LM_path)
# fill mask
example = "Trump is the <mask> of USA"
fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer)
outputs = fill_mask(example)
print(outputs)
# see embeddings
inputs = tokenizer(example, return_tensors="pt")
outputs = model(**inputs)
print(outputs)
# OR you can use this model to train on your downstream task!
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter](XXX), LREC 2022.
# Citation
```bibtex
@inproceedings{kawintiranon2022polibertweet,
title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter},
author = {Kawintiranon, Kornraphop and Singh, Lisa},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
year = {2022},
publisher = {European Language Resources Association}
}
``` |
okho0653/Bio_ClinicalBERT-zero-shot-sentiment-model | fee1ce229a671ff13fd2f111ba21c4529ab0909a | 2022-05-06T05:57:30.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
] | text-classification | false | okho0653 | null | okho0653/Bio_ClinicalBERT-zero-shot-sentiment-model | 39 | null | transformers | 6,533 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot-sentiment-model
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. -->
# Bio_ClinicalBERT-zero-shot-sentiment-model
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Jatin-WIAI/doctor_patient_clf_en | 78115f1d9f4649339685a365649554ef018c9cb8 | 2022-05-19T09:53:05.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | Jatin-WIAI | null | Jatin-WIAI/doctor_patient_clf_en | 39 | null | transformers | 6,534 | Entry not found |
connectivity/feather_berts_28 | 9beae1bc72b121e5703987cc2a003eb7d1bbb3a8 | 2022-05-21T14:28:23.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | connectivity | null | connectivity/feather_berts_28 | 39 | null | transformers | 6,535 | Entry not found |
thunninoi/wav2vec2-japanese-vtuber | 1d9c01d13d10df74385de5b468bb692ac6ac377b | 2022-07-08T17:33:48.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"model-index"
] | automatic-speech-recognition | false | thunninoi | null | thunninoi/wav2vec2-japanese-vtuber | 39 | null | transformers | 6,536 | ---
tags:
- generated_from_trainer
model-index:
- name: checkpoints2
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. -->
# checkpoints2
This model is a fine-tuned version of [ttop324/wav2vec2-live-japanese](https://huggingface.co/ttop324/wav2vec2-live-japanese) on the extracted and cleaned transcript of [Holo No Graffiti](https://youtube.com/playlist?list=PLS51cvjOMUKwKtxe_IxhbBBvQ9XpiL1W_)
dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Evaluated from [common_voice | train | ja ](https://huggingface.co/datasets/common_voice/viewer/ja/test):
- WER: 32.940524
- CER: 15.251746
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 3
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
aomar85/fine-tuned-arabert-random-negative | 44c6420e66595a90e1837aa5da74b456f99775e5 | 2022-05-29T22:24:58.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | aomar85 | null | aomar85/fine-tuned-arabert-random-negative | 39 | null | transformers | 6,537 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine-tuned-arabert-random-negative
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. -->
# fine-tuned-arabert-random-negative
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0080
- Accuracy: 0.9989
- Precision: 0.9990
- Recall: 0.9988
- F1: 0.9989
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0105 | 1.0 | 62920 | 0.0061 | 0.9986 | 0.9993 | 0.9979 | 0.9986 |
| 0.0069 | 2.0 | 125840 | 0.0096 | 0.9986 | 0.9993 | 0.9979 | 0.9986 |
| 0.0058 | 3.0 | 188760 | 0.0084 | 0.9988 | 0.9988 | 0.9988 | 0.9988 |
| 0.0047 | 4.0 | 251680 | 0.0080 | 0.9989 | 0.9990 | 0.9988 | 0.9989 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
juancavallotti/t5-base-gec | 25b3102f637927f3555e3a098577cc2b64d517d6 | 2022-06-08T15:26:04.000Z | [
"pytorch",
"tensorboard",
"onnx",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | juancavallotti | null | juancavallotti/t5-base-gec | 39 | 1 | transformers | 6,538 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-gec
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-gec
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
niclas/ATC_1 | 6369daa401b2761f6a1f404745a39a51fad4caec | 2022-06-09T08:52:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | niclas | null | niclas/ATC_1 | 39 | null | transformers | 6,539 | Entry not found |
wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics | 5889d9869dc3f3840b2aa3496b0122550233a58c | 2022-06-17T11:20:35.000Z | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | wvangils | null | wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics | 39 | null | transformers | 6,540 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
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. -->
# GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the [Cmotions - Beatles lyrics](https://huggingface.co/datasets/cmotions/Beatles_lyrics) dataset. It will complete an input prompt with Beatles-like text.
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4438 | 1.0 | 18 | 1.8004 |
| 2.1981 | 2.0 | 36 | 1.6985 |
| 1.9766 | 3.0 | 54 | 1.6487 |
| 1.8233 | 4.0 | 72 | 1.6384 |
| 1.6137 | 5.0 | 90 | 1.6574 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
GonzoJurezz/gpt2-horo | e26def2b2971f8c22e813de26efeb43e3d15e987 | 2022-06-18T21:30:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | GonzoJurezz | null | GonzoJurezz/gpt2-horo | 39 | null | transformers | 6,541 | Entry not found |
ryo0634/bert-base-zip-dependency-0 | 5436dcfe6d178aef3142a48730f41e919d8a79b5 | 2022-06-13T03:38:10.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | ryo0634 | null | ryo0634/bert-base-zip-dependency-0 | 39 | null | transformers | 6,542 | Entry not found |
Intel/distilbert-base-cased-distilled-squad-int8-static | 1a083d94199ed7c70554880354283ba9c23a4a47 | 2022-07-25T02:50:45.000Z | [
"pytorch",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | Intel | null | Intel/distilbert-base-cased-distilled-squad-int8-static | 39 | null | transformers | 6,543 | ---
license: apache-2.0
tags:
- int8
- Intel® Neural Compressor
- PostTrainingStatic
datasets:
- squad
metrics:
- f1
---
# INT8 DistilBERT base cased finetuned on Squad
### Post-training static quantization
This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad).
The calibration dataloader is the train dataloader. The default calibration sampling size 300 isn't divisible exactly by batch size 8, so the real sampling size is 304.
The linear module **distilbert.transformer.layer.1.ffn.lin2** falls back to fp32 to meet the 1% relative accuracy loss.
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |86.0005|86.8373|
| **Model size (MB)** |71.2|249|
### Load with Intel® Neural Compressor:
```python
from neural_compressor.utils.load_huggingface import OptimizedModel
int8_model = OptimizedModel.from_pretrained(
'Intel/distilbert-base-cased-distilled-squad-int8-static',
)
```
|
mosesju/distilbert-base-uncased-finetuned-news | e52883c2776fbfc0299d3eb7ef6299970b24e602 | 2022-06-17T12:14:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:ag_news",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | mosesju | null | mosesju/distilbert-base-uncased-finetuned-news | 39 | null | transformers | 6,544 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-news
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9388157894736842
- name: F1
type: f1
value: 0.9388275184627893
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-news
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2117
- Accuracy: 0.9388
- F1: 0.9388
## Model description
This model is intended to categorize news headlines into one of four categories; World, Sports, Science & Technology, or Business
## Intended uses & limitations
The model is limited by the training data it used. If you use the model with a news story that falls outside of the four intended categories, it produces quite confused results.
## 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: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2949 | 1.0 | 3750 | 0.2501 | 0.9262 | 0.9261 |
| 0.1569 | 2.0 | 7500 | 0.2117 | 0.9388 | 0.9388 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
emilys/BERTweet-WNUT17 | 1a748f44c714525686d30b777c36b4b5f8f40334 | 2022-06-15T22:31:22.000Z | [
"pytorch",
"roberta",
"token-classification",
"en",
"dataset:wnut_17",
"transformers",
"NER",
"autotrain_compatible"
] | token-classification | false | emilys | null | emilys/BERTweet-WNUT17 | 39 | null | transformers | 6,545 | ---
language:
- en
tags:
- NER
datasets:
- wnut_17
---
bertweet-base (https://huggingface.co/vinai/bertweet-base) finetuned on WNUT (2017), following https://github.com/huggingface/transformers/tree/main/examples/legacy/token-classification |
DingosGotMyBaby/uhn-twitch-chat | 8ac35a645e15581fdc54d1df4cca84d0a9e9daeb | 2022-06-24T05:08:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:mit"
] | text-generation | false | DingosGotMyBaby | null | DingosGotMyBaby/uhn-twitch-chat | 39 | null | transformers | 6,546 | ---
license: mit
---
# A model based on UberHaxorNova's Twitch chat
Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset.
## Dataset
The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into a simple line-by-line text file.
## Training
This was trained using [aitextgen](https://github.com/minimaxir/aitextgen), created by [Max Woolf](https://github.com/minimaxir), using the example notebook found [here](https://colab.research.google.com/drive/15qBZx5y9rdaQSyWpsreMDnTiZ5IlN0zD?usp=sharing). Using GPT-2's 124M model as the base, it was trained for 3000 steps and produces an output scuffed enough to look like a real Twitch chat user.
