license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | [] | false | From Google Drive We have put the model on Google Drive for users. | Model | DATASET(year) | Base Model | | ------------------------------------------------------------ | ------------- | ---------------------- | | [SSCI-BERT-e2](https://drive.google... | 444e4cf7cbb0d371de27d8d136855e90 |
apache-2.0 | [] | false | Evaluation & Results - We use SSCI-BERT and SSCI-SciBERT to perform Text Classificationon different social science research corpus. The experimental results are as follows. Relevant data sets are available for download in the **Verification task datasets** folder of this project. | 6e645a263932c486b41365f7990445dc |
apache-2.0 | [] | false | JCR Title Classify Dataset | Model | accuracy | macro avg | weighted avg | | ---------------------- | -------- | --------- | ------------ | | Bert-base-cased | 28.43 | 22.06 | 21.86 | | Scibert-scivocab-cased | 38.48 | 33.89 | 33.92 | | SSCI-BERT-e2 | 40.4... | 24b54e1617d65c6390857501c00c7918 |
apache-2.0 | [] | false | JCR Abstract Classify Dataset | Model | accuracy | macro avg | weighted avg | | ---------------------- | -------- | --------- | ------------ | | Bert-base-cased | 48.59 | 42.8 | 42.82 | | Scibert-scivocab-cased | 55.59 | 51.4 | 51.81 | | SSCI-BERT-e2 | 5... | 076593530def49ed0842e4b575e3d7f4 |
apache-2.0 | [] | false | JCR Mixed Titles and Abstracts Dataset | **Model** | **accuracy** | **macro avg** | **weighted avg** | | ---------------------- | ------------ | -------------- | ----------------- | | Bert-base-cased | 58.24 | 57.27 | 57.25 | | Scibert-scivocab-cased | 59.58 | ... | 1474171a314cd8ce8ac886e1efaffa45 |
apache-2.0 | [] | false | SSCI Abstract Structural Function Recognition (Classify Dataset) | | Bert-base-cased | SSCI-BERT-e2 | SSCI-BERT-e4 | support | | ------------ | -------------------------- | ------------------- | ------------------- | ----------- | | B | 63.77 |... | e2ccc51df5d14502df94a20743e845eb |
apache-2.0 | [] | false | Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - **Users can use the model arbitra... | 4db86640a93b3af7740e4721ac351145 |
apache-2.0 | [] | false | Acknowledgment - SSCI-BERT was trained based on [BERT-Base-Cased]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - SSCI-SciBERT was trained based on [scibert-scivocab-cased]([allenai/scibert: A BERT model for scientific text. (github.co... | 2754dc096b0618ee1d2c35d1f1b1821a |
apache-2.0 | [] | false | DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings [](https://github.com/voidism/DiffCSE/) [](https://colab.research.google.com/github/voidis... | ec875ea97bc29ec61a06a94c019fdfd6 |
apache-2.0 | [] | false | Overview  We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the ed... | 8b0fbbeb546f767bd5b17771201b05b0 |
apache-2.0 | [] | false | Install our customized Transformers package ``` cd transformers-4.2.1 pip install . ``` > If you have already installed `transformers==4.2.1` through pip, you need to put `modeling_bert.py` into `<your_python_env>/site-packages/transformers/models/bert/modeling_bert.py` and `modeling_roberta.py` into `<your_python_env... | 7fd0209084922bfa3068c199d0a2a4af |
apache-2.0 | [] | false | Training (The same as `run_diffcse.sh`.) ```bash python train.py \ --model_name_or_path bert-base-uncased \ --generator_name distilbert-base-uncased \ --train_file data/wiki1m_for_simcse.txt \ --output_dir <your_output_model_dir> \ --num_train_epochs 2 \ --per_device_train_batch_size 64 \ -... | 812346133f9b761d57df597513a43a67 |
apache-2.0 | [] | false | Evaluation [](https://colab.research.google.com/github/voidism/DiffCSE/blob/master/diffcse_evaluation.ipynb) We provide a simple colab notebook to reproduce our results easily. We can also run the commands below for evaluation: ```bash pyt... | c4c31529ac00efd578555dc421f0369a |
apache-2.0 | [] | false | Transfer Tasks ```bash python evaluation.py \ --model_name_or_path voidism/diffcse-roberta-base-trans \ --pooler cls_before_pooler \ --task_set transfer \ --mode test ``` For more detailed information, please check [SimCSE's GitHub repo](https://github.com/princeton-nlp/SimCSE). | c56a7100b4f6640c2696891e6381f72d |
apache-2.0 | [] | false | Pretrained models [](https://huggingface.co/voidism) * DiffCSE-BERT-base (STS): https://huggingface.co/voidism/diffcse-bert-base-uncased-sts * DiffCSE-BERT-base (transfer tasks): https://huggingface.co/voidism/diffcse-bert-base-uncased-tr... | 2e7897040172b216b699df80b435a0ef |
