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 | ['bert', 'biomedical'] | false | CODER: Knowledge infused cross-lingual medical term embedding for term normalization. English Version. ``` @article{YUAN2022103983, title = {CODER: Knowledge-infused cross-lingual medical term embedding for term normalization}, journal = {Journal of Biomedical Informatics}, pages = {103983}, year = {2022}, issn = {153... | d5688d359ab202c927bd53ec7dc13946 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Introduction ptt5-base-msmarco-pt-100k-v1 is a T5-based model pretrained in the BrWac corpus, finetuned on Portuguese translated version of MS MARCO passage dataset. In the version v1, the Portuguese dataset was translated using [Helsinki](https://huggingface.co/Helsinki-NLP) NMT model. This model was finetuned for 10... | 36a8142c237c380eab9bcaf032f58201 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'unicamp-dl/ptt5-base-msmarco-pt-100k-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) ``` | 1853d4401d7d5299a3940dfff1798192 |
mit | ['msmarco', 't5', 'pytorch', 'tensorflow', 'pt', 'pt-br'] | false | Citation If you use ptt5-base-msmarco-pt-100k-v1, please cite: @misc{bonifacio2021mmarco, title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto L... | f13d00f2ac46cbeb64d8d867ae45c82e |
mit | [] | false | Ruan Jia on Stable Diffusion This is the `<ruan-jia>` 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 tra... | a75ad66c8f4fbd118444b0a4b522c958 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'aiart'] | false | I decide to stop creating a separate repository for each model, so most of the future models will go here. I will only create repository for more important project. Despite the name YuriDiffusion, I am not sure whether I will really train such a model. Dataset collection and the hard limit of SD both make this task ve... | d630cb1ca1831622bfbed44e4c964d89 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'aiart'] | false | List of models -[suremio-nozomizo-eilanya-maplesally](https://huggingface.co/alea31415/YuriDiffusion/blob/main/suremio-nozomizo-eilanya-maplesally/README.md)  ... | b48a41be4d2e84e6623fdac85d9a4717 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'aiart'] | false | Questions that I have partial answer to **Can we make an image of multiple known characters** This is possible either through native training, lora, or merging. **Can we use embedding, lora, and native training together?** Interestingly, independent trained lora and embedding, or indpendent fine-tuning and embedd... | 20da92d51018003cd8a522f4333f90c3 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'aiart'] | false | Questions that I do not have answer to **Native training or LoRA?** I tried both for [suremio-nozomizo-eilanya-maplesally](https://huggingface.co/alea31415/YuriDiffusion/blob/main/suremio-nozomizo-eilanya-maplesally/README.md) and [grass wonder from umamusume](https://huggingface.co/alea31415/YuriDiffusion/blob/main... | 4e5433c399bc21ce18f9b7729af971d9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4798 - Wer: 0.3474 | 9764f2903b2afd95109c737e1ecde522 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5229 | 4.0 | 500 | 1.6557 | 1.0422 | | 0.6618 | 8.0 | 1000 | 0.4420 | 0.4469 | | 0.2211 | 12.0 | 1500 | 0.4705 | 0.4002 | |... | 2640468e82210826e5fc107471893aeb |
afl-3.0 | [] | false | afro-xlmr-mini AfroXLMR-mini was created by MLM adaptation of [XLM-R-miniLM](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, ... | 822db6392a1b76fdb946f3af095c74ca |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'NbAiLab/NPSC', 'robust-speech-event', False, 'nb-NO', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-1b-npsc This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the [NbAiLab/NPSC (16K_mp3_bokmaal)](https://huggingface.co/datasets/NbAiLab/NPSC/viewer/16K_mp3_bokmaal/train) dataset. It achieves the following results on the evaluation s... | 2e80944205e0ba12aa6bac53859841a4 |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'NbAiLab/NPSC', 'robust-speech-event', False, 'nb-NO', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.8361 | 0.32 | 500 | 0.6304 | 0.4970 | | 0.5703 | 0.64 | 1000 | 0.3195 | 0.2775 | | 0.5451 | 0.97 | 1500 | 0.2700 | 0.224... | 5164b06d8a16d47414bb93bcc91b732d |
other | [] | false | Test Model https://huggingface.co/syaimu/7th_test <img src="https://i.imgur.com/0xKIUvL.jpg" width="1700" height=""> <img src="https://i.imgur.com/lFZAYVv.jpg" width="1700" height=""> <img src="https://i.imgur.com/4IYqlYq.jpg" width="1700" height=""> <img src="https://i.imgur.com/v2pn57R.jpg" width="1700" height=... | 963e977b5978956c72b7967d9fdd4487 |
