Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
xet
 Community
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2
30
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513
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readme_section
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32
apache-2.0
['whisper-event', 'generated_from_trainer']
false
long-form-transcription) section. ```bash pip install git+https://github.com/huggingface/transformers --force-reinstall pip install torch ``` ```python >>> from transformers import pipeline >>> import torch >>> device = 0 if torch.cuda.is_available() else "cpu"
596fda66f27f7553e54573f7d74e7d74
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Long-form transcription To improve the performance of long-form transcription you can convert the HF model into a `whisper` model, and use the original paper's matching algorithm. To do this, you must install `whisper` and a set of tools developed by [@bayartsogt](https://huggingface.co/bayartsogt). ```bash pip instal...
f5c9cc19710dff7e8b8579671e0e9422
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters We used the following hyperparameters for training: - `learning_rate`: 1e-05 - `train_batch_size`: 32 - `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 - `training_steps`: 5000 - `m...
ed3a4f306044c4e1584eb57405d57b00
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0698 | 1.09 | 1000 | 0.1876 | 7.189 | | 0.0218 | 3.07 | 2000 | 0.2254 | 7.110 | | 0.0053 | 5.06 | 3000 | 0.2711 | 6.969 | | 0....
493bfd846b99a06c2bb754cd0c20ead6
creativeml-openrail-m
['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard']
false
DreamBooth model for the MLSEnglishGnome concept trained by gavrenkov on the gavrenkov/MLSGnome dataset. This is a Stable Diffusion model fine-tuned on the MLSEnglishGnome concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of MLSEnglishGnome gnome** This model was created as part o...
4a344391fb971b5b2f789be6ec18750a
creativeml-openrail-m
['text-to-image', 'stable-diffusion']
false
saqib_sarahkhan_t350-u4000-11-21-pm 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/g...
d4e431ce782458115b8a225f4e8c2591
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1
384e371edc218b6ed5dc0e8073f3071a
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 42 | 0.4092 | 0.5360 |
4e2ab0cd60c64c019edd72da28c600ab
apache-2.0
['generated_from_trainer']
false
Article_50v8_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.7555 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7786
6cec3fcfce3c5d4266bbb11338baa725
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 6 | 0.9789 | 0.1 | 0.0047 | 0.0089 | 0.7776 | | No log | 2.0 |...
cef96d8b5ab383127454b4a8a94b601e
apache-2.0
['translation']
false
nic-eng * source group: Niger-Kordofanian languages * target group: English * OPUS readme: [nic-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md) * model: transformer * source language(s): bam_Latn ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho...
0bc0642c95c9c7462b12d012a5f59b83
apache-2.0
['translation']
false
Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.bam-eng.bam.eng | 2.4 | 0.090 | | Tatoeba-test.ewe-eng.ewe.eng | 10.3 | 0.384 | | Tatoeba-test.ful-eng.ful.eng | 1.2 | 0.114 | | Tatoeba-test.ibo-eng.ibo.eng | 7.5 | 0.197 | | Tatoeba-test.kin-eng.kin...
ce6056f6a3886fc1847b13252f332763
apache-2.0
['translation']
false
System Info: - hf_name: nic-eng - source_languages: nic - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts',...
d11cebf758a67b480e8c558337665569
apache-2.0
['generated_from_trainer']
false
distilroberta-base-wiki-mark This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0062
c04091478331309482b3f9763b356640
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2841 | 1.0 | 1265 | 2.0553 | | 2.1536 | 2.0 | 2530 | 1.9840 | | 2.1067 | 3.0 | 3795 | 1.9731 |
a5b7ccfd06831c3643a1f2a7f2100640
apache-2.0
['generated_from_trainer']
false
openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7427 - Wer: 19.3902 - Cer: 8.7285
5d3e886ee6388bdfff14e9d8b562c7da
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch...
0b763d7b2264457d9288e6e3d2d95882
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:| | 0.0471 | 1.05 | 1000 | 0.5961 | 20.2895 | 8.9820 | | 0.0194 | 2.11 | 2000 | 1.0999 | 22.6146 | 9.7105 | | 0.002 | 4.0...