apache-2.0 | [] | false | getting-started) for more information. ```python from diffcse import DiffCSE model_bert_sts = DiffCSE("voidism/diffcse-bert-base-uncased-sts") model_bert_trans = DiffCSE("voidism/diffcse-bert-base-uncased-trans") model_roberta_sts = DiffCSE("voidism/diffcse-roberta-base-sts") model_roberta_trans = DiffCSE("voidism/dif... | 4947cef7be8807187158bbaf5b51a7ce |
apache-2.0 | [] | false | Citations [](https://doi.org/10.48550/arXiv.2204.10298) Please cite our paper and the SimCSE paper if they are helpful to your work! ```bibtex @inproceedings{chuang2022diffcse, title={{DiffCSE}: Difference-based Con... | 227480c0db232bd868d01cd590bd3e09 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ka', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-georgian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KA dataset. It achieves the following results on the evaluation set: - Loss: 0.3666 - Wer: 0.4211 | 280abddf8750c46fcfb94054c58edd24 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ka', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precisio... | fc51ab4bcfb38ec289e5f9a47f684700 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'ka', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.8805 | 5.95 | 500 | 0.7547 | 0.8438 | | 1.2123 | 11.9 | 1000 | 0.4732 | 0.6542 | | 1.0822 | 17.86 | 1500 | 0.4027 | 0.5778 | |... | 37e212c2e197ada177d8e3db320856ee |
mit | [] | false | Carol on Stable Diffusion This is the `<carol>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train you... | a29838069ac609b1c91555c6075fcd68 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2244 - Accuracy: 0.923 - F1: 0.9233 | 2697c756fb6bf63e192bd581d990610e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8412 | 1.0 | 250 | 0.3186 | 0.904 | 0.9022 | | 0.2501 | 2.0 | 500 | 0.2244 | 0.923 | 0.9233 | | 89ca0f2c1187be1611ddbad4292d2929 |
mit | ['PyLaia', 'PyTorch', 'Handwritten text recognition'] | false | Model description The model has been trained using the PyLaia library on the [NorHand](https://zenodo.org/record/6542056) document images. Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio. | 5af2a4efbcf3b4361369c2c70e6d75bf |
mit | ['PyLaia', 'PyTorch', 'Handwritten text recognition'] | false | Evaluation results The model achieves the following results: | set | CER (%) | WER (%) | | ----- | ---------- | --------- | | train | 2.17 | 7.65 | | val | 8.78 | 24.93 | | test | 7.94 | 24.04 | Results improve on validation and test sets when PyLaia is combined with a 6-gr... | 7e89bdef8025af2c43959606e4bb70e3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-16-6 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8331 - Accuracy: 0.625 | 7c696ce090cd0a7402db4388c1177d47 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0881 | 1.0 | 10 | 1.1248 | 0.1 | | 1.0586 | 2.0 | 20 | 1.1162 | 0.2 | | 0.9834 | 3.0 | 30 | 1.1199 | 0.... | ad9406d4e77e4276681e9c0cb6855e0a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.2424 | 6567728f528fb84e8e5981f864d1e90f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4921 | 1.0 | 479 | 2.3047 | | 2.3893 | 2.0 | 958 | 2.2607 | | 2.3571 | 3.0 | 1437 | 2.2481 | | 39aa949c7c660b1f59679e97f69af6c7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_wnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6894 - Accuracy: 0.5634 | 93e882186d9d480f94bdf73122a7b1e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6929 | 1.0 | 3 | 0.6908 | 0.5634 | | 0.6926 | 2.0 | 6 | 0.6914 | 0.5634 | | 0.6934 | 3.0 | 9 | 0.6912 | 0.... | ae713603828e4519f3198ee58357090f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_ner_conll2003 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0524 - Precision: 0.9358 - Recall: 0.9438 - F1: 0.9398 - Accuracy: 0.9877 | 53f418163830640d15b8cb1a1d7984ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1897 | 1.0 | 878 | 0.0544 | 0.9223 | 0.9270 | 0.9246 | 0.9848 | | 0.0363 | 2.0 |... | 6c54d1cebddcd2dc1df46039da86c13e |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 0b55b48db98d346b8911b9117bf32b47 |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-NL 16B) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data. | 34aa6e94b5bdfc8536263b970702bd20 |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-16B-nl") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-nl") text = "d... | 5d1276cbffc1ee0a478630aa480685a7 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2512 - Accuracy: 0.904 - F1: 0.9048 | b921136b9bff8629ee039b45a2e9c648 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the toydata dataset. It achieves the following results on the evaluation set: - Loss: 0.1233 - Precision: 0.8373 - Recall: 0.8722 - F1: 0.8544 - Accuracy: 0.9640 | 8ca549b34f4cd4d69ed772b220bcc38d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 408 | 0.1435 | 0.7577 | 0.8557 | 0.8038 | 0.9526 | | 0.1984 | 2.0 |... | 49b8926ad5663e5280593e372596234f |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2643 - F1: 0.8417 | 1b7519f98188ff52769b04911a91d5e4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5689 | 1.0 | 191 | 0.3469 | 0.7746 | | 0.2605 | 2.0 | 382 | 0.2802 | 0.8362 | | 0.1695 | 3.0 | 573 | 0.2643 | 0.8417 | ... | 381884af2ff2c4488945aaa423ef375d |