other | [] | false | other <img src="https://i.imgur.com/oCZyzdA.jpg" width="1700" height=""> <img src="https://i.imgur.com/sAw842D.jpg" width="1700" height=""> <img src="https://i.imgur.com/lzuYVh0.jpg" width="1700" height=""> <img src="https://i.imgur.com/dOXsoeg.jpg" width="1700" height=""> | 80b6bff0bfa83e3b4b28eba24a13a187 |
mit | [] | false | bozo 22 on Stable Diffusion This is the `<bozo-22>` 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... | b7c08ab6c51d3037580abc00d21334ae |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | How to use First, install sentencepiece. We have confirmed behavior with the latest version August 2022. (Skip if not necessary.) ``` shell pip install sentencepiece ``` When using pipeline for text generation. ``` python from transformers import pipeline generator = pipeline("text-generation", model="abeja/gpt2-... | 9648211d7a5c406bb565f149097e6ed3 |
mit | ['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp'] | false | Dataset The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz), [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch), and [Japanese OSCAR](https://huggingface.co/datasets/oscar). | 47428f467c6e3ef447039ec9b509641f |
apache-2.0 | ['translation'] | false | opus-mt-he-de * source languages: he * target languages: de * OPUS readme: [he-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/he-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://... | 66e3993c06d11bf99382c95f9a072a81 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | saqib_v2 Dreambooth model trained by imjunaidafzal with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | 5c14d2f70d9a852047a6745eebec10b8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6554 - Accuracy: 0.6050 | e41f5756c9e8360d391c6da55ca813d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6802 | 1.0 | 410 | 0.6614 | 0.5988 | | 0.6514 | 2.0 | 820 | 0.6554 | 0.6050 | | 0.6306 | 3.0 | 1230 | 0.6610 | 0.... | 5ab4eab15b12af0811399ebe0ea0616a |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/t5-base-squad-qag` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation... | 2f23b371b09a862315747261de69b26c |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-gener... | d407f9f97fa266adb4aa2cf64e5bcf2e |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-qag") output = pipe("generate question and a... | 6fe3a2944bee008c99a3bd8aad9721fb |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset ... | 78f74183e70f6bfb25c133760f76070f |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: ['qag'] - model: t5-base - max_length: 512 - max_length_output: 256 - epoch: 17 - bat... | 73f85b765a5277bfe6b692e1d78f411a |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Old_Church_Slavonic (cu) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our ... | 07b49196267c559e6e809c2fd1794b87 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-ia 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: 0.1452 - Wer: 0.1253 | 15f015b30ec0cdd90ff638c9364db5a4 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training and evaluation data Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua) Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_ia.ipynb" Test WER without LM wer = 20.1776 % cer = 4.7205 % ... | c5839bba3829a43d50451c1701deb280 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 4 - 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_sche... | a68702b733b71433c515a2f745c1599c |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'robust-speech-event', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.432 | 1.87 | 400 | 2.9636 | 1.0 | | 2.6922 | 3.74 | 800 | 2.2111 | 0.9977 | | 1.2581 | 5.61 | 1200 | 0.4864 | 0.4028 | |... | 323960b55604271df88e95dcd52c8d30 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-code-mixed-DS 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: 1.0408 - Accuracy: 0.7324 - Precision: 0.6883 - Recall: 0.6822 - F1: 0.6833 | 29d7abfbd215c18b0dc7ba2f4f3c4b45 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.2766380106570283e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9c070858f385fde237e8d2b509823220 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9207 | 1.0 | 497 | 0.7810 | 0.6016 | 0.5878 | 0.5953 | 0.5264 | | 0.7519 | 2.0 |... | 08aa6325354a4e0138dc152db48e8902 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.3932 - F1 Score: 0.6774 | 5ba28ce87cbb4bebb81a363364bfffc5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0236 | 1.0 | 50 | 0.5462 | 0.5109 | | 0.5047 | 2.0 | 100 | 0.4387 | 0.6370 | | 0.3716 | 3.0 | 150 | 0.3932 | 0.... | cf3e76b21a095e41598f2ae1d1966271 |