92f1c94bee5876f823235b35c2f2f1a7
mit
['generated_from_trainer']
false
![SGH logo.png](https://s3.amazonaws.com/moonup/production/uploads/1667143139655-631feef1124782a19eff4243.png) This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 4.1...
a2fc92a3fd7312449042e48afea23091
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - label_smoothing_fact...
047eb484533c49a16afced8347dc730f
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001372 - train_batch_size: 1 - eval_batch_size: 8 - seed: 2780791035 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0
834261964c09e0990391173d6fee3460
mit
[]
false
CosmicRoBERTa This model is a further pre-trained version of RoBERTa for space science on a domain-specific corpus, which includes abstracts from the NTRS library, abstracts from SCOPUS, ECSS requirements, and other sources from this domain. The model performs slightly better on a subset (0.6 of total data set) of ...
5ba97d3cd1f12d1a854df45d39b262eb
mit
[]
false
gibasachan on Stable Diffusion This is the `gibasachan` 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...
3b4ea34b34f503cc1816a45bc1d31eba
['apache-2.0']
[]
false
```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer ...
48e23856854909b5c5a034737cc79b79
apache-2.0
['generated_from_trainer']
false
google-vit-base-patch16-224-cartoon-emotion-detection This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3706 - Accuracy: 0.8807 - Precision: 0.8769 - Rec...
9c3fa06198132ffeed71bd3e04fc1bd7
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 0.97 | 8 | 0.9902 | 0.5596 | 0.5506 | 0.5596 | 0.5360 | | 1.242 | 1.97 |...
dfb386d2c62c2511d6c20dba8f6beefc
cc-by-sa-4.0
['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio']
false
Description: This model was trained by Manuel Pariente using the wham/DPRNN recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `sep_clean` task of the WHAM! dataset.
8f576575aa2eb9f912fc3617f091f15c
cc-by-sa-4.0
['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio']
false
Training config: ```yaml data: mode: min nondefault_nsrc: None sample_rate: 8000 segment: 2.0 task: sep_clean train_dir: data/wav8k/min/tr valid_dir: data/wav8k/min/cv filterbank: kernel_size: 2 n_filters: 64 stride: 1 main_args: exp_dir: exp/train_dprnn_new/ gpus: -1 ...
3b89d271c4163e8810048138fd1c3bd0
cc-by-sa-4.0
['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio']
false
Results: ```yaml si_sdr: 19.316743490695334 si_sdr_imp: 19.317895273889842 sdr: 19.68085347190952 sdr_imp: 19.5298092932871 sir: 30.362213998701232 sir_imp: 30.21116982007881 sar: 20.15553251343315 sar_imp: -129.02091762351188 stoi: 0.97772664309074 stoi_imp: 0.23968091518217424 ```
e86ac940c6ccda220451e8621e0bff0f
cc-by-sa-4.0
['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio']
false
License notice: This work "DPRNNTasNet-ks2_WHAM_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A) by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only). "DPR...
81e7fc5f6fc2a41f7b20cee21cddbfcc
apache-2.0
['translation']
false
opus-mt-kwy-fr * source languages: kwy * target languages: fr * OPUS readme: [kwy-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kwy-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http...
6ca5e613c896c93b19cc3aa442d7b814
apache-2.0
[]
false
distilbert-base-en-lt-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accur...
320d574b34cf7f265fafab7e30f6f702
apache-2.0
[]
false
How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-lt-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-lt-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Gi...
a60a34c5662a87b3a2201542a3f48a17
afl-3.0
[]
false
Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b ...
09b53ba50b573abf89789eb3ea85c850
cc-by-sa-4.0
['coptic', 'masked-lm']
false
Model Description This is a DeBERTa(V2) model pre-trained on Coptic Scriptorium Corpora. You can fine-tune `deberta-base-coptic` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-coptic-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-coptic-ud-...
9dc02bb2779ab8a819e579cd206f00b5
cc-by-sa-4.0
['coptic', 'masked-lm']
false
How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-coptic") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-coptic") ```
1f3d661da29f69608480348817251d7a
apache-2.0
['generated_from_trainer']
false
bert-base-uncased-finetuned-jdcv-16Nov This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0005
5c01994df1400874f37291d1dcacdf1a
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0065 | 1.0 | 2951 | 0.0003 | | 0.0004 | 2.0 | 5902 | 0.0004 | | 0.0002 | 3.0 | 8853 | 0.0005 |
6de2be12447db557d5533ea6e51839a1
apache-2.0
vae
false
T5-VAE-Python (flax) A Transformer-VAE made using flax. Try the [demo](https://huggingface.co/spaces/flax-community/t5-vae)! It has been trained to interpolate on lines of Python code from the [python-lines dataset](https://huggingface.co/datasets/Fraser/python-lines). Done as part of Huggingface community trainin...