## Use
This was created as a fun little project for the discord server and as such, should only be used for fun and not to harm people. This model must also follow the ethics guide of the tool that created it https://docs.aitextgen.io/ethics/
|
plncmm/mdeberta-wl-base-es | 0682a32b193e16422ff25c16ce48eb417fb737e2 | 2022-06-26T13:49:00.000Z | [
"pytorch",
"deberta-v2",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | fill-mask | false | plncmm | null | plncmm/mdeberta-wl-base-es | 39 | null | transformers | 6,547 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: mdeberta-wl-base-es
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. -->
# mdeberta-wl-base-es
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
hellennamulinda/eng-lug | 2ed04490c7d5bdce995951c5d3642d4e00c7aff6 | 2022-07-11T06:45:00.000Z | [
"pytorch",
"marian",
"text2text-generation",
"unk",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | text2text-generation | false | hellennamulinda | null | hellennamulinda/eng-lug | 39 | null | transformers | 6,548 | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
co2_eq_emissions: 0.04087910671538076
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1026034854
- CO2 Emissions (in grams): 0.04087910671538076
## Validation Metrics
- Loss: 1.0871405601501465
- Rouge1: 55.8225
- Rouge2: 34.1547
- RougeL: 54.4274
- RougeLsum: 54.408
- Gen Len: 23.178
## 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/hellennamulinda/autotrain-eng-lug-1070637495
``` |
chradden/opencampus_age-detection | 7173e20896f511734cdf9aacec4e5dd9bada8d86 | 2022-07-02T12:28:02.000Z | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index"
] | image-classification | false | chradden | null | chradden/opencampus_age-detection | 39 | null | transformers | 6,549 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: opencampus_age-detection
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5892857313156128
---
# opencampus_age-detection
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### child portrait face
#### generation x portrait face
#### millennials portrait face
#### pensioner portrait face
#### teenager portrait face
 |
Olivia-umich/SpanKeptParaphraser | 13f08b84c05c3f67fe915f5ee461e0f86787f428 | 2022-07-04T19:45:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | Olivia-umich | null | Olivia-umich/SpanKeptParaphraser | 39 | null | transformers | 6,550 | ---
license: apache-2.0
---
|
turingmachine/hupd-distilroberta-base | 5bd1290dfaa958dd29ebb8641674d2c88df0176b | 2022-07-05T15:30:46.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:HUPD/hupd",
"transformers",
"hupd",
"distilroberta",
"patents",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | fill-mask | false | turingmachine | null | turingmachine/hupd-distilroberta-base | 39 | 1 | transformers | 6,551 | ---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- hupd
- roberta
- distilroberta
- patents
license: cc-by-sa-4.0
datasets:
- HUPD/hupd
---
# HUPD DistilRoBERTa-Base Model
This HUPD DistilRoBERTa model was fine-tuned on the HUPD dataset with a masked language modeling objective. It was originally introduced in [this paper](TBD).
For more information about the Harvard USPTO Patent Dataset, please feel free to visit the [project website](https://patentdataset.org/) or the [project's GitHub repository](https://github.com/suzgunmirac/hupd).
### How to Use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
model = pipeline(task="fill-mask", model="turingmachine/hupd-distilroberta-base")
model("Improved <mask> for playing a game of thumb wrestling.")
```
Here is the output:
```python
[{'score': 0.4274042248725891,
'sequence': 'Improved method for playing a game of thumb wrestling.',
'token': 5448,
'token_str': ' method'},
{'score': 0.06967400759458542,
'sequence': 'Improved system for playing a game of thumb wrestling.',
'token': 467,
'token_str': ' system'},
{'score': 0.06849079579114914,
'sequence': 'Improved device for playing a game of thumb wrestling.',
'token': 2187,
'token_str': ' device'},
{'score': 0.04544765502214432,
'sequence': 'Improved apparatus for playing a game of thumb wrestling.',
'token': 26529,
'token_str': ' apparatus'},
{'score': 0.025765646249055862,
'sequence': 'Improved means for playing a game of thumb wrestling.',
'token': 839,
'token_str': ' means'}]
```
Alternatively, you can load the model and use it as follows:
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
# cuda/cpu
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained("turingmachine/hupd-distilroberta-base")
model = AutoModelForMaskedLM.from_pretrained("turingmachine/hupd-distilroberta-base").to(device)
TEXT = "Improved <mask> for playing a game of thumb wrestling."
inputs = tokenizer(TEXT, return_tensors="pt").to(device)
with torch.no_grad():
logits = model(**inputs).logits
# retrieve indices of <mask>
mask_token_indxs = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
for mask_idx in mask_token_indxs:
predicted_token_id = logits[0, mask_idx].argmax(axis=-1)
output = tokenizer.decode(predicted_token_id)
print(f'Prediction for the <mask> token at index {mask_idx}: "{output}"')
```
Here is the output:
```python
Prediction for the <mask> token at index 2: " method"
```
## Citation
For more information, please take a look at the original paper.
* Paper: [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](TBD)
* Authors: *Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber*
* BibTeX:
```
@article{suzgun2022hupd,
title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K and Kominers, Scott and Shieber, Stuart},
year={2022}
}
``` |
KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos | 88ecb4c66419d6db05f57a4b600d35300129b205 | 2022-07-23T14:44:08.000Z | [
"pytorch",
"deberta-v2",
"token-classification",
"ja",
"dataset:universal_dependencies",
"transformers",
"japanese",
"wikipedia",
"pos",
"dependency-parsing",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | false | KoichiYasuoka | null | KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos | 39 | null | transformers | 6,552 | ---
language:
- "ja"
tags:
- "japanese"
- "wikipedia"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# deberta-large-japanese-wikipedia-luw-upos
## Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-wikipedia). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
## How to Use
```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos")
s="国境の長いトンネルを抜けると雪国であった。"
t=tokenizer.tokenize(s)
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(list(zip(t,p)))
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## Reference
安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
Team-PIXEL/pixel-base-finetuned-pos-ud-arabic-padt | d2ad296d3b6a72c6f02d6ed2ceecbc4d9b251fee | 2022-07-13T00:21:13.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-pos-ud-arabic-padt | 39 | null | transformers | 6,553 | Entry not found |
huggingtweets/angelsexytexty-janieclone | a7e5148042812eefbed5d2f88a1e6dc94966d728 | 2022-07-28T13:51:41.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/angelsexytexty-janieclone | 39 | null | transformers | 6,554 | ---
language: en
thumbnail: http://www.huggingtweets.com/angelsexytexty-janieclone/1659016297136/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1536389142287892481/N6kCwACw_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1539644507880411137/05M0Qc_I_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Columbine Janie & Angel Sexy Texty</div>
<div style="text-align: center; font-size: 14px;">@angelsexytexty-janieclone</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Columbine Janie & Angel Sexy Texty.
| Data | Columbine Janie | Angel Sexy Texty |
| --- | --- | --- |
| Tweets downloaded | 2475 | 171 |
| Retweets | 1037 | 2 |
| Short tweets | 343 | 14 |
| Tweets kept | 1095 | 155 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3vy2ixd4/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 @angelsexytexty-janieclone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yvxvuns) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yvxvuns/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/angelsexytexty-janieclone')
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)
|
sam34738/bert-hindi-kabita | 5b686be729d946bee89c52f01b134530e7aae210 | 2022-07-13T19:31:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | sam34738 | null | sam34738/bert-hindi-kabita | 39 | null | transformers | 6,555 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hindi-kabita
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-hindi-kabita
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4795
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1956 | 1.0 | 460 | 0.5352 |
| 0.4796 | 2.0 | 920 | 0.4795 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
Team-PIXEL/pixel-base-finetuned-masakhaner-yor | eb79d078cd4db81bdbbacdf1258af0587e633a5a | 2022-07-15T03:33:30.000Z | [
"pytorch",
"pixel",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | Team-PIXEL | null | Team-PIXEL/pixel-base-finetuned-masakhaner-yor | 39 | null | transformers | 6,556 | Entry not found |
Bachstelze/Rapgenerator | 4ab4eaef76e56d6edf1efa987e1f1695976b96cd | 2022-07-20T15:39:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"de",
"dataset:genius lyrics",
"transformers",
"Text Generation",
"license:mit"
] | text-generation | false | Bachstelze | null | Bachstelze/Rapgenerator | 39 | null | transformers | 6,557 | ---
language: de
widget:
- text: "Ich mach′ ein'n Song auf mein′n Lieblings-MCs (jaja)"
tags:
- Text Generation
datasets:
- genius lyrics
license: mit
---
# GPT-Rapgenerator
The Rapgenerator is trained for [nullsechsroy](https://genius.com/artists/Nullsechsroy) on an english [GPT2](https://huggingface.co/transformers/model_doc/gpt2.html) that is converted to a german [GerPT2](https://github.com/bminixhofer/gerpt2).
We used the [genius](https://docs.genius.com/#/songs-h2) songlyrics from the following artists:
['Ace Tee', 'Aligatoah', 'AnnenMayKantereit', 'Apache 207', 'Azad', 'Badmómzjay', 'Bausa', 'Blumentopf', 'Blumio', 'Capital Bra', 'Casper', 'Celo & Abdi', 'Cro', 'Dardan', 'Dendemann', 'Die P', 'Dondon', 'Dynamite Deluxe', 'Edgar Wasser', 'Eko Fresh', 'Farid Bang', 'Favorite', 'Genetikk', 'Haftbefehl', 'Haiyti', 'Huss und Hodn', 'Jamule', 'Jamule', 'Juju', 'Kasimir1441', 'Katja Krasavice', 'Kay One', 'Kitty Kat', 'Kool Savas', 'LX & Maxwell', 'Leila Akinyi', 'Loredana', 'Loredana & Mozzik', 'Luciano', 'Marsimoto', 'Marteria', 'Morlockk Dilemma', 'Moses Pelham', 'Nimo', 'NullSechsRoy', 'Prinz Pi', 'SSIO', 'SXTN', 'Sabrina Setlur', 'Samy Deluxe', 'Sanito', 'Sebastian Fitzek', 'Shirin David', 'Summer Cem', 'T-Low', 'Ufo361', 'YBRE', 'YFG Pave']
|
shengnan/visualize-v0-pre10w-preseed1-ft2w-seed1 | 6e84747286bc734fa321720d6d917c408e87c12a | 2022-07-17T05:51:16.000Z | [
"pytorch",
"t5",
"transformers"
] | null | false | shengnan | null | shengnan/visualize-v0-pre10w-preseed1-ft2w-seed1 | 39 | null | transformers | 6,558 | Entry not found |
Anonymous1111/bert-base-emotion | d235f103f8464f7b65f1416b207d12b6797973c5 | 2022-07-18T10:32:56.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers",
"license:apache-2.0"
] | text-classification | false | Anonymous1111 | null | Anonymous1111/bert-base-emotion | 39 | null | transformers | 6,559 | ---
license: apache-2.0
---
|
BrunoHays/wav2vec2XLS-R-common_voice_10-fr | ac7fa7e969ef3f60fbd63f9a33947d40da38c126 | 2022-07-25T11:39:43.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | BrunoHays | null | BrunoHays/wav2vec2XLS-R-common_voice_10-fr | 39 | null | transformers | 6,560 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2XLS-R-common_voice_10-fr
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. -->
# wav2vec2XLS-R-common_voice_10-fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3934
- Wer: 0.2774
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 2.72 | 400 | 1.3597 | 0.8722 |
| 3.9444 | 5.44 | 800 | 0.5503 | 0.4751 |
| 0.5425 | 8.16 | 1200 | 0.4261 | 0.3445 |
| 0.2356 | 10.88 | 1600 | 0.4007 | 0.3042 |
| 0.1345 | 13.61 | 2000 | 0.4101 | 0.2836 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
diwank/bartner | 50124b1f2357e726a40f5ac1aac14fabd2fd09c5 | 2022-07-27T14:24:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | diwank | null | diwank/bartner | 39 | null | transformers | 6,561 | ---
license: mit
---
Bart + Gartner = Bartner |
tdobrxl/ClinicBERT | 4824b6802bfd113e24551840f61ba1a1ffab9659 | 2022-07-29T22:33:11.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | tdobrxl | null | tdobrxl/ClinicBERT | 39 | null | transformers | 6,562 | ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data.