mit | ['generated_from_trainer'] | false | xlnet-base-cased-mrpc This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.7156 - Accuracy: 0.8456 - F1: 0.8897 - Combined Score: 0.8676 | f1abc7dfc3e94cd748a551017b3f0f2c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | ee56878adda39e6f7f805130408ebf9a |
apache-2.0 | ['generated_from_trainer'] | false | t5-opus_infopankki-en-zh This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_infopankki dataset. It achieves the following results on the evaluation set: - Loss: 2.3548 | 7b1bac977156abea1cd24d2378a44b15 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.065 | 1.0 | 1496 | 2.7383 | | 2.8459 | 2.0 | 2992 | 2.6077 | | 2.7296 | 3.0 | 4488 | 2.5336 | | 2.6639 | 4.0 | 5984 | 2.4761 ... | e7ce2e3f779c6611e20b1c85bcefdca4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Hindi - Shripad Bhat This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3909 - Wer: 21.4519 | a9d2d8244a62d584d6e4ecaddf981711 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4337 | 0.73 | 100 | 0.4874 | 47.5868 | | 0.1894 | 1.47 | 200 | 0.3264 | 23.9482 | | 0.1007 | 2.21 | 300 | 0.3101 | 22.526... | 419c548c17713810fa88b9f7cc7bc090 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Ontocord/Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4).
When using this model, make su... | 3cc0d712d52b74354b8c1f51b6cf6071 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage
The model can be used directly (without a language model) as follows:
```
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "vi", split="test[:2%]")
processor = Wav2Vec2Processor.f... | b01b8348cd2807b120e0d5b77410f572 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech... | 59441f4ca25284f44520f791138c3ae0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
```
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "vi", split="tes... | d8bc628961ce2f6adf90d314620906b8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_datas... | 0418d1f69fbcc97617961909c184f1e4 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | you may also want to use the decode_string from https://huggingface.co/Nhut/wav2vec2-large-xlsr-vietnamese
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=i... | 37a781a6ad7358f3d97c60a9dda83cf4 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mt5-base-jaquad-qag` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm... | 889b989c246c0423bbdbb342d75ac0db |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** ja - **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417... | 4d45674b57c06b6f5f18c284fb1d0b86 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-jaquad-qag") output ... | f65289ab32cae07dfd2616455e60e352 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-jaquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_jaquad.default.json) | | Score | Type | Dataset ... | 8f7c47da97bf3c0a2cda95a0c619c068 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_jaquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 256 - epoch: 18 ... | 929edab8380d20d16a2d58623fbdef01 |
apache-2.0 | ['translation'] | false | opus-mt-eu-en * source languages: eu * target languages: en * OPUS readme: [eu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/eu-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | ee0055506512298bc1245a42c4fc9ed7 |
mit | ['generated_from_trainer'] | false | punctuation-taboa-bert This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the tapaco dataset. It achieves the following results on the evaluation set: - Loss: 0.0181 - Precision: 0.9850 - Recall: 0.9836 - F1: 0.9843 - Accura... | be1f0e3defff49e6af0adf23633f2bad |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0272 | 1.0 | 17438 | 0.0181 | 0.9850 | 0.9836 | 0.9843 | 0.9946 | | 0.0234 | 2.0 ... | fbbd13a0fb885795a27e1fd8dcb29af7 |