67c7e64a8f7a565a6cb5f70bab9d4292
apache-2.0
vae
false
How to use from the 🤗/transformers library Add model repo as a submodule: ```bash git submodule add https://github.com/Fraser-Greenlee/t5-vae-flax.git t5_vae_flax ``` ```python from transformers import AutoTokenizer from t5_vae_flax.src.t5_vae import FlaxT5VaeForAutoencoding tokenizer = AutoTokenizer.from_pretrain...
020efe50887460381907f14caffba47d
apache-2.0
['translation']
false
aze-spa * source group: Azerbaijani * target group: Spanish * OPUS readme: [aze-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-spa/README.md) * model: transformer-align * source language(s): aze_Latn * target language(s): spa * model: transformer-align * pre-processing: normalizati...
18ac38d8971b2c9db51843594eb534c7
apache-2.0
['translation']
false
System Info: - hf_name: aze-spa - source_languages: aze - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/aze-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['az', 'es'] - src_constituents: {'aze_Latn'} - tgt_...
d0b59878b0b41481d3cbf706b1700cd6
apache-2.0
['automatic-speech-recognition', 'es']
false
exp_w2v2t_es_xls-r_s51 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is...
148ac853dbd058a9abd2a80f9654a497
mit
['sot', 'fill-mask', 'pytorch', 'roberta', 'masked-lm']
false
How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_sot_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_sot_roberta") ```
9360d85658853671c781b70e65c431e7
apache-2.0
['generated_from_trainer']
false
bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Precision: 0.3170 - Recall: 0.5261 - F1: 0.3956 - Accuracy: 0.8...
8c721d52cb0029242badf28f98fd0f87
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.2912 | 0.2752 | 0.4444 | 0.3400 | 0.8730 | | No log | 2.0 |...
3a6262fd4d5a57066c64328a35daf627
apache-2.0
['stanza', 'token-classification']
false
Stanza model for Italian (it) 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 website](htt...
bd0f316776eb7031ef6e857ea91c06ae
mit
['natural-questions-short', 'question-answering']
false
xtremedistil-l6-h256-uncased for QA This is a [xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) model, fine-tuned using the [NaturalQuestionsShort](https://research.google/pubs/pub47761/) dataset from the [MRQA Shared Task 2019](https://github.com/mrqa/MRQA-Shared-Task-201...
ccd1d7383ba59b569643d0c28db6ec93
mit
['natural-questions-short', 'question-answering']
false
Overview **Language model:** xtremedistil-l6-h256-uncased **Language:** English **Downstream-task:** Extractive QA **Training data:** NaturalQuestionsShort **Eval data:** NaturalQuestionsShort **Infrastructure**: Google Colaboratory GPU
e0f647488169bcdfd50460b31a33cbb5
mit
['natural-questions-short', 'question-answering']
false
Hyperparameters ``` batch_size = 16 n_epochs = 2 base_LM_model = "xtremedistil-l6-h256-uncased" max_seq_len = 512 learning_rate = 3e-5 optimizer = AdamW weight_decay = 0.01 lr_schedule = Linear warmup_steps = 0 ```
b2be609ac56ae62f38e1dd935cecc06e
mit
['natural-questions-short', 'question-answering']
false
Performance The model was evaluated on the on the [NaturalQuestionsShort](https://research.google/pubs/pub47761/) dev set from the [MRQA Shared Task 2019](https://github.com/mrqa/MRQA-Shared-Task-2019) repository. ``` "exact_match": 46.914926768463694, "f1": 63.863619507647456, ```
c257226a6c59e9583bec28c2521584a7
mit
['natural-questions-short', 'question-answering']
false
UKP Square This model can also be found on [UKP Square](https://square.ukp-lab.de/qa). This website from the [UKP lab at the TU Darmstadt](https://www.informatik.tu-darmstadt.de/ukp/ukp_home/index.en.jsp) is a platform to compare and evaluate cloud-hosted QA models via explainability techniques and behavioral tests.