## How to use
### Feature Extraction
```
from transformers import RobertaModel, RobertaTokenizer
model = RobertaModel.from_pretrained("tdobrxl/ClinicBERT")
tokenizer = RobertaTokenizer.from_pretrained("tdobrxl/ClinicBERT")
text = "Randomized Study of Shark Cartilage in Patients With Breast Cancer."
last_hidden_state, pooler_output = model(tokenizer.encode(text)).last_hidden_state, model(tokenizer.encode(text)).pooler_output
```
### Masked Word Prediction
```
from transformers import pipeline
fill_mask = pipeline("fill-mask", model="tdobrxl/ClinicBERT", tokenizer="tdobrxl/ClinicBERT")
text = "this is the start of a beautiful <mask>."
fill_mask(text)
```
```[{'score': 0.26558592915534973, 'token': 363, 'token_str': ' study', 'sequence': 'this is the start of a beautiful study.'}, {'score': 0.06330082565546036, 'token': 2010, 'token_str': ' procedure', 'sequence': 'this is the start of a beautiful procedure.'}, {'score': 0.04393036663532257, 'token': 661, 'token_str': ' trial', 'sequence': 'this is the start of a beautiful trial.'}, {'score': 0.0363750196993351, 'token': 839, 'token_str': ' period', 'sequence': 'this is the start of a beautiful period.'}, {'score': 0.027248281985521317, 'token': 436, 'token_str': ' treatment', 'sequence': 'this is the start of a beautiful treatment.'}``` |
Emran/ClinicalBERT_description_full_ICD10_Code | 2ba30aa7e482f86279d1889507dd1664f42c2520 | 2021-10-18T20:31:13.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | Emran | null | Emran/ClinicalBERT_description_full_ICD10_Code | 38 | null | transformers | 6,563 | Entry not found |
Helsinki-NLP/opus-mt-en-hy | 2b0a113b0968e30d7c6d4eedfe58007fe50ad819 | 2021-01-18T08:09:21.000Z | [
"pytorch",
"marian",
"text2text-generation",
"en",
"hy",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-en-hy | 38 | null | transformers | 6,564 | ---
language:
- en
- hy
tags:
- translation
license: apache-2.0
---
### eng-hye
* source group: English
* target group: Armenian
* OPUS readme: [eng-hye](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hye/README.md)
* model: transformer-align
* source language(s): eng
* target language(s): hye
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hye/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hye/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hye/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng.hye | 16.6 | 0.404 |
### System Info:
- hf_name: eng-hye
- source_languages: eng
- target_languages: hye
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hye/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'hy']
- src_constituents: {'eng'}
- tgt_constituents: {'hye', 'hye_Latn'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hye/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hye/opus-2020-06-16.test.txt
- src_alpha3: eng
- tgt_alpha3: hye
- short_pair: en-hy
- chrF2_score: 0.40399999999999997
- bleu: 16.6
- brevity_penalty: 1.0
- ref_len: 5115.0
- src_name: English
- tgt_name: Armenian
- train_date: 2020-06-16
- src_alpha2: en
- tgt_alpha2: hy
- prefer_old: False
- long_pair: eng-hye
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
Helsinki-NLP/opus-mt-zh-fi | 02f43b28f6765bc0397c4c2da1609e8c358243e1 | 2020-08-21T14:42:52.000Z | [
"pytorch",
"marian",
"text2text-generation",
"zh",
"fi",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | false | Helsinki-NLP | null | Helsinki-NLP/opus-mt-zh-fi | 38 | null | transformers | 6,565 | ---
language:
- zh
- fi
tags:
- translation
license: apache-2.0
---
### zho-fin
* source group: Chinese
* target group: Finnish
* OPUS readme: [zho-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md)
* model: transformer-align
* source language(s): cmn_Bopo cmn_Hani cmn_Latn nan_Hani yue yue_Hani
* target language(s): fin
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.fin | 35.1 | 0.579 |
### System Info:
- hf_name: zho-fin
- source_languages: zho
- target_languages: fin
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-fin/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'fi']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'fin'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-fin/opus-2020-06-17.test.txt
- src_alpha3: zho
- tgt_alpha3: fin
- short_pair: zh-fi
- chrF2_score: 0.579
- bleu: 35.1
- brevity_penalty: 0.935
- ref_len: 1847.0
- src_name: Chinese
- tgt_name: Finnish
- train_date: 2020-06-17
- src_alpha2: zh
- tgt_alpha2: fi
- prefer_old: False
- long_pair: zho-fin
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
IMSyPP/hate_speech_slo | e20bf5141fd7718c657493702dccaaae795147f3 | 2022-05-16T06:13:11.000Z | [
"pytorch",
"bert",
"text-classification",
"sl",
"transformers",
"license:mit"
] | text-classification | false | IMSyPP | null | IMSyPP/hate_speech_slo | 38 | null | transformers | 6,566 | ---
pipeline_tag: text-classification
inference: true
widget:
- text: "Sem Mark in živim v Ljubljani. Sem doktorski študent na Mednarodni podiplomski šoli Jožefa Stefana."
language:
- sl
license: mit
---
# Hate Speech Classifier for Social Media Content in Slovenian Language
A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.
## Tokenizer
During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.
## Model output
The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent |
SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune | 1aa14babad086d30fd7cc14836f816a857f171d4 | 2021-06-23T05:15:56.000Z | [
"pytorch",
"jax",
"t5",
"feature-extraction",
"transformers",
"summarization"
] | summarization | false | SEBIS | null | SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune | 38 | null | transformers | 6,567 | ---
tags:
- summarization
widget:
- text: "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"
---
# CodeTrans model for source code summarization csharp
Pretrained model on programming language csharp using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized csharp code functions: it works best with tokenized csharp functions.
## Model description
This CodeTrans model is based on the `t5-base` model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the source code summarization task for the csharp code snippets.
## Intended uses & limitations
The model could be used to generate the description for the csharp function or be fine-tuned on other csharp code tasks. It can be used on unparsed and untokenized csharp code. However, if the csharp code is tokenized, the performance should be better.
### How to use
Here is how to use this model to generate csharp function documentation using Transformers SummarizationPipeline:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
pipeline = SummarizationPipeline(
model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune"),
tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask_finetune", skip_special_tokens=True),
device=0
)
tokenized_code = "public static DateTime ParseUnixDateTime ( double unixTime ) { var dt = new DateTime ( CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , CODE_INTEGER , System . DateTimeKind . Utc ) ; dt = dt . AddSeconds ( unixTimeStamp ) . ToLocalTime ( ) ; return dt ; }"
pipeline([tokenized_code])
```
Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/source%20code%20summarization/csharp/base_model.ipynb).
## Training data
The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1)
## Training procedure
### Multi-task Pretraining
The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096).
It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture.
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
### Fine-tuning
This model was then fine-tuned on a single TPU Pod V2-8 for 500 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing csharp code.
## Evaluation results
For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score):
Test results :
| Language / Model | Python | SQL | C# |
| -------------------- | :------------: | :------------: | :------------: |
| CodeTrans-ST-Small | 8.45 | 17.55 | 19.74 |
| CodeTrans-ST-Base | 9.12 | 15.00 | 18.65 |
| CodeTrans-TF-Small | 10.06 | 17.71 | 20.40 |
| CodeTrans-TF-Base | 10.94 | 17.66 | 21.12 |
| CodeTrans-TF-Large | 12.41 | 18.40 | 21.43 |
| CodeTrans-MT-Small | 13.11 | 19.15 | 22.39 |
| CodeTrans-MT-Base | **13.37** | 19.24 | 23.20 |
| CodeTrans-MT-Large | 13.24 | 19.40 | **23.57** |
| CodeTrans-MT-TF-Small | 12.10 | 18.25 | 22.03 |
| CodeTrans-MT-TF-Base | 10.64 | 16.91 | 21.40 |
| CodeTrans-MT-TF-Large | 12.14 | **19.98** | 21.10 |
| CODE-NN | -- | 18.40 | 20.50 |
> Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)
|
SaulLu/recreate-history | 858f5225017881cd075c830c329427d1bea0b001 | 2021-05-28T16:37:37.000Z | [
"pytorch",
"albert",
"token-classification",
"bn",
"dataset:xtreme",
"transformers",
"collaborative",
"bengali",
"NER",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | false | SaulLu | null | SaulLu/recreate-history | 38 | null | transformers | 6,568 |
---
language: bn
tags:
- collaborative
- bengali
- NER
license: apache-2.0
datasets: xtreme
metrics:
- Loss
- Accuracy
- Precision
- Recall
---
# sahajBERT Named Entity Recognition
## Model description
[sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann).
