license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | How to use This model along with the tokenizer can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Milos/slovak-gpt-j-405M") model = AutoModelForCausalLM.from_pretrained("Milos/slovak-g... | 72ce2e93ed5aad2ef35979f548864db7 |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Capabilities, Limitations, and Biases The capability of this particular model is somewhat decent despite its small size totalling 405M parameters. With relative ease it can manage to generate interesting and grammatically correct content. For example, you can try few of the following prompts. (For sake of simplicity,... | bae7a17583a9c61984573fcf596bdc86 |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | transformers.generation_utils.GenerationMixin.generate) on how to introduce a frequency/repetition penalty. Since the dataset contains profanity, politically incorrect language, and (unintentionally) even a bits of text in Czech, the model can generate them in some extent too. Here's an example of the model output wh... | 9bf9db54d6ae1ab09e4e6f8e315978da |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Citation and Related Information This was done as a moonlighting project during summer of 2021 to better understand transformers. I didn't have much free time to open source it properly, so it all sat on my hard drive until now :) If you use this model or have any questions about it feel free to hit me up at [twitte... | 7ef15593b47076a059e9e0ceb17e938f |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | BibTeX entry To cite this model: ```bibtex @misc{slovak-gpt-j-405m, author = {Kondela, Milos}, title = {{Slovak GPT-J-405M}}, howpublished = {\url{https://huggingface.co/Milos/slovak-gpt-j-405M}}, year = 2022, month = February } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transfor... | 706768460fde38d542931c0615b932a5 |
gpl-3.0 | ['Slovak GPT-J', 'pytorch', 'causal-lm'] | false | Acknowledgements This project was generously supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/about/). Shoutout also goes to [Ben Wang](https://github.com/kingoflolz) and great [EleutherAI community](https://www.eleuther.ai/). | 70308ea44dfb819e43fa841ebc5d8045 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-gc-art1e 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: 0.0928 - Accuracy: 0.982 - F1: 0.9763 | d27e971393c09baad7f0903237c2eb7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0226 | 1.0 | 32 | 0.0928 | 0.982 | 0.9763 | | 27ed43192a2a8d3c8ed71d7c78e56d3d |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Turkish This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | adf178a7683125c688c1f5b52826f393 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-tr") ``` | c71be84fc8f2b0cbf6e100efd4d20d09 |
mit | ['generated_from_trainer'] | false | stbl_clinical_bert_ft_rs10 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0846 - F1: 0.9297 | 0d884f5f48b485583668899875e15bfd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2834 | 1.0 | 101 | 0.0930 | 0.8446 | | 0.0669 | 2.0 | 202 | 0.0732 | 0.8938 | | 0.033 | 3.0 | 303 | 0.0676 | 0.9119 | |... | e4befc74f4d489d86056f959945c6368 |
apache-2.0 | ['generated_from_keras_callback'] | false | stevhliu/my_awesome_wnut_model 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: - Train Loss: 0.1210 - Validation Loss: 0.2698 - Train Precision: 0.5099 - Train Recall: 0.3995... | 04db3db137002b37edb9a23bddb85d72 |
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': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 6484c09cc6c71d87ecd4e76704801ca0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3233 | 0.3099 | 0.4155 | 0.2117 | 0.2805 | 0.9333 | 0 ... | 5ef9cd52099722f149a5247148eb3979 |
cc-by-sa-4.0 | ['translation'] | false | How to use This model uses transformers and sentencepiece. ```python !pip install transformers sentencepiece ``` You can use this model directly with a pipeline: ```python from transformers import pipeline fugu_translator = pipeline('translation', model='staka/fugumt-en-ja') fugu_translator('This is a cat.') ``` If... | 06ff8f7a8c027316728a7342aa328bc0 |