cf5cbaa64c598512fb121b2e29720ccf
apache-2.0
['generated_from_keras_callback']
false
amyeroberts/temp_upload_test_local_7 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2260 - Epoch: 1
926543dd44a4797ef21094ee4215959c
apache-2.0
['generated_from_trainer']
false
mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4754 - Accuracy: 0.8635
6793e2149e186d3dd035d3c1f9bffe40
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5038 | 1.0 | 8748 | 0.5359 | 0.8326 | | 0.3322 | 2.0 | 17496 | 0.5152 | 0.8394 | | 0.2798 | 3.0 | 26244 | 0.5338 ...
9ec8e2999eb9cafba541e2341d09be9b
apache-2.0
['generated_from_trainer']
false
distilbert_add_GLUE_Experiment_logit_kd_mnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5576 - Accuracy: 0.5239
87a7b5f08b935eadf294445fe23031d9
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.624 | 1.0 | 1534 | 0.6178 | 0.3605 | | 0.6176 | 2.0 | 3068 | 0.6138 | 0.3767 | | 0.6139 | 3.0 | 4602 | 0.6112 ...
d51fe6d00adc65c092efe3ef01abe60b
mit
['generated_from_trainer']
false
xlm-roberta-targin-final This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8172 - Accuracy: 0.6873 - Precision: 0.6494 - Recall: 0.6422 - F1: 0.6450
170e8c9dc3d7773eef3643bafbc2d9ae
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.6065 | 0.6873 | 0.6537 | 0.5833 | 0.5748 | | 0.597 | 2.0 |...
e0ba46c93765751e10db25c2d6539549
openrail
[]
false
Al-Nay (الناي) Unconditional Diffusion Al-Nay is one of the oldest instruments used to this date. With its roots in ancient Egypt nearly 5,000 years ago, it has become a staple in Arabic and Persian music. While the number of Nayzens – the name associated with skilled players of the instrument – has diminished over...
a14dfbdc360629514556ed046e3195d1
openrail
[]
false
Limitations of Model The dataset used was very small, so the diversity of snippets that can be generated is rather limited. Furthermore, with high intensity segments (think a human playing the instrument with high intensity,) the realism/naturalness of the generated flute degrades.
6559cbc227f98d9ab00de5949f4bf624
apache-2.0
['generated_from_trainer']
false
t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1345 - Rouge2 Precision: 0.8121 - Rouge2 Recall: 0.7208 - Rouge2 Fmeasure: 0.7567
6ed33828b892248b69873fb4d4e72211
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.2214 | 1.0 | 3569 | 0.1721 | 0.7829 | 0.6951 | 0.72...
c13c4eb2ee157a20195797efd56d3793
apache-2.0
['deep-narrow']
false
T5-Efficient-SMALL-FF12000 (Deep-Narrow version) T5-Efficient-SMALL-FF12000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* check...
2502e880fe7d5cffd47a92e9dcf36f80
apache-2.0
['deep-narrow']
false
Details model architecture This model checkpoint - **t5-efficient-small-ff12000** - is of model type **Small** with the following variations: - **ff** is **12000** It has **186.35** million parameters and thus requires *ca.* **745.4 MB** of memory in full precision (*fp32*) or **372.7 MB** of memory in half precis...
0fb4c9236e3c2650df754f9903f556b6
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: 0.3112 - Accuracy: 0.9128
dc3b796f47a2cad2720f5b2b28123c5a
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1868 | 1.0 | 4210 | 0.3112 | 0.9128 | | 0.1236 | 2.0 | 8420 | 0.3682 | 0.9083 | | 0.1007 | 3.0 | 12630 | 0.3696 ...
48e178145f1b7acd4b73c8af06a501c3
apache-2.0
['bart', 'seq2seq', 'summarization']
false
Usage ```python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/bart-large-xsum-samsum") conversation = '''Hannah: Hey, do you have Betty's number? Amanda: Lemme check Amanda: Sorry, can't find it. Amanda: Ask Larry Amanda: He called her last time we were at the park together...
ba6f373a04f3b3ad7888fa101fe1141a
apache-2.0
['generated_from_keras_callback']
false
EMaghakyan/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 18.8463 - Validation Loss: 8.7150 - Epoch: 0
a2593de9a34f915b55f032d5a21c735f
apache-2.0
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 208, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'...