Named Entities predicted by the model:
| Label id | Label |
|:--------:|:----:|
|0 |O|
|1 |B-PER|
|2 |I-PER|
|3 |B-ORG|
|4 |I-ORG|
|5 |B-LOC|
|6 |I-LOC|
## Intended uses & limitations
#### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast
# Initialize tokenizer
tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NER")
# Initialize model
model = AlbertForTokenClassification.from_pretrained("neuropark/sahajBERT-NER")
# Initialize pipeline
pipeline = TokenClassificationPipeline(tokenizer=tokenizer, model=model)
raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me
output = pipeline(raw_text)
```
#### Limitations and bias
<!-- Provide examples of latent issues and potential remediations. -->
WIP
## Training data
The model was initialized it with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) at step 19519 and trained on the bengali of [WikiANN ](https://huggingface.co/datasets/wikiann)
## Training procedure
Coming soon!
<!-- ```bibtex
@inproceedings{...,
year={2020}
}
``` -->
## Eval results
loss: 0.11714419722557068
accuracy: 0.9772286821705426
precision: 0.9585365853658536
recall: 0.9651277013752456
f1 : 0.9618208516886931
### BibTeX entry and citation info
Coming soon!
<!-- ```bibtex
@inproceedings{...,
year={2020}
}
``` -->
|
XSY/albert-base-v2-fakenews-discriminator | 49230d434cd1df26a368a2284ada7aeeacd8f25b | 2021-11-16T13:11:50.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | XSY | null | XSY/albert-base-v2-fakenews-discriminator | 38 | null | transformers | 6,569 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: albert-base-v2-fakenews-discriminator
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. -->
# albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
I use title and label to train the classifier
label_0 : Fake news
label_1 : Real news
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0910
- Accuracy: 0.9758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0452 | 1.0 | 1768 | 0.0910 | 0.9758 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
XSY/albert-base-v2-imdb-calssification | 833e0badbb0807db0f9382e61bf95f537e36cd42 | 2021-11-13T09:10:38.000Z | [
"pytorch",
"albert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | XSY | null | XSY/albert-base-v2-imdb-calssification | 38 | null | transformers | 6,570 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: albert-base-v2-imdb-calssification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93612
---
<!-- 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. -->
# albert-base-v2-imdb-calssification
label_0: negative
label_1: positive
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1983
- Accuracy: 0.9361
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.26 | 1.0 | 1563 | 0.1983 | 0.9361 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
aasem/wav2vec2-xls-r-300m-Urdu | 4d8badf671cc4297a2f264e61da973fb82fc78b2 | 2022-03-01T08:28:25.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | aasem | null | aasem/wav2vec2-xls-r-300m-Urdu | 38 | null | transformers | 6,571 | ---
datasets:
-
common_voice: ~
language:
-
ur: ~
library_name:
transformers: ~
license:
mit: ~
metrics:
-
wer: ~
model-index:
-
name:
wav2vec2-xls-r-300m-Urdu: ~
results:
-
task:
dataset:
args:
ur: ~
name:
: "common_voice"
: ~
type:
common_voice: ~
metrics:
-
type:
wer: ~
value:
0.2459: ~
-
type:
cer: ~
value:
0.0691: ~
type:
automatic-speech-recognition: ~
tags:
-
audio: ~
-
automatic-speech-recognition: ~
-
speech: ~
Finetuning of [Facebook's 300M model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Common Voice 8.0 Urdu dataset |
amtam0/timer-ner-en | c89664191fc6fcc9ab0755b487610121b5d171d4 | 2021-11-28T09:58:54.000Z | [
"pytorch",
"en",
"flair",
"token-classification",
"sequence-tagger-model"
] | token-classification | false | amtam0 | null | amtam0/timer-ner-en | 38 | 1 | flair | 6,572 | ---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
widget:
- text: "12 sets of 2 minutes 38 minutes between each set"
---
#### This model is used in the [Speech Interval Timer app](https://medium.com/@amtam0/speech-interval-timer-app-using-transformers-1df8fa3821d5)
7-class NER English model using [Flair TransformerWordEmbeddings - distilroberta-base](https://github.com/flairNLP/flair/).
| **tag** | **meaning** |
|---------------------------------|-----------|
| nb_rounds | Number of rounds |
| duration_br_sd | Duration btwn rounds in seconds |
| duration_br_min | Duration btwn rounds in minutes |
| duration_br_hr | Duration btwn rounds in hours |
| duration_wt_sd | workout duration in seconds |
| duration_wt_min | workout duration in minutes |
| duration_wt_hr | workout duration in hours |
---
The dataset was created manually (perfectible). Sentences example :
```
19 sets of 3 minutes 21 minutes between sets
start 7 sets of 32 seconds
create 13 sets of 26 seconds
init 8 series of 3 hours
2 sets of 30 seconds 35 minutes between each cycle
...
``` |
asapp/sew-d-base-plus-400k | d93aa9415b25df69eba6a72df98b0bc30ad5c1de | 2021-10-28T13:55:32.000Z | [
"pytorch",
"sew-d",
"feature-extraction",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"transformers",
"speech",
"license:apache-2.0"
] | feature-extraction | false | asapp | null | asapp/sew-d-base-plus-400k | 38 | null | transformers | 6,573 | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
|
bhavikardeshna/multilingual-bert-base-cased-vietnamese | 389f1f11625880b00408b04071e601d88936320b | 2021-12-21T11:44:14.000Z | [
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"transformers",
"autotrain_compatible"
] | question-answering | false | bhavikardeshna | null | bhavikardeshna/multilingual-bert-base-cased-vietnamese | 38 | null | transformers | 6,574 | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
chinhon/pegasus-newsroom-summarizer_02 | d414b047bcbd01efa2e62a062f6fe8d4d5b5cc9e | 2021-11-06T02:55:10.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | chinhon | null | chinhon/pegasus-newsroom-summarizer_02 | 38 | 1 | transformers | 6,575 | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-newsroom-summarizer_02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-newsroom-summarizer_02
This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2204
- Rouge1: 52.4459
- Rouge2: 35.2568
- Rougel: 41.6213
- Rougelsum: 48.7859
- Gen Len: 98.0627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3231 | 1.0 | 16113 | 1.2305 | 52.1565 | 34.8681 | 41.3189 | 48.4258 | 95.9049 |
| 1.3001 | 2.0 | 32226 | 1.2186 | 52.4921 | 35.2661 | 41.6264 | 48.8168 | 98.9241 |
| 1.2372 | 3.0 | 48339 | 1.2204 | 52.4459 | 35.2568 | 41.6213 | 48.7859 | 98.0627 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
danurahul/alex_gpt3_endoftext | 9472e564ff4306b8ef9387faa10dddbcb636ef8b | 2021-05-21T15:20:28.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | danurahul | null | danurahul/alex_gpt3_endoftext | 38 | null | transformers | 6,576 | Entry not found |
deepset/tinyroberta-squad2-step1 | 8289712ae4b3799ae63cdf0cdf1b321d0c9baac7 | 2022-02-15T14:29:07.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | deepset | null | deepset/tinyroberta-squad2-step1 | 38 | null | transformers | 6,577 | Entry not found |
educhav/Austin-DialoGPT-small | 72b5f77232ab9c085aa141971333de2372119bc9 | 2022-01-22T07:00:24.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | educhav | null | educhav/Austin-DialoGPT-small | 38 | null | transformers | 6,578 | ---
tags:
- conversational
---
# Austin Medina |
flax-community/roberta-hindi | 81f8b41477e02b631eac5fbbc6493ee57c8108ac | 2021-07-20T12:50:29.000Z | [
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | flax-community | null | flax-community/roberta-hindi | 38 | 1 | transformers | 6,579 | ---
widget:
- text: "मुझे उनसे बात करना <mask> अच्छा लगा"
- text: "हम आपके सुखद <mask> की कामना करते हैं"
- text: "सभी अच्छी चीजों का एक <mask> होता है"
---
# RoBERTa base model for Hindi language
Pretrained model on Hindi language using a masked language modeling (MLM) objective. [A more interactive & comparison demo is available here](https://huggingface.co/spaces/flax-community/roberta-hindi).
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/pretrain-roberta-from-scratch-in-hindi/7091), organized by [Hugging Face](https://huggingface.co/) and TPU usage sponsored by Google.
## Model description
RoBERTa Hindi is a transformers model pretrained on a large corpus of Hindi data(a combination of **mc4, oscar and indic-nlp** datasets)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi')
>>> unmasker("हम आपके सुखद <mask> की कामना करते हैं")
[{'score': 0.3310680091381073,
'sequence': 'हम आपके सुखद सफर की कामना करते हैं',
'token': 1349,
'token_str': ' सफर'},
{'score': 0.15317578613758087,
'sequence': 'हम आपके सुखद पल की कामना करते हैं',
'token': 848,
'token_str': ' पल'},
{'score': 0.07826550304889679,
'sequence': 'हम आपके सुखद समय की कामना करते हैं',
'token': 453,
'token_str': ' समय'},
{'score': 0.06304813921451569,
'sequence': 'हम आपके सुखद पहल की कामना करते हैं',
'token': 404,
'token_str': ' पहल'},
{'score': 0.058322224766016006,
'sequence': 'हम आपके सुखद अवसर की कामना करते हैं',
'token': 857,
'token_str': ' अवसर'}]
```
## Training data
The RoBERTa Hindi model was pretrained on the reunion of the following datasets:
- [OSCAR](https://huggingface.co/datasets/oscar) is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
- [mC4](https://huggingface.co/datasets/mc4) is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus.
- [IndicGLUE](https://indicnlp.ai4bharat.org/indic-glue/) is a natural language understanding benchmark.