cc-by-sa-4.0 | ['translation'] | false | Eval results The results of the evaluation using [tatoeba](https://tatoeba.org/ja)(randomly selected 500 sentences) are as follows: |source |target |BLEU(*1)| |-------|-------|--------| |en |ja |32.7 | (*1) sacrebleu --tokenize ja-mecab | 88dbe3667b3c538227c4df511f793271 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper medium Finnish CV This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 fi dataset. It achieves the following results on the evaluation set: - Loss: 0.3010 - Wer: 15.7181 | 5852d09cb25ec79b74879d4ec93bf741 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Model description The Model is fine-tuned for 1000 steps/updates on CV11 Finnish train+valiation data. - Zero-shot - 18.8 (CV9 test data, even on CV11 the WER is closer a bit higher than this) - Fine-tuned - 15.71 (CV11 test data) | c06880ef5d2b639c7c2b693dfc41cb55 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - 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 - training_steps: 1000 - mixed_precis... | add5f8d88e6907395632d4374dc6aac5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0009 | 19.01 | 1000 | 0.3010 | 15.7181 | | 29b06d29092eef71708c5a4d8a2d8c26 |
creativeml-openrail-m | [] | false | Link to the constituent models: https://huggingface.co/Conflictx/Complex-Lineart https://huggingface.co/Envvi/Inkpunk-Diffusion https://huggingface.co/ogkalu/Comic-Diffusion https://huggingface.co/nitrosocke/Ghibli-Diffusion | 8129e941765947adc8625fc6668bdd7c |
creativeml-openrail-m | [] | false | Sample images:   on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0050 - Validation Loss: 0.1358 - Epoch: 9 | 9dd4cf0ef63a8b6155cbeb1d84ff3163 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2800, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 2c6078562abeaea4e4ee762f7e4bf664 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3514 | 0.1493 | 0 | | 0.1166 | 0.1159 | 1 | | 0.0628 | 0.1066 | 2 | | 0.0282 | 0.1249 | 3 | | 0.0245 | 0.1338 | 4 | | 0.0181 |... | e2b2a8cb9cfeb8b10c898aa93a7dbd7e |
apache-2.0 | ['code', 'gpt2', 'generation'] | false | CodeParrot 🦜 small for text-t-code generation This model is [CodeParrot-small](https://huggingface.co/codeparrot/codeparrot-small) (from `branch megatron`) Fine-tuned on [github-jupyter-text-to-code](https://huggingface.co/datasets/codeparrot/github-jupyter-text-to-code), a dataset where the samples are a succession... | 6279b35a5f7d075be1e227f2c84170d7 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | HassanBlend1.4 I am hassan, I created HassansBlend, the latest version currently is 1.4. I continue to iterate and improve on this model over time. Feel free to check out our discord or rentry page for more examples with prompts and outputs generated. I have also some custom created content such as enhancement hypern... | 6268849230f27a920303fd2200574da9 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Gradio Demo We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run hassanblend1.4: [ on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8022 | 081e5a7b94a8b9a8be7a791ececd4755 |
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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 22 | 9173d78461058b9024900c8a8266ebe5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3257 | 1.0 | 1825 | 3.2231 | | 2.9885 | 2.0 | 3650 | 3.0069 | | 2.7955 | 3.0 | 5475 | 2.8440 | | 2.5748 | 4.0 | 7300 | 2.7059 ... | 9b1adeb7cd3b5a381c5139ea316bb618 |
apache-2.0 | ['generated_from_trainer'] | false | bert-mini-mlm-finetuned-imdb This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6935 | d5748377d08be9824f3e0ca2c3ad5955 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.2058 | 0.64 | 500 | 2.9411 | | 3.1048 | 1.28 | 1000 | 2.9042 | | 3.0631 | 1.92 | 1500 | 2.8780 | | 3.0197 | 2.56 | 2000 | 2.8667 ... | bcd1fbc4a0f33669663dc4042aedf26a |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-2-finetuned-RRamicus 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: 1.4784 | ff0cb11a41e1471f2d94f90a958d3636 |