8832c6c68f135d58e1c1367195a8d029
mit
[]
false
ResNet50 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia, [ad...
a2ee9b7506b1fbea9cf1231d0449310c
mit
['generated_from_trainer']
false
xlm-roberta-base-jm-finetuned-panx-it_hub 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: 0.2590 - F1: 0.8124
a44813c1bc4e66d1864ee4b66aaa994e
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8144 | 1.0 | 70 | 0.3081 | 0.7371 | | 0.2885 | 2.0 | 140 | 0.2605 | 0.8119 | | 0.184 | 3.0 | 210 | 0.2590 | 0.8124 | ...
b130119e098d4d4ce7a07f8980488907
apache-2.0
['feature-extraction', 'ja', 'japanese', 'clip', 'cloob', 'vision']
false
rinna/japanese-cloob-vit-b-16 ![rinna-icon](./rinna.png) This is a Japanese [CLOOB (Contrastive Leave One Out Boost)](https://arxiv.org/abs/2110.11316) model trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/). Please see [japanese-clip](https://github.com/rinnakk/japanese-clip) for the other available models. ...
7dd56b0d90cd3f028ce891ad44a607a1
apache-2.0
['feature-extraction', 'ja', 'japanese', 'clip', 'cloob', 'vision']
false
How to use the model 1. Install package ```shell $ pip install git+https://github.com/rinnakk/japanese-clip.git ``` 2. Run ```python import io import requests from PIL import Image import torch import japanese_clip as ja_clip device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = ja_clip.l...
6c0f34ebbbd4345c83198f6ddeb5434e
apache-2.0
['feature-extraction', 'ja', 'japanese', 'clip', 'cloob', 'vision']
false
this is optional. if you don't pass, load tokenizer each time ) with torch.no_grad(): image_features = model.get_image_features(image) text_features = model.get_text_features(**encodings) text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) print("Label probs:", text_probs)
4a67572c515d1cb3b793440901b790fb
apache-2.0
['feature-extraction', 'ja', 'japanese', 'clip', 'cloob', 'vision']
false
Model architecture The model was trained a ViT-B/16 Transformer architecture as an image encoder and uses a 12-layer BERT as a text encoder. The image encoder was initialized from the [AugReg `vit-base-patch16-224` model](https://github.com/google-research/vision_transformer).
ed390589d09cde0fba15df07131ccb63
mit
['huggan', 'gan', 'unconditional-image-generation']
false
Model description [FastGAN model](https://arxiv.org/abs/2101.04775) is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model w...
8fd855dccf7e00e0601361375c581716
mit
['huggan', 'gan', 'unconditional-image-generation']
false
Clone this model git clone https://huggingface.co/huggan/fastgan-few-shot-painting/ def load_generator(model_name_or_path): generator = Generator(in_channels=256, out_channels=3) generator = generator.from_pretrained(model_name_or_path, in_channels=256, out_channels=3) _ = generator.eval() return ge...
868e1a13b73287dcf5366dbfb68fc2ea
apache-2.0
['translation']
false
opus-mt-sv-rnd * source languages: sv * target languages: rnd * OPUS readme: [sv-rnd](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-rnd/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http...
55455a3e00344cca0ef923813e79f18d
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT) Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
998001d62c2f2d23883e86336607863f
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
Training Data This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Donald Trump.
3b8c0e8961d01981d91e88cc0bf15885
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
Usage This pre-trained language model is fine-tuned to the stance detection task specifically for Donald Trump. Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import tor...
f67473e9195085655e92eda459124d7b
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
load model tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path) model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path) id2label = { 0: "AGAINST", 1: "FAVOR", 2: "NONE" }
e841b938acebb1aa3bc2749c128caceb
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
sentence = "Hello World." inputs = tokenizer(sentence.lower(), return_tensors="pt") outputs = model(**inputs) predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist() print("Sentence:", sentence) print("Prediction:", id2label[np.argmax(predicted_probability)]) print("Against:", predicted_probability[0]) p...