- [Samanantar](https://indicnlp.ai4bharat.org/samanantar/) is a parallel corpora collection for Indic language.
- [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-and-large-summarization-corpus) is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
- [Hindi Text Short Summarization Corpus](https://www.kaggle.com/disisbig/hindi-text-short-summarization-corpus) is a collection of ~330k articles with their headlines collected from Hindi News Websites.
- [Old Newspapers Hindi](https://www.kaggle.com/crazydiv/oldnewspapershindi) is a cleaned subset of HC Corpora newspapers.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`.
- We had to perform cleanup of **mC4** and **oscar** datasets by removing all non hindi (non Devanagari) characters from the datasets.
- We tried to filter out evaluation set of WikiNER of [IndicGlue](https://indicnlp.ai4bharat.org/indic-glue/) benchmark by [manual labelling](https://github.com/amankhandelia/roberta_hindi/blob/master/wikiner_incorrect_eval_set.csv) where the actual labels were not correct and modifying the [downstream evaluation dataset](https://github.com/amankhandelia/roberta_hindi/blob/master/utils.py).
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores).A randomized shuffle of combined dataset of **mC4, oscar** and other datasets listed above was used to train the model. Training logs are present in [wandb](https://wandb.ai/wandb/hf-flax-roberta-hindi).
## Evaluation Results
RoBERTa Hindi is evaluated on various downstream tasks. The results are summarized below.
| Task | Task Type | IndicBERT | HindiBERTa | Indic Transformers Hindi BERT | RoBERTa Hindi Guj San | RoBERTa Hindi |
|-------------------------|----------------------|-----------|------------|-------------------------------|-----------------------|---------------|
| BBC News Classification | Genre Classification | **76.44** | 66.86 | **77.6** | 64.9 | 73.67 |
| WikiNER | Token Classification | - | 90.68 | **95.09** | 89.61 | **92.76** |
| IITP Product Reviews | Sentiment Analysis | **78.01** | 73.23 | **78.39** | 66.16 | 75.53 |
| IITP Movie Reviews | Sentiment Analysis | 60.97 | 52.26 | **70.65** | 49.35 | **61.29** |
## Team Members
- Aman K ([amankhandelia](https://huggingface.co/amankhandelia))
- Haswanth Aekula ([hassiahk](https://huggingface.co/hassiahk))
- Kartik Godawat ([dk-crazydiv](https://huggingface.co/dk-crazydiv))
- Prateek Agrawal ([prateekagrawal](https://huggingface.co/prateekagrawal))
- Rahul Dev ([mlkorra](https://huggingface.co/mlkorra))
## Credits
Huge thanks to Hugging Face 🤗 & Google Jax/Flax team for such a wonderful community week, especially for providing such massive computing resources. Big thanks to [Suraj Patil](https://huggingface.co/valhalla) & [Patrick von Platen](https://huggingface.co/patrickvonplaten) for mentoring during the whole week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:medium> |
google/bert_uncased_L-10_H-256_A-4 | 652c66397cc8b8db62c1c35ab290d55ef3239c44 | 2021-05-19T17:23:44.000Z | [
"pytorch",
"jax",
"bert",
"arxiv:1908.08962",
"transformers",
"license:apache-2.0"
] | null | false | google | null | google/bert_uncased_L-10_H-256_A-4 | 38 | null | transformers | 6,580 | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with WordPiece masking).
We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.
Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.
You can download the 24 BERT miniatures either from the [official BERT Github page](https://github.com/google-research/bert/), or via HuggingFace from the links below:
| |H=128|H=256|H=512|H=768|
|---|:---:|:---:|:---:|:---:|
| **L=2** |[**2/128 (BERT-Tiny)**][2_128]|[2/256][2_256]|[2/512][2_512]|[2/768][2_768]|
| **L=4** |[4/128][4_128]|[**4/256 (BERT-Mini)**][4_256]|[**4/512 (BERT-Small)**][4_512]|[4/768][4_768]|
| **L=6** |[6/128][6_128]|[6/256][6_256]|[6/512][6_512]|[6/768][6_768]|
| **L=8** |[8/128][8_128]|[8/256][8_256]|[**8/512 (BERT-Medium)**][8_512]|[8/768][8_768]|
| **L=10** |[10/128][10_128]|[10/256][10_256]|[10/512][10_512]|[10/768][10_768]|
| **L=12** |[12/128][12_128]|[12/256][12_256]|[12/512][12_512]|[**12/768 (BERT-Base)**][12_768]|
Note that the BERT-Base model in this release is included for completeness only; it was re-trained under the same regime as the original model.
Here are the corresponding GLUE scores on the test set:
|Model|Score|CoLA|SST-2|MRPC|STS-B|QQP|MNLI-m|MNLI-mm|QNLI(v2)|RTE|WNLI|AX|
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|BERT-Tiny|64.2|0.0|83.2|81.1/71.1|74.3/73.6|62.2/83.4|70.2|70.3|81.5|57.2|62.3|21.0|
|BERT-Mini|65.8|0.0|85.9|81.1/71.8|75.4/73.3|66.4/86.2|74.8|74.3|84.1|57.9|62.3|26.1|
|BERT-Small|71.2|27.8|89.7|83.4/76.2|78.8/77.0|68.1/87.0|77.6|77.0|86.4|61.8|62.3|28.6|
|BERT-Medium|73.5|38.0|89.6|86.6/81.6|80.4/78.4|69.6/87.9|80.0|79.1|87.7|62.2|62.3|30.5|
For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained for 4 epochs:
- batch sizes: 8, 16, 32, 64, 128
- learning rates: 3e-4, 1e-4, 5e-5, 3e-5
If you use these models, please cite the following paper:
```
@article{turc2019,
title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
journal={arXiv preprint arXiv:1908.08962v2 },
year={2019}
}
```
[2_128]: https://huggingface.co/google/bert_uncased_L-2_H-128_A-2
[2_256]: https://huggingface.co/google/bert_uncased_L-2_H-256_A-4
[2_512]: https://huggingface.co/google/bert_uncased_L-2_H-512_A-8
[2_768]: https://huggingface.co/google/bert_uncased_L-2_H-768_A-12
[4_128]: https://huggingface.co/google/bert_uncased_L-4_H-128_A-2
[4_256]: https://huggingface.co/google/bert_uncased_L-4_H-256_A-4
[4_512]: https://huggingface.co/google/bert_uncased_L-4_H-512_A-8
[4_768]: https://huggingface.co/google/bert_uncased_L-4_H-768_A-12
[6_128]: https://huggingface.co/google/bert_uncased_L-6_H-128_A-2
[6_256]: https://huggingface.co/google/bert_uncased_L-6_H-256_A-4
[6_512]: https://huggingface.co/google/bert_uncased_L-6_H-512_A-8
[6_768]: https://huggingface.co/google/bert_uncased_L-6_H-768_A-12
[8_128]: https://huggingface.co/google/bert_uncased_L-8_H-128_A-2
[8_256]: https://huggingface.co/google/bert_uncased_L-8_H-256_A-4
[8_512]: https://huggingface.co/google/bert_uncased_L-8_H-512_A-8
[8_768]: https://huggingface.co/google/bert_uncased_L-8_H-768_A-12
[10_128]: https://huggingface.co/google/bert_uncased_L-10_H-128_A-2
[10_256]: https://huggingface.co/google/bert_uncased_L-10_H-256_A-4
[10_512]: https://huggingface.co/google/bert_uncased_L-10_H-512_A-8
[10_768]: https://huggingface.co/google/bert_uncased_L-10_H-768_A-12
[12_128]: https://huggingface.co/google/bert_uncased_L-12_H-128_A-2
[12_256]: https://huggingface.co/google/bert_uncased_L-12_H-256_A-4
[12_512]: https://huggingface.co/google/bert_uncased_L-12_H-512_A-8
[12_768]: https://huggingface.co/google/bert_uncased_L-12_H-768_A-12
|
google/tapas-medium-finetuned-wikisql-supervised | c0429384d7ca2bd8bfe64a8e1d2f00d519299b5b | 2021-11-29T13:06:28.000Z | [
"pytorch",
"tf",
"tapas",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2004.02349",
"arxiv:2010.00571",
"arxiv:1709.00103",
"transformers",
"license:apache-2.0"
] | table-question-answering | false | google | null | google/tapas-medium-finetuned-wikisql-supervised | 38 | null | transformers | 6,581 | ---
language: en
tags:
- tapas
license: apache-2.0
datasets:
- wikisql
---
# TAPAS medium model fine-tuned on WikiSQL (in a supervised fashion)
his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training, and then fine-tuned in a chain on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253), and [WikiSQL](https://github.com/salesforce/WikiSQL). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is:
- `no_reset`, which corresponds to `tapas_wikisql_sqa_inter_masklm_medium` (intermediate pre-training, absolute position embeddings).
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding a cell selection head and aggregation head on top of the pre-trained model, and then jointly train these randomly initialized classification heads with the base model on SQA and WikiSQL.
## Intended uses & limitations
You can use this model for answering questions related to a table.
For code examples, we refer to the documentation of TAPAS on the HuggingFace website.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Question [SEP] Flattened table [SEP]
```
The authors did first convert the WikiSQL dataset into the format of SQA using automatic conversion scripts.
### Fine-tuning
The model was fine-tuned on 32 Cloud TPU v3 cores for 50,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, fine-tuning takes around 10 hours. The optimizer used is Adam with a learning rate of 6.17164e-5, and a warmup
ratio of 0.1424. See the [paper](https://arxiv.org/abs/2004.02349) for more details (tables 11 and 12).