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: 928 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 28609e72118baa777dd17964d856ca0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0341 | 1.0 | 1113 | 1.7515 | | 1.7881 | 2.0 | 2226 | 1.6616 | | 1.697 | 3.0 | 3339 | 1.6061 | | 1.6328 | 4.0 | 4452 | 1.5662 ... | f7a77dd54d309332154ef26f8232f408 |
apache-2.0 | ['generated_from_trainer'] | false | my_sanskrit_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the itihasa dataset. It achieves the following results on the evaluation set: - Loss: 3.5101 - Bleu: 0.2607 - Gen Len: 18.9973 | b2035651b954bb44bda2b2bde96c7b29 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 3.9557 | 1.0 | 4698 | 3.7191 | 0.3291 | 18.9973 | | 3.8243 | 2.0 | 9396 | 3.6068 | 0.2728 | 18.9973 | | 3.7562 |... | 396c6e4fd8cb8eb26e6243ef5a6ce9a8 |
apache-2.0 | ['idt5'] | false | Indonesian Version of Multilingual T5 Transformer Smaller version of the [Google's Multilingual T5-base](https://huggingface.co/google/mt5-base) model with only Indonesian and some English embeddings. This model has to be fine-tuned before it is useable on a downstream task.\ Fine-tuned idT5 for the Question Generat... | bddc20e3656f3a4948aeca088474c1c2 |
apache-2.0 | ['idt5'] | false | Citation ``` @misc{https://doi.org/10.48550/arxiv.2302.00856, doi = {10.48550/ARXIV.2302.00856}, url = {https://arxiv.org/abs/2302.00856}, author = {Fuadi, Mukhlish and Wibawa, Adhi Dharma and Sumpeno, Surya}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Comp... | 607f75dc83c53647ce7bd319ae64106d |
apache-2.0 | ['idt5'] | false | Abstract Indonesian language is spoken by almost 200 million people and is the 10th most spoken language in the world, but it is under-represented in NLP (Natural Language Processing) research. A sparsity of language resources has hampered previous work on Indonesian. The Transformer is a new architecture rapidly beco... | 01c4278bfe386ba1bd30d41294caed09 |
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: 0.5452 - Wer: 0.3296 | 45eb258edc5783e7f27ecd671d80555d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5557 | 1.0 | 500 | 1.9362 | 1.0072 | | 0.867 | 2.01 | 1000 | 0.5197 | 0.5173 | | 0.4281 | 3.01 | 1500 | 0.4609 | 0.455... | 03a3ad088188ef14f31d8cd1663d374a |
mit | [] | false | A Hat kid on Stable Diffusion This is the `<hatintime-kid>` 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 al... | 2b0563be3bcd3b6f4f9ad42b557824da |
gpl-3.0 | ['bicleaner-ai'] | false | Bicleaner AI full model for en-hbs Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Fin... | 1d678a3dd2474cff9a832c2657bcf2ca |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-5front-1body-5rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4383 - Wer: 0.1697 - Mer: 0.1641 - Wil: 0.2500 - Wip: 0.7500 - Hits: 55852 - S... | 1e0befc6687c857d0a71a1b68abf5cf0 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6185 ... | 135c24f0f32ea80b00aaf2caf2f5937e |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_no-pretraining_s930 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model h... | 3208a4f7b61a40ad2c02c922e4a474da |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-b1.25 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.7945 - Bleu: 7.5563 - Gen Len: 44.1141 | 5337d4124aec64897dc9fa066eb706cc |
openrail | [] | false | MJv4 Hallucinations These are 3 models trained on a small (<2000) dataset of Midjourney v4 images with no particular style. <b> These models are nowhere near as good as Midjourney v4 </b>, and they all suffer from a lot of "language drift" but they do have an interesting style. They are the best of something like 60 ... | 4af67729e4ff22a2d2d334c516c1a29a |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-snli-plain_text 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 None dataset. It achieves the following results on the evaluation set: - Loss: 3.1233 | 8ef444c5ebe4d3d4df9ca5424cd68f69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.665 | 0.4 | 500 | 3.2495 | | 3.4103 | 0.8 | 1000 | nan | | 3.2635 | 1.2 | 1500 | 3.1518 | | 3.1738 | 1.6 | 2000 | 3.1555 ... | 00308b9f760e6be7b4a50b7c186c53c4 |