668b24c6bb1b17fd0d299108164b3b8c
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
sentence = "Go Go Trump!!!" inputs = tokenizer(sentence.lower(), return_tensors="pt") outputs = model(**inputs) predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist() print("Sentence:", sentence) print("Prediction:", id2label[np.argmax(predicted_probability)]) print("Against:", predicted_probability[0])...
d81d054be74116479741f8f7c73492e5
gpl-3.0
['twitter', 'stance-detection', 'election2020', 'politics']
false
sentence = "Trump is the worst." inputs = tokenizer(sentence.lower(), return_tensors="pt") outputs = model(**inputs) predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist() print("Sentence:", sentence) print("Prediction:", id2label[np.argmax(predicted_probability)]) print("Against:", predicted_probabilit...
cf110856875db96fea5be6b261384aa6
creativeml-openrail-m
['stable-diffusion']
false
MeadMix ![TitleImage](https://huggingface.co/sazanka-imoto/MaedMix/resolve/main/examples/13486-3096934695-masterpiece%201%20girl%20smile%20blush%20standing%20Dirndl%20blonde%20hair%20blue%20eys%20mead%20bottle%20Taverns%20middle%20ages%20anime3839bcfb487f3d164c8fcf80d50c9fa6b72ee7b8.png) MeadMix is a merged model that...
0b287e82c310a0430aadda0dacc78a0f
creativeml-openrail-m
['stable-diffusion']
false
Examples ![example01](https://huggingface.co/sazanka-imoto/MaedMix/resolve/main/examples/example01.png) ``` 1girl, solo, hat, witch hat, skirt, long hair, thighhighs, bow, flask, red bow, book, long sleeves, black footwear, open mouth, black hair, holding, shirt, indoors, flower, test tube, pleated skirt, black headwe...
e97c43d576d4155728ba75282e6e124e
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5277 | 0.7840 |
b7938d8de0422cb2486dceed29772496
mit
['generated_from_keras_callback']
false
Sushant45/Human_Development_Index-clustered This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1822 - Train End Logits Accuracy: 0.9375 - Train Start L...
aa6af7c3f71c2ce3d8a881c67f374fad
mit
['generated_from_keras_callback']
false
Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------...
fcacf494acf3bad03fddadf6d5491f0c
apache-2.0
['automatic-speech-recognition', 'NyanjaSpeech', 'generated_from_trainer']
false
xls-r-300m-nyanja-fullset This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NYANJASPEECH - NYA dataset. It achieves the following results on the evaluation set: - Loss: 3.1987 - Wer: 1.0
08275e31bd8967198bc144284f5e0f06
apache-2.0
['automatic-speech-recognition', 'NyanjaSpeech', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched...
b37ff426c1485c26592ee16369d721e9
mit
['PyTorch', 'Transformers', 'spaCy', 'ELECTRA', 'GiNZA', 'mC4', 'UD_Japanese-BCCWJ', 'GSK2014-A', 'ja', 'MIT']
false
transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese) This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3...
3a5bfdca6e261803c6a0f36d8a854ee9
mit
['PyTorch', 'Transformers', 'spaCy', 'ELECTRA', 'GiNZA', 'mC4', 'UD_Japanese-BCCWJ', 'GSK2014-A', 'ja', 'MIT']
false
Acknowledgments This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo.
08a2301c6fb2ede849c8a8a6996b1136
mit
['PyTorch', 'Transformers', 'spaCy', 'ELECTRA', 'GiNZA', 'mC4', 'UD_Japanese-BCCWJ', 'GSK2014-A', 'ja', 'MIT']
false
Citations - [mC4](https://huggingface.co/datasets/mc4) Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Mi...
a402c21970dd65d7313aab760258e89d
apache-2.0
['generated_from_trainer']
false
funnel-transformer-xlarge_cls_CR This model is a fine-tuned version of [funnel-transformer/xlarge](https://huggingface.co/funnel-transformer/xlarge) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9388
d09b0a8585fba528adc62e8210c7cca9
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 213 | 0.3813 | 0.9016 | | No log | 2.0 | 426 | 0.5227 | 0.8564 | | 0.3933 | 3.0 | 639 | 0.2958 | 0....
d204c6d0d4a9eefd7f18b0c1ca2f4439
mit
[]
false
rahkshi bionicle on Stable Diffusion This is the `<rahkshi-bionicle>` 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. ...
f94116c3de5a28c909bbee2b4e347351