apache-2.0 | [] | false | Features - Can understand and respond to user input in natural language - Can perform a variety of tasks such as answering questions, providing information and executing commands - Has been trained on data from a specific Discord server and is familiar with the language and context used on that server | a0586568847a38acf0e188dc610fd644 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-1B-common_voice7-lt-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.5101 - Wer: 1.0 | 6867db91cd85ddbdd06752df63214bfa |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 36 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 23d7305c5e6b53971f3e7d5b81fdb3d2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 2.3491 | 31.24 | 500 | 3.9827 | 1.0 | | 0.0421 | 62.48 | 1000 | 2.9544 | 1.0 | | 0.0163 | 93.73 | 1500 | 2.5101 | 1.0 | | 2ac7310bf39ca2502aafaa0c56b7da9c |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_200k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 200k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 67e775575b435018b3db1b04cc4dea0d |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_200k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_200k') model = TFBertModel.from_pretrained("google/multibe... | 319b8dcc59065d047009ddc0af5e1a7a |
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: 0.2208 - Accuracy: 0.9215 - F1: 0.9217 | e37be3f94aeeeec38cfa136b4378ffa8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8381 | 1.0 | 250 | 0.3167 | 0.8995 | 0.8960 | | 0.2493 | 2.0 | 500 | 0.2208 | 0.9215 | 0.9217 | | 4ca7b21c36574263b24d2eb3d2d8fa88 |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned Bert for Italian
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: Italian (it)
- model type... | e3cc49a00e4baccdcdebf921ea4d0ada |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-it-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-it-bert")
````
| 9e6f7d6280ceda2fcefc2f781251340a |
apache-2.0 | ['generated_from_keras_callback'] | false | tmpny35efxx This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1996 - Train Accuracy: 0.9348 - Validation Loss: 0.8523 - Validation Accuracy: 0.7633 - Epoch: 1 | f05535285e659f83870f71f9fbd1cd94 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.5865 | 0.7626 | 0.5505 | 0.8010 | 0 | | 0.1996 | 0.9348 | 0.8523 | 0.7633 ... | eb0cd96fc73891369fe33d4d036213dc |
apache-2.0 | ['dighum'] | false | Description This is a fine-tuned NER model for early-modern Dutch United East India Company (VOC) letters based on XLM-R_base [(Conneau et al., 2020)](https://aclanthology.org/2020.acl-main.747/). The model identifies *locations*, *persons*, *organisations*, but also *ships* as well as derived forms of locations and r... | 3e6ea344887609ea3c71db58cf9640aa |
apache-2.0 | ['dighum'] | false | Intended uses and limitations This model was fine-tuned (trained, validated and tested) on a single source of data, the General Letters (Generale Missiven). These letters span a large variety of Dutch, as they cover the largest part of the 17th and 18th centuries, and have been extended with editorial notes between 1... | fad17574e31057ea7952fddf5c4e0629 |
apache-2.0 | ['dighum'] | false | How to use The model can run on raw text through the *token-classification* pipeline: ``` from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("CLTL/gm-ner-xlmrbase") model = AutoModelForTokenClassification.from_pretrained... | 5889827e68a02a44b2fb4b119b7b5e9b |
apache-2.0 | ['dighum'] | false | Training data and tagset The model was fine-tuned on the General Letters [GM-NER](https://github.com/cltl/voc-missives/tree/master/data/ner/datasplit_all_standard) dataset, with the following tagset: | tag | description | notes | | --- | ----------- | ----- | | LOC | locations | | | LOCderiv | derived forms of loc... | e18446d009e1360fee20f98443025120 |
apache-2.0 | ['dighum'] | false | Training procedure The model was fine-tuned with [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), using [this script](https://github.com/huggingface/transformers/blob/master/examples/legacy/token-classification/run_ner.py). Non-default training parameters are: * training batch size: 16 * max sequence len... | 27bbdfe79bb0fa2357f1f5ebc5c05cb4 |
apache-2.0 | ['dighum'] | false | Reference The model and fine-tuning data presented here were developed as part of: ```bibtex @inproceedings{arnoult-etal-2021-batavia, title = "Batavia asked for advice. Pretrained language models for Named Entity Recognition in historical texts.", author = "Arnoult, Sophie I. and Petram, Lodewijk and ... | b4972e214e5c7a726e717888a9bafd5d |
gpl | ['corenlp'] | false | Core NLP model for sp CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, q... | c873d494402373d671f8fe0518873e12 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased_title_fine_tuned This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3368 - Accuracy: {'accuracy': 0.8810840405146455} - Recall: {'recall': 0.8611674554879423} - ... | 515f2cdb2c3a51ba8994ffc643fe2513 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 | ad334e90bac026caa7debb6bcff05af1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|:-----------------------------... | 95747b46a08910f648fb01240b4ce35b |