apache-2.0 | ['translation'] | false | eng-cpp * source group: English * target group: Creoles and pidgins, Portuguese-based * OPUS readme: [eng-cpp](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cpp/README.md) * model: transformer * source language(s): eng * target language(s): ind max_Latn min pap tmw_Latn zlm_Latn zsm_La... | 38e1ab17ff8ef30c7d8519c84416fa3e |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-msa.eng.msa | 32.6 | 0.573 | | Tatoeba-test.eng.multi | 32.7 | 0.574 | | Tatoeba-test.eng-pap.eng.pap | 42.5 | 0.633 | | 650e56d926683d5699718b21b6a53127 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-cpp - source_languages: eng - target_languages: cpp - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cpp/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'id', 'cpp'] - src_constituents: {'eng'} - tg... | 1d12b84c84788761c94a640cb5601ad8 |
openrail | [] | false | Parameters ``` model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b5", num_labels=2, id2label=id2label, label2id=label2id, ) ... | c622affd68ffc35daabd1fc18b018204 |
mit | ['classification', 'similarity'] | false | Aspect-based Document Similarity for Research Papers A `scibert-scivocab-uncased` model fine-tuned on the CORD-19 corpus as in [Aspect-based Document Similarity for Research Papers](https://arxiv.org/abs/2010.06395). <img src="https://raw.githubusercontent.com/malteos/aspect-document-similarity/master/docrel.png"> ... | 0cf053674bb080ed965d2778f6a05495 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5442 - Wer: 0.3327 | bde22713417ea9ccfb00157fc992b7f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.558 | 1.0 | 500 | 1.9825 | 0.9952 | | 0.8674 | 2.01 | 1000 | 0.5186 | 0.5141 | | 0.4291 | 3.01 | 1500 | 0.4576 | 0.459... | 1865f9364234eab960a247ec94f265f7 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings [Facebook's Wav2Vec2 Conformer (TODO-add link)]() Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make s... | 7b36556209b525ae2348636a7f92117e |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC from datasets import load_dataset import torch | 8eb7264e80456e4e054b9c5e822e863c |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft") model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-100h-ft") | 7f04f9ec1fa56c67f8b1c63e6577715f |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3863 - Wer: 0.3095 | 7e1dd7fb5c21448277830a8c8ae1c514 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8284 | 3.67 | 400 | 0.6782 | 0.6739 | | 0.4174 | 7.34 | 800 | 0.4524 | 0.4811 | | 0.2015 | 11.01 | 1200 | 0.4736 | 0.4311 | |... | 5254b859bf2b9269c071325d48f828b6 |
mit | [] | false | max-twain on Stable Diffusion This is the `<max-twain>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also t... | c8d2e2e7a2a5dcc741e3ee5199f96bed |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2991 - Accuracy: 0.9025 | a1dda29c68d412bc0db38372f09cd548 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.227 | 1.0 | 4210 | 0.2991 | 0.9025 | | 57b0d51722e1582c3b4406805205f705 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-bert-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8235 - F1: 0.8792 | 8b85cbfee6b18ba28d88f2e4a71f3b16 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5382 | 1.0 | 230 | 0.4008 | 0.8456 | 0.8893 | | 0.3208 | 2.0 | 460 | 0.4182 | 0.8309 | 0.8844 | | 0.1587 |... | fc245a9744e5a1d5361fed00a72bf042 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_wnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3455 - Accuracy: 0.5634 | 18fdef641cb5226693fc53200fe6c6e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3469 | 1.0 | 5 | 0.3456 | 0.5634 | | 0.3467 | 2.0 | 10 | 0.3458 | 0.5634 | | 0.3466 | 3.0 | 15 | 0.3459 | 0.... | 2ccd45416c6e3c9f426097c6089df691 |
apache-2.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | distilroberta-finetuned-financial-text-classification This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 ... | ab88ca2a432eb045dbe0f50d74bc084a |
apache-2.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | Model description Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models ... | 93fea3436b332b07e8696bd5b08c77ea |
apache-2.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7309 | 1.0 | 72 | 0.3671 | 0.8441 | | 0.3757 | 2.0 | 144 | 0.3199 | 0.8709 | | 0.3054 | 3.0 | 216 | 0.3096 | 0.8678 | |... | 1ef1ebf9edef4021044876e2912e1764 |
apache-2.0 | ['ner', 'chemical', 'bionlp', 'bc4cdr', 'bioinfomatics'] | false | NER to find Gene & Gene products > The model was trained on bionlp and bc4cdr dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed) All the labels, the possible token classes. ```json {"label2id": { "O": 0, "Chemical": 1, } } ``` Notice, we removed the 'B-','I-' etc fro... | 5eccaa36ada37dbb229ed06cc7622bc1 |