apache-2.0 | ['pytorch', 'query-generation'] | false | Model description: This model was created with the purpose to generate possible queries for a german input article. For this model, we finetuned a multilingual T5 model [mt5-small](https://huggingface.co/google/mt5-small) on the [MMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco) the machine translate... | f1964217c2da5293d5fcff825c8ad785 |
apache-2.0 | ['pytorch', 'query-generation'] | false | Model Performance: Model evaluation was done on 2000 evaluation paragraphs of the dataset. Mean [f1 ROUGE scores](https://github.com/pltrdy/rouge) were calculated for the model. | Rouge-1 | Rouge-2 | Rouge-L | |---|---|---| |0.162 | 0.052 | 0.161 | | 3ff9b681b196f86980c366b3aebe8a8d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.4235 - Accuracy: 0.4729 | fdf6e74d14ff7a2a845291d5a9b42172 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4313 | 1.0 | 10 | 0.4259 | 0.4729 | | 0.4183 | 2.0 | 20 | 0.4235 | 0.4729 | | 0.4175 | 3.0 | 30 | 0.4239 | 0.... | d1fa15d7cbf424bfc998f232e039ce03 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola 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: 1.1195 - Matthews Correlation: 0.6749 | 7a237b57a8a639f8229ee98d9311f13e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 8 | 1.6008 | 0.5863 | | No log | 2.0 | 16 | 1.5039 | 0.4583 | | No ... | ecfd284adf726d9555866df6263e2f25 |
mit | [] | false | Models and other data for https://github.com/jeniyat/StackOverflowNER. Use `git lfs fetch --all` to download all files. Please note that folders are stored decompressed due to HuggingFace file size limitations. The individual files in ./data_ctc/ are compressed using `gzip`, and can be decompressed using `gunzip -d... | fa5a404a6ba1665f8bc5c8f50a0137ce |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-train 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: 2.2367 - Rouge1: 43.9525 - Rouge2: 22.3403 - Rougel: 38.7683 - Rougelsum: 39.2056 | 5e183a2500486cfcbe35a4cbd4a90d96 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.6e-05 - train_batch_size: 9 - eval_batch_size: 9 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | b054144a5fa4dc4501ced4c0f049c256 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 3.3237 | 1.0 | 40 | 2.6713 | 34.4731 | 14.9731 | 29.4814 | 29.9747 | | 2.7401 | 2.0 ... | 443d0b0067f0a2f4f7747b514c447711 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-effectiveFeedback-Classification-kaggleEffectiveFeedback2 This model is a fine-tuned version of [YaHi/bert-base-uncased-finetuned-effectiveFeedback](https://huggingface.co/YaHi/bert-base-uncased-finetuned-effectiveFeedback) on the None dataset. It achieves the following results on the eval... | 6f194a270dde7dcb18e25bd1d3ea5ac7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7506 | 1.0 | 3677 | 0.7284 | | 0.623 | 2.0 | 7354 | 0.7558 | | 0.4225 | 3.0 | 11031 | 0.9724 | | a105a39a383ed547b7decea3e1076204 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 500k (uncased) Seed 3 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | c4d5407062e7e74ba7d22e5b0bf227f3 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-3-500k') model = BertModel.from_pretrained("multiberts-seed-3-500k") text = "Replace me by any text you'd like.... | 12066f2f958e3f70f4127173435d42b6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-cola-2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9235 - Matthews Correlation: 0.6016 | 93a4337db83edd775127b1a69d6ff14a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4906 | 1.0 | 535 | 0.5046 | 0.5080 | | 0.2901 | 2.0 | 1070 | 0.5881 | 0.5235 | | 0.1... | 4ed1e58c8bc39095f4f7f99c62a499d3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-arxiv-pubmed-pubmed-v3-e16 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8306 - Rouge1: 56.4519 - R... | 5f81a080637de1250522283b4ad191cb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP | 0e88f6a845473cdcd654dec5a7c461cf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1157 | 50.9487 | 31.3005 | 34.0145 | 48.6057 | ... | 6ce95312600424212c33f99ab4b96cc0 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | BERT for Chinese Named Entity Recognition(bert4ner) Model 中文实体识别模型 `bert4ner-base-chinese` evaluate PEOPLE(人民日报) test data： The overall performance of BERT on people **test**: | | Accuracy | Recall | F1 | | ------------ | ------------------ | ------------------ | ------------------ | | BertSoftmax... | 392bc921afbb607ed1146c5abfcbacd8 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | Usage 本项目开源在实体识别项目：[nerpy](https://github.com/shibing624/nerpy)，可支持bert4ner模型，通过如下命令调用： ```shell >>> from nerpy import NERModel >>> model = NERModel("bert", "shibing624/bert4ner-base-chinese") >>> predictions, raw_outputs, entities = model.predict(["常建良，男，1963年出生，工科学士，高级工程师"], split_on_space=False) entities: [('常建良'... | bd8499a4dd9b0164188cafed0e8ce187 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | Usage (HuggingFace Transformers) Without [nerpy](https://github.com/shibing624/nerpy), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the bio tag to get the entity words. Install package: ``` pip install transformers seqeval ``` ```python import os... | 09d1a06c751a2b517460e1e9a33fb599 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("shibing624/bert4ner-base-chinese") model = AutoModelForTokenClassification.from_pretrained("shibing624/bert4ner-base-chinese") label_list = ['I-ORG', 'B-LOC', 'O', 'B-ORG', 'I-LOC', 'I-PER', 'B-TIME', 'I-TIME', 'B-PER'] sentence = "王宏伟来自北京，是个警... | 23eac648de2bcf508c475bb8bd94f958 |
apache-2.0 | ['bert', 'pytorch', 'zh', 'ner'] | false | 中文实体识别数据集 | 数据集 | 语料 | 下载链接 | 文件大小 | | :------- | :--------- | :---------: | :---------: | | **`CNER中文实体识别数据集`** | CNER(12万字) | [CNER github](https://github.com/shibing624/nerpy/tree/main/examples/data/cner)| 1.1MB | | **`PEOPLE中文实体识别数据集`** | 人民日报数据集（200万字） | [PEOPLE github](https://github.com/shibing624/nerpy/tree/... | eeab7b860c21d4823b3a7c5c68e484ca |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-NER-favsbot This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.0992 - Precision: 0.8571 - Recall: 0.96 - F1: 0.9057 - Accuracy: 0.9583 | a6486e8e7375fcebe668d99675b61a95 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 1.7643 | 0.0 | 0.0 | 0.0 | 0.5694 | | No log | 2.0 |... | bb59db58943fdd218695ffe337ab76c7 |
bsd-3-clause | [] | false | Model description This code generation model was fine-tuned on Python code from a generic multi-language code generation model. This model was then pushed to 30% sparsity using Yurts' in-house technology without performance loss. In this specific instance, the class representation for the network is still dense. This ... | 3fc08ab71a3d96e4196aecb77a852159 |
bsd-3-clause | [] | false | Training data This model was tuned on a subset of the Python data available in the BigQuery open-source [Github dataset](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code). | 92e0963b69f7a41e107dc3834146114e |
bsd-3-clause | [] | false | How to use The model is great at autocompleting based off of partially generated function signatures and class signatures. It is also decent at generating code base based off of natural language prompts with a comment. If you find something cool you can do with the model, be sure to share it with us! Check out our [... | fa00da85af564f4393bdfb6fe0d03940 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-EL8 (Deep-Narrow version) T5-Efficient-BASE-EL8 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* checkpoint and ... | 1b2e77c6c5d145d0365565a91fc8588a |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-el8** - is of model type **Base** with the following variations: - **el** is **8** It has **194.61** million parameters and thus requires *ca.* **778.44 MB** of memory in full precision (*fp32*) or **389.22 MB** of memory in half precision (*fp... | c2ce6caa7b7f169423828bd024d9026f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | d833ec59b84da84a51e0e406cd515fe3 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649934336}, 'generation': {'batch_size': 128, 'every_n_steps': 256, 'force_call_on': [12588], 'metrics_con... | a3faf382b65b64efc5a561d6b89b23e4 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-wikitext2 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: 1.8833 | 5751c3f3c7e2ad38cbb4b92216930aa5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.1026 | 1.0 | 5835 | 1.9705 | | 2.0088 | 2.0 | 11670 | 1.9090 | | 1.9766 | 3.0 | 17505 | 1.8833 | | 4b0dfcca7412358ff784f5671254243c |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Model description **iGPT-fr** 🇫🇷 is a GPT model for French pre-trained incremental language model developped by the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We adapted [GPT-fr 🇫🇷](https://huggingface.co/asi/gpt-fr-cased-base) model to generate images conditionned by text inputs. | 87e8aca1278a409b42213f86477fb38e |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | How to use The model might be used through the 🤗 `Transformers` librairie. You will also need to install the `Taming Transformers` library for high-resolution image synthesis: ```bash pip install git+https://github.com/CompVis/taming-transformers.git ``` ```python from transformers import GPT2Tokenizer, GPT2LMHead... | 05f8f242873b436d962675fa290b1693 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Load VQGAN model vqgan_ckpt = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="model.ckpt", force_download=False) vqgan_config = hf_hub_download(repo_id="boris/vqgan_f16_16384", filename="config.yaml", force_download=False) config = OmegaConf.load(vqgan_config) vqgan_model = vqgan.VQModel(**config.model.par... | b568783e50921b3c5400e522fd46e667 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Add image generation token greedy_output = model.generate( input_ids.to(device), max_length=256+input_ids.shape[1], do_sample=True, top_p=0.92, top_k=0) def custom_to_pil(x): x = x.detach().cpu() x = torch.clamp(x, -1., 1.) x = (x + 1.)/2. x = x.permute(1,2,0).numpy() x = (255*x).astype(np.uin... | 393c002ec5c4b8d224173890b30d361b |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Add image generation token all_images = [] for i in tqdm(range(num_images)): greedy_output = model.generate( input_ids.to(device), max_length=256+input_ids.shape[1], do_sample=True, top_p=0.92, top_k=0) z_idx = greedy_output[0, input_ids.sha... | 6cc579604fe4092c829bc2b95258f9cf |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | this is the image-text similarity score scores = np.array(logits[0].detach()).argsort()[-k:][::-1] return [images[score] for score in scores] filtered_images = clip_top_k(input_sentence, all_images) for fi in filtered_images: display(fi) ``` | 6bc2686bddf8227b7c5a936f621ce8e8 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Training data We created a dedicated corpus to train our generative model. The training corpus consists in text-image pairs. We aggregated portions from existing corpora: [Laion-5B](https://laion.ai/blog/laion-5b/) and [WIT](https://github.com/google-research-datasets/wit). The final dataset includes 10,807,534 sampl... | 2ecf734393acf4c940dc42b498162fd3 |
apache-2.0 | ['tf', 'pytorch', 'gpt2', 'text-to-image'] | false | Training procedure We pre-trained the model on the new CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/eng/jean-zay/) supercomputer. We perform the training within a total of 140 hours of computation on Tesla V-100 hardware (TDP of 300W). The training was distributed on 8 compute ... | 21609c726558cdb68540ee189517e3c7 |
apache-2.0 | ['Image Captioning'] | false | Model Description These are model weights originally provided by the authors of the paper [Text-Only Training for Image Captioning using Noise-Injected CLIP](https://arxiv.org/pdf/2211.00575.pdf). Their method aims to train CLIP with only text samples. Therefore they are injecting zero-mean Gaussian Noise into the t... | 74e2b44dc953b44c81636eb282a9152c |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | whisper-medium-et-ERR2020 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the following training sets: Common Voice 11, VoxPopuli, FLEURS and [ERR2020](http://bark.phon.ioc.ee/lw/korpused/ERR2020.html). The checkpoint-7000 was on [Whisper Event leaderboar... | 4d99eda3390491046e0692fa6ba35041 |
apache-2.0 | ['generated_from_trainer', 'whisper-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1828 | 0.1 | 1000 | 0.3547 | 20.8829 | | 0.09 | 0.2 | 2000 | 0.3476 | 19.0096 | | 0.083 | 0.3 | 3000 | 0.3386 | 1... | 74bb7301ebd1790aacf2f61d7b758a3f |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.