apache-2.0 | [] | false | Results on Natural Questions - Test Set |Id | link | Exact Match | |---|---|---| |**T5-large**|**https://huggingface.co/google/t5-large-ssm-nqo**|**29.0**| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-nqo|35.2| |T5-3b|https://huggingface.co/google/t5-3b-ssm-nqo|31.7| |T5-11b|https://huggingface.co/google/t5-11b-... | f2298aba381e4c542b8e91f626f04af9 |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-large-ssm-nqo") t5_tok = AutoTokenizer.from_pretrained("google/t5-large-ssm-nqo") input_ids = t5_tok(... | 7c1d021bb01f07e190496b6d3b152513 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-English Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-... | 430e61211327ac07e76a5878bae4e2e9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english") audio_paths = ["/... | 8b470aa6cd8d5b98eb7611cc248ef8f3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | e04bcbd048ebc2f039812fdf7afe3ce3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset mozilla-foundation/common_voice_6_0 --config en --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python... | bba8f38b28346fd2f541e5855d407364 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021wav2vec2-large-xlsr-53-english, title={XLSR Wav2Vec2 English by Jonatas Grosman}, author={Grosman, Jonatas}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav... | 583748c83961c5c088180f1ea8b5e79f |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/t5-large-squad-ae` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | f5a14937f88d5a04229e4d314ce6eb3b |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-ae") output = pipe("extract answers: <hl> Beyonce further ... | ab127d21608194a27c29863814d9550a |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------... | e882c4497d53d8a2be74f8e2648577ea |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: ['ae'] - model: t5-large - max_length: 512 - max_length_output: 32 - epoch: 9 - batch: 4... | d82cbc5aae9b4177ea82560c67e4d26b |
apache-2.0 | ['generated_from_trainer'] | false | bertiny-finetuned-finer-full This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the 10% of finer-139 dataset for 40 epochs according to paper. It achieves the following results on the evaluation set: - Loss: 0.0788 - Precision: 0.5554... | 391e6487938486db695ddb7fae5b81a4 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 40 | e435490b947ae319c35d7238408927a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0852 | 1.0 | 11255 | 0.0929 | 1.0 | 0.0001 | 0.0002 | 0.9843 | | 0.08 | 2... | addd563b0b70e6f643e9b012d7dcff70 |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | SMALL-100 Model SMaLL-100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in [this paper](https://arxiv.org/abs/2210.11621)(accepted to EMNLP2022), and ini... | cfc2d97fea17fc5e1cd9dda71338deac |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | forward pass ``` Training data can be provided upon request. - **Generation** Beam size of 5, and maximum target length of 256 is used for the generation. ``` from transformers import M2M100ForConditionalGeneration from tokenization_small100 import SMALL100Tokenizer hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" chi... | 5ca4ffa2fbf96df371329d5292dc937a |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | translate Hindi to French tokenizer.tgt_lang = "fr" encoded_hi = tokenizer(hi_text, return_tensors="pt") generated_tokens = model.generate(**encoded_hi) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | 76aba4b34ba8c6ccef9320e44e6c6648 |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | translate Chinese to English tokenizer.tgt_lang = "en" encoded_zh = tokenizer(chinese_text, return_tensors="pt") generated_tokens = model.generate(**encoded_zh) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | ae3d32354acc34b97631f6a4854713d6 |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | => "Life is like a box of chocolate." ``` - **Evaluation** Please refer to [original repository](https://github.com/alirezamshi/small100) for spBLEU computation. - **Languages Covered** Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Benga... | e74be3ad1b0e899d6a7f1f3cede2bedd |