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@article{DBLP:journals/corr/abs-1709-00103,
author = {Victor Zhong and
Caiming Xiong and
Richard Socher},
title = {Seq2SQL: Generating Structured Queries from Natural Language using
Reinforcement Learning},
journal = {CoRR},
volume = {abs/1709.00103},
year = {2017},
url = {http://arxiv.org/abs/1709.00103},
archivePrefix = {arXiv},
eprint = {1709.00103},
timestamp = {Mon, 13 Aug 2018 16:48:41 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1709-00103.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
gurkan08/bert-turkish-text-classification | 1a8ddc4b9c3818cb734b5c14b90141338a8e64d1 | 2021-05-19T17:50:18.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"transformers"
] | text-classification | false | gurkan08 | null | gurkan08/bert-turkish-text-classification | 38 | 1 | transformers | 6,582 | ---
language: tr
---
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
70% of the data were used for training and 30% for testing.
train f1-weighted score = %97
test f1-weighted score = %94
# Usage
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
out = nlp(text)
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
results = []
for result in out:
result['label'] = label_dict[result['label']]
results.append(result)
print(results)
# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
|
huggingtweets/bitcoin | ef6f3c42f4645f74fca90669420836ae1b9032aa | 2021-05-21T20:44:55.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/bitcoin | 38 | null | transformers | 6,583 | ---
language: en
thumbnail: https://www.huggingtweets.com/bitcoin/1612625608055/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/421692600446619648/dWAbC2wg_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Bitcoin 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@bitcoin 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 [@bitcoin's tweets](https://twitter.com/bitcoin).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>3206</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>1190</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>390</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>1626</td>
</tr>
</tbody>
</table>
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/9fss3789/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 @bitcoin's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pqrlo2u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pqrlo2u/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/bitcoin'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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*
</section>
[](https://twitter.com/intent/follow?screen_name=borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/michaeljackson | f29d75b3690f5a345e11f376ee47e983010d8249 | 2021-05-22T14:22:13.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/michaeljackson | 38 | null | transformers | 6,584 | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo_share.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css">
<style>
@media (prefers-color-scheme: dark) {
.prose { color: #E2E8F0 !important; }
.prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; }
}
</style>
<section class='prose'>
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('http://pbs.twimg.com/profile_images/556179314660478976/l_MadSiU_400x400.jpeg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Michael Jackson 🤖 AI Bot </div>
<div style="font-size: 15px; color: #657786">@michaeljackson 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 [@michaeljackson's tweets](https://twitter.com/michaeljackson).
<table style='border-width:0'>
<thead style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #CBD5E0'>
<th style='border-width:0'>Data</th>
<th style='border-width:0'>Quantity</th>
</tr>
</thead>
<tbody style='border-width:0'>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Tweets downloaded</td>
<td style='border-width:0'>2671</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Retweets</td>
<td style='border-width:0'>24</td>
</tr>
<tr style='border-width:0 0 1px 0; border-color: #E2E8F0'>
<td style='border-width:0'>Short tweets</td>
<td style='border-width:0'>32</td>
</tr>
<tr style='border-width:0'>
<td style='border-width:0'>Tweets kept</td>
<td style='border-width:0'>2615</td>
</tr>
</tbody>
</table>
[Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/3lg17rb5/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 @michaeljackson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/lnx54cjj) for full transparency and reproducibility.
At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/lnx54cjj/artifacts) is logged and versioned.
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation:
<pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline
generator = pipeline(<span style="color:#FF9800">'text-generation'</span>,
model=<span style="color:#FF9800">'huggingtweets/michaeljackson'</span>)
generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre>
### 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*
</section>
[](https://twitter.com/borisdayma)
<section class='prose'>
For more details, visit the project repository.
</section>
[](https://github.com/borisdayma/huggingtweets)
|
iamtarun/wav2vec-osr | 74950d7c4d19662200990eba4085543a321296b1 | 2021-11-04T15:08:10.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:librispeech_asr",
"transformers",
"audio",
"speech to text",
"license:apache-2.0"
] | automatic-speech-recognition | false | iamtarun | null | iamtarun/wav2vec-osr | 38 | null | transformers | 6,585 | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- speech to text
license: apache-2.0
widget:
- example_title: OSR sample 1
src: https://github.com/TheSoundOfAIOSR/rg_speech_to_text/blob/main/data/finetuning-dataset/audiofiles/TA-5.wav?raw=true
- example_title: OSR sample 2
src: https://github.com/TheSoundOfAIOSR/rg_speech_to_text/blob/main/data/finetuning-dataset/audiofiles/TK-17.wav?raw=true
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
---
# Wav2Vec-OSR
Finetuned facebook's wav2vec2 model for speech to text module of [The Sound Of AI open source research group](https://thesoundofaiosr.github.io/).
The original base model is pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
## Paper
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
## Abstract
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
The original model can also be found in hugging face public model repository [here](https://huggingface.co/facebook/wav2vec2-base-960h)
## Usage
```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import soundfile as sf
import torch
# load tokenizer, data_processor and model
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("iamtarun/wav2vec-osr")
processor = Wav2Vec2Processor.from_pretrained("iamtarun/wav2vec-osr")
model = Wav2Vec2ForCTC.from_pretrained("iamtarun/wav2vec-osr")
model = model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# define function to read in sound file
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
# speech data is passed to data processor whose output is then fed to model
input_values = processor(ds["speech"][:2], sampling_rate=rate, padding="longest", return_tensors="pt").input_values.to(device)
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim =-1)
transcriptions = tokenizer.decode(predicted_ids[0])
print(transcriptions)
``` |
jordan-m-young/buzz-article-gpt-2 | 1893609bd968d9f413d0cc84c4e0859c67907024 | 2021-05-23T06:03:38.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | jordan-m-young | null | jordan-m-young/buzz-article-gpt-2 | 38 | null | transformers | 6,586 | Entry not found |
kssteven/ibert-roberta-large | 202dedcec60c0aece82a3c4d424cb7505efcb31f | 2021-05-10T05:34:01.000Z | [
"pytorch",
"ibert",
"fill-mask",
"arxiv:1907.11692",
"arxiv:2101.01321",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kssteven | null | kssteven/ibert-roberta-large | 38 | null | transformers | 6,587 | # I-BERT large model
This model, `ibert-roberta-large`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this papaer](https://arxiv.org/abs/2101.01321).
I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.
In particular, I-BERT replaces all floating point operations in the Transformer architectures (e.g., MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations.
This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU.
The best model parameters searched via quantization-aware finetuning can be then exported (e.g., to TensorRT) for integer-only deployment of the model.
## Finetuning Procedure
Finetuning of I-BERT consists of 3 stages: (1) Full-precision finetuning from the pretrained model on a down-stream task, (2) model quantization, and (3) integer-only finetuning (i.e., quantization-aware training) of the quantized model.
### Full-precision finetuning
Full-precision finetuning of I-BERT is similar to RoBERTa finetuning.
For instance, you can run the following command to finetune on the [MRPC](https://www.microsoft.com/en-us/download/details.aspx?id=52398) text classification task.
```
python examples/text-classification/run_glue.py \
--model_name_or_path kssteven/ibert-roberta-large \
--task_name MRPC \
--do_eval \
--do_train \
--evaluation_strategy epoch \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--save_steps 115 \
--learning_rate 2e-5 \
--num_train_epochs 10 \
--output_dir $OUTPUT_DIR
```
### Model Quantization
Once you are done with full-precision finetuning, open up `config.json` in your checkpoint directory and set the `quantize` attribute as `true`.
```
{
"_name_or_path": "kssteven/ibert-roberta-large",
"architectures": [
"IBertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"finetuning_task": "mrpc",
"force_dequant": "none",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "ibert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"quant_mode": true,
"tokenizer_class": "RobertaTokenizer",
"transformers_version": "4.4.0.dev0",
"type_vocab_size": 1,
"vocab_size": 50265
}
```
Then, your model will automatically run as the integer-only mode when you load the checkpoint.
Also, make sure to delete `optimizer.pt`, `scheduler.pt` and `trainer_state.json` in the same directory.
Otherwise, HF will not reset the optimizer, scheduler, or trainer state for the following integer-only finetuning.
### Integer-only finetuning (Quantization-aware training)
Finally, you will be able to run integer-only finetuning simply by loading the checkpoint file you modified.
Note that the only difference in the example command below is `model_name_or_path`.
```
python examples/text-classification/run_glue.py \
--model_name_or_path $CHECKPOINT_DIR
--task_name MRPC \
--do_eval \
--do_train \
--evaluation_strategy epoch \
--max_seq_length 128 \
--per_device_train_batch_size 32 \
--save_steps 115 \
--learning_rate 1e-6 \
--num_train_epochs 10 \
--output_dir $OUTPUT_DIR
```
## Citation info
If you use I-BERT, please cite [our papaer](https://arxiv.org/abs/2101.01321).
```
@article{kim2021bert,
title={I-BERT: Integer-only BERT Quantization},
author={Kim, Sehoon and Gholami, Amir and Yao, Zhewei and Mahoney, Michael W and Keutzer, Kurt},
journal={arXiv preprint arXiv:2101.01321},
year={2021}
}
```
|
describeai/gemini | aa5c52e95888664ccbb83d868de3b7b26ae123c3 | 2022-05-14T00:46:52.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"transformers",
"Explain code",
"Code Summarization",
"Summarization",
"license:mit",
"autotrain_compatible"
] | text2text-generation | false | describeai | null | describeai/gemini | 38 | 0 | transformers | 6,588 | ---
language: en
tags:
- Explain code
- Code Summarization
- Summarization
license: mit
---
# Gemini
For in-depth understanding of our model and methods, please see our blog [here](https://www.describe-ai.com/gemini)
## Model description
Gemini is a transformer based on Google's T5 model. The model is pre-trained on approximately 800k code/description pairs and then fine-tuned on 10k higher-level explanations that were synthetically generated. Gemini is capable of summarization/explaining short to medium code snippets in:
- Python
- Javascript (mostly vanilla JS, however, it can handle frameworks like React as well)
- Java
- Ruby
- Go
And outputs a description in English.
## Intended uses
Gemini without any additional fine-tuning is capable of explaining code in a sentence or two and typically performs best in Python and Javascript. We recommend using Gemini for either simple code explanation, documentation or producing more synthetic data to improve its explanations.