apache-2.0 | ['ner', 'chemical', 'bionlp', 'bc4cdr', 'bioinfomatics'] | false | This is the template we suggest for using the model Of course I'm well aware of the ```aggregation_strategy``` arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way ... | 45f7c067d5f91e6e3b013b47f2a898ff |
apache-2.0 | ['generated_from_trainer'] | false | data2vec-large-uk This model is a fine-tuned version of [facebook/data2vec-audio-large-960h](https://huggingface.co/facebook/data2vec-audio-large-960h) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.3472 - eval_wer: 0.3410 - eval_cer: 0.0832 - eval_runtime: 231.00... | a5f8ae4caddb2bd0b9521afe50fb51a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | bd34a4a30cf45a2fb002537ccccacb1b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated as follows on the German test data of Common Voice. ```python import torchaudio.functional as F import torch from transformers import AutoModelForCTC, AutoProcessor import re from datasets import load_dataset, load_metric CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；"... | 4ed7a2164778cbe00c610209dabf2756 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | ds = ds.select(range(100)) def calculate_metrics(batch): global counter, wer_counter, cer_counter resampled_audio = F.resample(torch.tensor(batch["audio"]["array"]), 48_000, 16_000).numpy() input_values = processor(resampled_audio, return_tensors="pt", sampling_rate=16_000).input_values ... | 9770231c07a90713e63fd6cb31d66739 |
mit | ['generated_from_trainer'] | false | deberta-v3-base__sst2__all-train This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6964 - Accuracy: 0.49 | e3c1e41c4f9fe57721b2a93d90c7ebee |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.6964 | 0.49 | | No log | 2.0 | 14 | 0.7010 | 0.49 | | No log | 3.0 | 21 | 0.7031 | 0.... | 8437daa58804bfbb3bb1a63e38c7c3e8 |
apache-2.0 | ['vision', 'image-classification'] | false | densenet121-res224-mimic_ch A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from ... | 524659865a774725d95a7fbd4437f9c8 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of xray: Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs ... | f795a32a2216c411418d59a13a00453d |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the vioeva concept trained by Antiraedus on the Antiraedus/Violet-Evergarden dataset. I FORGOT IT WAS GRAYSCALED This is a Stable Diffusion model fine-tuned on the vioeva concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of a girl in vioeva style** This model ... | aad56028489c74b80421f96cde8488b6 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Description Violet Evergarden!!!! Dataset [is located here](https://huggingface.co/datasets/Antiraedus/Violet-Evergarden) This is a Stable Diffusion model fine-tuned on `style` images for the wildcard theme. | d0bae5488a56932d8b971faea86d7b87 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | effeffIX Concept Diffusion Fine-tuned Stable Diffusion model, based of ```F222```, trained with concept art from a high quality role playing game.  | a18849f85bcb0569227f33ecc732f44c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | Model Usage This model was trained on multiple concepts. Use the tokens below: | Token | Description | |----------------------|------------------------------------------------------| | effeff9 woman | Uses concepts trained on female designs. ... | 01332eb82d982e3f67d68c1ccd1f3b62 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | Examples: effeff9 architecture  --- ☕ If you enjoy this model, buy us a coffee [](https://ko-fi.com/3eegames) --- | 3090bcdda8d09e9f0302e9881d7c3d06 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | 🧾 Prompt examples: **The amazing Aubrey Plaza** ```Wide shot of a effeff9 woman warrior aubrey plaza with shining armor descending from heaven, lifelike, (highly detailed eyes), super highly detailed face, professional digital painting, artstation, concept art, Unreal Engine 5, HD quality, 8k resolution, beautiful,... | 4f63e19dd21402cf2524019b92cd7f66 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | ❎-negative-prompt-template) _Steps: 82, Sampler: DPM++ 2M, CFG scale: 8.5, Seed: 695884347, Size: 512x512, Model hash: b7ba5b22_ --- **The Wise Giraffe** ```portrait of a effeff9 creature Giraffe, artstation, concept art, Unreal Engine 5, HD quality, 4k resolution, beautiful, cinematic, art by artgerm and greg rutk... | d45332f0be820030e4b0e5cbb5c9a473 |
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