mit | ['small100', 'translation', 'flores101', 'gsarti/flores_101', 'tico19', 'gmnlp/tico19', 'tatoeba'] | false | Citation If you use this model for your research, please cite the following work: ``` @inproceedings{mohammadshahi-etal-2022-small, title = "{SM}a{LL}-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages", author = "Mohammadshahi, Alireza and Nikoulina, Vassilina ... | 77f5822ce3712488b3db17d2703822d3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-truthful This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4660 - Accuracy: 0.87 - F1: 0.8697 | 27d29e1c959dec6497b06106f66c72cd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.910294163459086e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 | 6d48c2c71a0fe96cd4629ba8c93e396c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 5 | 0.6509 | 0.59 | 0.5780 | | No log | 2.0 | 10 | 0.4950 | 0.77 | 0.7701 | | No log |... | 675a17c7a1ebff69a610056111896061 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-mt" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors=... | 60205e7da60393bd25420cca722eb843 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers accelerate from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-mt" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto") inputs = tokenizer.encode("... | 8109b4e773f812d182ce10fb23cb9976 |
apache-2.0 | ['text2text-generation'] | false | pip install -q transformers accelerate bitsandbytes from transformers import AutoModelForSeq2SeqLM, AutoTokenizer checkpoint = "bigscience/mt0-xxl-mt" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True) inputs = tokeni... | 2f7e32dc9a5dfcd3dcacd8752491f962 |
apache-2.0 | [] | false | 简介 Brief Introduction 本模型基于大规模信息抽取数据进行预训练，可支持few-shot、zero-shot场景下的实体识别、关系三元组抽取任务。 This model is pre-trained on large-scale information extraction data, to better support Named Entity Recognition (NER) and Relation Extraction (RE) tasks in few-shot/zero-shot scenarios. | 789691d4bea0c4295a8c23dc77bdafeb |
apache-2.0 | [] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | ---------- | ---------- | -------------- | -------- | ------------ | -------- | | 通用 General | 信息抽取 Information Extraction | 二郎神 Erlangshen | BagualuIEModel | 120M | Chinese | | 96d6e9255e13dfc704bd481a418d8e6c |
apache-2.0 | [] | false | 下游效果 Performance Erlangshen-BERT-120M-IE-Chinese在多个信息抽取任务下进行测试。 其中，zh_weibo/MSRA/OntoNote4/Resume为NER任务，其中MSRA在原始数据下进行测试；SanWen/FinRE作为实体关系联合抽取任务进行测试，非单一关系分类任务。 部分参数设置如下： ``` batch_size=16 precision=16 max_epoch=50 lr=2e-5 weight_decay=0.1 warmup=0.06 max_length=512 ``` 我们分别在随机种子123/456/789下进行测试，并以[MacBERT-base, C... | 3a3bc17f6717f094d9af7468576d3fea |
apache-2.0 | [] | false | 使用 Usage GTS引擎（GTS-Engine）是一款开箱即用且性能强大的自然语言理解引擎，能够仅用小样本就能自动化生产NLP模型。GTS Engine包含两个训练引擎：乾坤鼎和八卦炉。 本模型为可在GTS-Engine八卦炉引擎信息抽取任务中，作为预训练模型进行finetune。 GTS-Engine文档参考：[GTS-Engine](https://gts-engine-doc.readthedocs.io/en/latest/docs/about.html) | c21c739afe74b5eb7f3d485ba0808f52 |
apache-2.0 | [] | false | 引用 如果您在您的工作中使用了我们的模型，可以引用我们的[网站](https://github.com/IDEA-CCNL/GTS-Engine): You can also cite our website: ``` @misc{GTS-Engine, title={GTS-Engine}, author={IDEA-CCNL}, year={2022}, howpublished={\url{https://github.com/IDEA-CCNL/GTS-Engine}}, } ``` | b6dc43612b1ebbfa323db001bf667243 |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |---------|-------------|-------------|-------|------------|--------------|-----... | a01ff3b6c62dfe8a79c0efa4daaf8a44 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Counterfeit is anime style Stable Diffusion model. e.g. prompt: solo,(a girl),cowboy shot,((masterpiece)),((best quality)),((an extremely detailed)) negative: low quality, lowres, worst quality, normal quality,(bad anatomy) Steps: 28 Sampler: Euler a CFG scale: 12 Size: 640x960  on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.9699 | 2620b66e76db64bebd324fb3e9c8e57b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 7 | 5.5290 | | No log | 2.0 | 14 | 5.1385 | | No log | 3.0 | 21 | 4.9699 | | 22253a7ecca3bb92387bde94a13827e7 |
mit | ['generated_from_trainer'] | false | deberta-v3-base-lm-all This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0354 | 5aaf096293fa200a7ce885207b955f5b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 16 | 9f5f77f1599fca48af8bc1253b5fddea |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3663 | 1.0 | 2388 | 2.2002 | | 2.1379 | 2.0 | 4776 | 1.8135 | | 1.8234 | 3.0 | 7164 | 1.6199 | | 1.623 | 4.0 | 9552 | 1.5031 ... | 6f310708ef7b4a087e54cbdb48e96493 |
cc | [] | false | A Textual Inversion Embedding for Stable Diffusion, trained on SD2.1 Version 2 of this embedding now allows you to achieve that loose digital paint style with much greater flexibility and fewer artifacts. I highly recommend it over V1. Simply call on the keyword laxpeintV2 (or laxpeint for the original) as in, sin... | 3f33e24f6b641080be0befd7f08081e3 |
apache-2.0 | ['generated-from-trainer'] | false | model This card is a copy of [this repo](https://huggingface.co/julien-c/reactiongif-roberta/tree/main) for the purpose of testing the repo card utilities in `huggingface_hub`. It achieves the following results on the evaluation set: - Loss: 2.9150 - Accuracy: 0.2662 | 40b395aca23d7c3fa0c0982835345073 |
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