### How to use
You can use this model directly with a pipeline for Text2Text generation, as shown below:
```python
from transformers import pipeline, set_seed
summarizer = pipeline('text2text-generation', model='describeai/gemini')
code = "print('hello world!')"
response = summarizer(code, max_length=100, num_beams=3)
print("Summarized code: " + response[0]['generated_text'])
```
Which should yield something along the lines of:
```
Summarized code: The following code is greeting the world.
```
### Model sizes
- Gemini (this repo): 770 Million Parameters
- Gemini-Small - 220 Million Parameters
### Limitations
Typically, Gemini may produce overly simplistic descriptions that don't encompass the entire code snippet. We suspect with more training data, this could be circumvented and will produce better results.
### About Us
A Describe.ai, we are focused on building Artificial Intelligence systems that can understand language as well as humans. While a long path, we plan to contribute our findings to our API to the Open Source community. |
mervenoyan/PubMedBERT-QNLI | 8bd833ad8f65c11553c0b7c9230323aed04c4df9 | 2021-08-26T10:27:15.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | mervenoyan | null | mervenoyan/PubMedBERT-QNLI | 38 | 7 | transformers | 6,589 |
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question is answered.
> Example input: [CLS] Your question [SEP] The context to be searched in [SEP]
Link to the original model: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
Credits to the paper:
> @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and
> Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann
> and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific
> Language Model Pretraining for Biomedical Natural Language
> Processing}, year = {2020}, eprint = {arXiv:2007.15779}, }
|
mrm8488/spanbert-base-finetuned-tacred | 52aefc31f92dda6435312f6c785ddb7506e6d218 | 2021-05-20T00:53:07.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"arxiv:1907.10529",
"transformers"
] | feature-extraction | false | mrm8488 | null | mrm8488/spanbert-base-finetuned-tacred | 38 | null | transformers | 6,590 | ---
language: en
thumbnail:
---
# SpanBERT base fine-tuned on TACRED
[SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [TACRED](https://nlp.stanford.edu/projects/tacred/) dataset by [them](https://github.com/facebookresearch/SpanBERT#finetuned-models-squad-1120-relation-extraction-coreference-resolution)
## Details of SpanBERT
[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://arxiv.org/abs/1907.10529)
## Dataset 📚
[TACRED](https://nlp.stanford.edu/projects/tacred/) A large-scale relation extraction dataset with 106k+ examples over 42 TAC KBP relation types.
## Model fine-tuning 🏋️
You can get the fine-tuning script [here](https://github.com/facebookresearch/SpanBERT)
```bash
python code/run_tacred.py \
--do_train \
--do_eval \
--data_dir <TACRED_DATA_DIR> \
--model spanbert-base-cased \
--train_batch_size 32 \
--eval_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 10 \
--max_seq_length 128 \
--output_dir tacred_dir \
--fp16
```
## Results Comparison 📝
| | SQuAD 1.1 | SQuAD 2.0 | Coref | TACRED |
| ---------------------- | ------------- | --------- | ------- | ------ |
| | F1 | F1 | avg. F1 | F1 |
| BERT (base) | 88.5* | 76.5* | 73.1 | 67.7 |
| SpanBERT (base) | [92.4*](https://huggingface.co/mrm8488/spanbert-base-finetuned-squadv1) | [83.6*](https://huggingface.co/mrm8488/spanbert-base-finetuned-squadv2) | 77.4 | **68.2** (this one) |
| BERT (large) | 91.3 | 83.3 | 77.1 | 66.4 |
| SpanBERT (large) | [94.6](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv1) | [88.7](https://huggingface.co/mrm8488/spanbert-large-finetuned-squadv2) | 79.6 | [70.8](https://huggingface.co/mrm8488/spanbert-base-finetuned-tacred) |
Note: The numbers marked as * are evaluated on the development sets because those models were not submitted to the official SQuAD leaderboard. All the other numbers are test numbers.
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/spanish-t5-small-sqac-for-qa | a1da1f72dc52c2812517103a37ce98426d039430 | 2021-09-03T10:22:10.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"es",
"dataset:BSC-TeMU/SQAC",
"transformers",
"QA",
"Q&A",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/spanish-t5-small-sqac-for-qa | 38 | 3 | transformers | 6,591 | ---
language: es
tags:
- QA
- Q&A
datasets:
- BSC-TeMU/SQAC
widget:
- text: "question: ¿Cuál es el nombre que se le da a la unidad morfológica y funcional de los seres vivos? context: La célula (del latín cellula, diminutivo de cella, ‘celda’) es la unidad morfológica y funcional de todo ser vivo. De hecho, la célula es el elemento de menor tamaño que puede considerarse vivo.\u200b De este modo, puede clasificarse a los organismos vivos según el número de células que posean: si solo tienen una, se les denomina unicelulares (como pueden ser los protozoos o las bacterias, organismos microscópicos); si poseen más, se les llama pluricelulares. En estos últimos el número de células es variable: de unos pocos cientos, como en algunos nematodos, a cientos de billones (1014), como en el caso del ser humano. Las células suelen poseer un tamaño de 10 µm y una masa de 1 ng, si bien existen células mucho mayores."
---
# Spanish T5 (small) fine-tuned on **SQAC** for Spanish **QA** 📖❓
[spanish-T5-small](https://huggingface.co/flax-community/spanish-t5-small) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task.
## Details of Spanish T5 (small)
T5 (small) like arch trained from scatch on [large_spanish_corpus](https://huggingface.co/datasets/large_spanish_corpus) for **HuggingFace/Flax/Jax Week**.
## Details of the dataset 📚
This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
This dataset can be used to build extractive-QA.
## Results on test dataset 📝
| Metric | # Value |
| ------ | --------- |
| **BLEU** | **41.94** |
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ckpt = 'mrm8488/spanish-t5-small-sqac-for-qa'
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = T5ForConditionalGeneration.from_pretrained(ckpt).to(device)
def get_answer(question, context):
input_text = 'question: %s context: %s' % (question, context)
features = tokenizer([input_text ], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
output = model.generate(input_ids=features['input_ids'].to(device),
attention_mask=features['attention_mask'].to(device))
return tokenizer.decode(output[0], skip_special_tokens=True)
context = '''
La ex codirectora del grupo de investigación de IA ética de Google, Margaret Mitchell,
quien fue despedida en febrero después de una controversia sobre un artículo crítico del que fue coautora,
se unirá a HuggingFace para ayudar a que los algoritmos de IA sean más justos.
'''
question = '¿Qué hará Margaret Mitchell en HuggingFace?'
print(get_answer(context, question))
# ayudar a que los algoritmos de ia sean más justos
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Narrativa](https://www.narrativa.com/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/t5-small-finetuned-squadv2 | 7bea82cd43074683a78620325dc0474dc97d8e85 | 2021-05-06T16:25:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad_v2",
"arxiv:1910.10683",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mrm8488 | null | mrm8488/t5-small-finetuned-squadv2 | 38 | 1 | transformers | 6,592 | ---
language: en
datasets:
- squad_v2
---
# T5-small fine-tuned on SQuAD v2
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) [(small)](https://huggingface.co/t5-small) fine-tuned on [SQuAD v2](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
## Details of T5
The **T5** model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* in Here the abstract:
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.
## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓
Dataset ID: ```squad_v2``` from [Huggingface/NLP](https://github.com/huggingface/nlp)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| squad_v2 | train | 130319 |
| squad_v2 | valid | 11873 |
How to load it from [nlp](https://github.com/huggingface/nlp)
```python
train_dataset = nlp.load_dataset('squad_v2', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('squad_v2', split=nlp.Split.VALIDATION)
```
Check out more about this dataset and others in [NLP Viewer](https://huggingface.co/nlp/viewer/)
## Model fine-tuning 🏋️
The training script is a slightly modified version of [this awesome one](https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb) by [Suraj Patil](https://twitter.com/psuraj28)
## Results 📝
| Metric | # Value |
| ------ | --------- |
| **EM** | **69.46** |
| **F1** | **73.01** |
## Model in Action 🚀
```python
from transformers import AutoModelWithLMHead, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-squadv2")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-small-finetuned-squadv2")
def get_answer(question, context):
input_text = "question: %s context: %s </s>" % (question, context)
features = tokenizer([input_text], return_tensors='pt')
output = model.generate(input_ids=features['input_ids'],
attention_mask=features['attention_mask'])
return tokenizer.decode(output[0])
context = "Manuel has created RuPERTa-base (a Spanish RoBERTa) with the support of HF-Transformers and Google"
question = "Who has supported Manuel?"
get_answer(question, context)
# output: 'HF-Transformers and Google'
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
ncduy/roberta-imdb-sentiment-analysis | 031415a39e5f4a2e92fff93c5637fbfb28c78674 | 2021-08-09T10:54:50.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | ncduy | null | ncduy/roberta-imdb-sentiment-analysis | 38 | null | transformers | 6,593 | Entry not found |
nguyenvulebinh/spelling-oov | 48687d18de1e89f1d0ddba0eb4e686d3a4d67264 | 2021-12-15T17:00:58.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | nguyenvulebinh | null | nguyenvulebinh/spelling-oov | 38 | null | transformers | 6,594 | ```python
from transformers import EncoderDecoderModel
from importlib.machinery import SourceFileLoader
from transformers.file_utils import cached_path, hf_bucket_url
import torch
import os
## Load model & tokenizer
cache_dir='./cache'
model_name='nguyenvulebinh/spelling-oov'
def download_tokenizer_files():
resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
for item in resources:
if not os.path.exists(os.path.join(cache_dir, item)):
tmp_file = hf_bucket_url(model_name, filename=item)
tmp_file = cached_path(tmp_file,cache_dir=cache_dir)
os.rename(tmp_file, os.path.join(cache_dir, item))
download_tokenizer_files()
spell_tokenizer = SourceFileLoader("envibert.tokenizer",os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
spell_model = EncoderDecoderModel.from_pretrained(model_name)
def oov_spelling(word, num_candidate=1):
result = []
inputs = spell_tokenizer([word.lower()])
input_ids = inputs['input_ids']
attention_mask = inputs['attention_mask']
inputs = {
"input_ids": torch.tensor(input_ids),
"attention_mask": torch.tensor(attention_mask)
}
outputs = spell_model.generate(**inputs, num_return_sequences=num_candidate)
for output in outputs.cpu().detach().numpy().tolist():
result.append(spell_tokenizer.sp_model.DecodePieces(spell_tokenizer.decode(output, skip_special_tokens=True).split()))
return result
oov_spelling('spacespeaker')
# output: ['x pây x pếch cơ']
``` |
nyu-mll/roberta-med-small-1M-3 | 1c7750b0a6258ca88958e1b9741f77d07e0fd4d3 | 2021-05-20T19:09:09.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nyu-mll | null | nyu-mll/roberta-med-small-1M-3 | 38 | null | transformers | 6,595 | # RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
pierreguillou/t5-base-qa-squad-v1.1-portuguese | 21c3e49be1ce1a083ba4278427dc7799592ab33c | 2022-01-27T14:38:28.000Z | [
"pytorch",
"t5",
"text2text-generation",
"pt",
"dataset:squad",
"dataset:squad_v1_pt",
"transformers",
"qa",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | pierreguillou | null | pierreguillou/t5-base-qa-squad-v1.1-portuguese | 38 | 4 | transformers | 6,596 | ---
language:
- pt
tags:
- text2text-generation
- t5
- pytorch
- qa
datasets:
- squad
- squad_v1_pt
metrics:
- precision
- recall
- f1
- accuracy
- squad
model-index:
- name: checkpoints
results:
- task:
name: text2text-generation
type: text2text-generation
dataset:
name: squad
type: squad
metrics:
- name: f1
type: f1
value: 79.3
- name: exact-match
type: exact-match
value: 67.3983
widget:
- text: "question: Quando começou a pandemia de Covid-19 no mundo? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano."
- text: "question: Onde foi descoberta a Covid-19? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano."
---
# T5 base finetuned for Question Answering (QA) on SQUaD v1.1 Portuguese
## Introduction
**t5-base-qa-squad-v1.1-portuguese** is a QA model (Question Answering) in Portuguese that was finetuned on 27/01/2022 in Google Colab from the model [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) of Neuralmind on the dataset SQUAD v1.1 in portuguese from the [Deep Learning Brasil group](http://www.deeplearningbrasil.com.br/) by using a Test2Text-Generation objective.
Due to the small size of T5 base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset:
- **f1**: 79.3
- **exact_match**: 67.3983
Check our other QA models in Portuguese finetuned on SQUAD v1.1:
- [Portuguese BERT base cased QA](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese)
- [Portuguese BERT large cased QA](https://huggingface.co/pierreguillou/bert-large-cased-squad-v1.1-portuguese)
- [Portuguese ByT5 small QA](https://huggingface.co/pierreguillou/byt5-small-qa-squad-v1.1-portuguese)
## Blog post
[NLP nas empresas | Como eu treinei um modelo T5 em português na tarefa QA no Google Colab](https://medium.com/@pierre_guillou/nlp-nas-empresas-como-eu-treinei-um-modelo-t5-em-portugu%C3%AAs-na-tarefa-qa-no-google-colab-e8eb0dc38894) (27/01/2022)
## Widget & App
You can test this model into the widget of this page.
Use as well the [QA App | T5 base pt](https://huggingface.co/spaces/pierreguillou/question-answering-portuguese-t5-base) that allows using the model T5 base finetuned on the QA task with the SQuAD v1.1 pt dataset.
## Using the model for inference in production
````
# install pytorch: check https://pytorch.org/
# !pip install transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# model & tokenizer
model_name = "pierreguillou/t5-base-qa-squad-v1.1-portuguese"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# parameters
max_target_length=32
num_beams=1
early_stopping=True
input_text = 'question: Quando foi descoberta a Covid-19? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.'
label = '1 de dezembro de 2019'
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs["input_ids"],
max_length=max_target_length,
num_beams=num_beams,
early_stopping=early_stopping
)
pred = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
print('true answer |', label)
print('pred |', pred)
````
You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.
````
!pip install transformers
import transformers
from transformers import pipeline
# model
model_name = "pierreguillou/t5-base-qa-squad-v1.1-portuguese"
# parameters
max_target_length=32
num_beams=1
early_stopping=True
clean_up_tokenization_spaces=True
input_text = 'question: Quando foi descoberta a Covid-19? context: A pandemia de COVID-19, também conhecida como pandemia de coronavírus, é uma pandemia em curso de COVID-19, uma doença respiratória aguda causada pelo coronavírus da síndrome respiratória aguda grave 2 (SARS-CoV-2). A doença foi identificada pela primeira vez em Wuhan, na província de Hubei, República Popular da China, em 1 de dezembro de 2019, mas o primeiro caso foi reportado em 31 de dezembro do mesmo ano.'
label = '1 de dezembro de 2019'
text2text = pipeline(
"text2text-generation",
model=model_name,
max_length=max_target_length,
num_beams=num_beams,
early_stopping=early_stopping,
clean_up_tokenization_spaces=clean_up_tokenization_spaces
)
pred = text2text(input_text)
print('true answer |', label)
print('pred |', pred)
````
## Training procedure
### Notebook
The notebook of finetuning ([HuggingFace_Notebook_t5-base-portuguese-vocab_question_answering_QA_squad_v11_pt.ipynb](https://github.com/piegu/language-models/blob/master/HuggingFace_Notebook_t5_base_portuguese_vocab_question_answering_QA_squad_v11_pt.ipynb)) is in github.
### Hyperparameters
````
# do training and evaluation
do_train = True
do_eval= True
# batch
batch_size = 4
gradient_accumulation_steps = 3
per_device_train_batch_size = batch_size
per_device_eval_batch_size = per_device_train_batch_size*16
# LR, wd, epochs
learning_rate = 1e-4
weight_decay = 0.01
num_train_epochs = 10
fp16 = True
# logs
logging_strategy = "steps"
logging_first_step = True
logging_steps = 3000 # if logging_strategy = "steps"
eval_steps = logging_steps
# checkpoints
evaluation_strategy = logging_strategy
save_strategy = logging_strategy
save_steps = logging_steps
save_total_limit = 3
# best model
load_best_model_at_end = True
metric_for_best_model = "f1" #"loss"
if metric_for_best_model == "loss":
greater_is_better = False
else:
greater_is_better = True
# evaluation
num_beams = 1
````
### Training results
````
Num examples = 87510
Num Epochs = 10
Instantaneous batch size per device = 4
Total train batch size (w. parallel, distributed & accumulation) = 12
Gradient Accumulation steps = 3
Total optimization steps = 72920
Step Training Loss Exact Match F1
3000 0.776100 61.807001 75.114517
6000 0.545900 65.260170 77.468930
9000 0.460500 66.556291 78.491938
12000 0.393400 66.821192 78.745397
15000 0.379800 66.603595 78.815515
18000 0.298100 67.578051 79.287899
21000 0.303100 66.991485 78.979669
24000 0.251600 67.275307 78.929923
27000 0.237500 66.972564 79.333612
30000 0.220500 66.915799 79.236574
33000 0.182600 67.029328 78.964212
36000 0.190600 66.982025 79.086125
```` |
robkayinto/distilbert-base-uncased-finetuned-emotion | 45bd8e9904cf14c16d892e78269d5b5be9aeb6a5 | 2022-05-31T15:17:31.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | false | robkayinto | null | robkayinto/distilbert-base-uncased-finetuned-emotion | 38 | null | transformers | 6,597 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9195
- name: F1
type: f1
value: 0.919829815254287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.9195
- F1: 0.9198
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8338 | 1.0 | 250 | 0.3114 | 0.9065 | 0.9044 |
| 0.2467 | 2.0 | 500 | 0.2192 | 0.9195 | 0.9198 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.2+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
sentence-transformers/nli-bert-base-cls-pooling | e59f6ff65548f7fa407ddd9b4e2754454a7a36e5 | 2021-08-05T08:27:14.000Z | [
"pytorch",
"bert",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | false | sentence-transformers | null | sentence-transformers/nli-bert-base-cls-pooling | 38 | null | sentence-transformers | 6,598 | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-bert-base-cls-pooling
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/nli-bert-base-cls-pooling')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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/nli-bert-base-cls-pooling')
model = AutoModel.from_pretrained('sentence-transformers/nli-bert-base-cls-pooling')
# 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, max pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
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/nli-bert-base-cls-pooling)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
veronica320/TE-for-Event-Extraction | 38f55b8c833f880a487358e42e6ed5419f93a039 | 2021-07-30T23:11:05.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/TE-for-Event-Extraction | 38 | null | transformers | 6,599 | # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
The label mapping is:
```
LABEL_0: Contradiction
LABEL_1: Neutral
LABEL_2: Entailment
```
## Demo
To see how the model works, type a sentence and a hypothesis separated by "\<\/s\>\<\/s\>" in the right-hand-side textbox under "Hosted inference API".
Example:
- Input:
```
A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem. </s></s> This text is about an attack.
```
- Output:
```
LABEL_2 (Entailment)
```
## Usage
- To use the TE model independently, follow the [huggingface documentation on AutoModelForSequenceClassification](https://huggingface.co/transformers/task_summary.html#sequence-classification).
- To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
### BibTeX entry and citation info
```
@inproceedings{lyu-etal-2021-zero,
title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
author = "Lyu, Qing and
Zhang, Hongming and
Sulem, Elior and
Roth, Dan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.42",
doi = "10.18653/v1/2021.acl-short.42",
pages = "322--332",
abstract = "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {``}A city was attacked{''} entails {``}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.",
}
``` |
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