license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['indogpt', 'indobenchmark', 'indonlg'] | false | IndoBART-v2 Model [IndoBART-v2](https://arxiv.org/abs/2104.08200) is a state-of-the-art language model for Indonesian based on the BART model. The pretrained model is trained using the BART training objective. | b462bb4388f10ba4f9a2f712d157481e |
mit | ['indogpt', 'indobenchmark', 'indonlg'] | false | params | Training data | |--------------------------------|--------------------------------|-----------------------------------| | `indobenchmark/indobart-v2` | 132M | Indo4B-Plus (26 GB of text) | | fee5adef5af70c111604f07734b19d3c |
mit | ['indogpt', 'indobenchmark', 'indonlg'] | false | Authors <b>IndoBART</b> was trained and evaluated by Samuel Cahyawijaya*, Genta Indra Winata*, Bryan Wilie*, Karissa Vincentio*, Xiaohong Li*, Adhiguna Kuncoro*, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung | 17f5c7810f923888237de5a779f853c1 |
mit | ['indogpt', 'indobenchmark', 'indonlg'] | false | Citation If you use our work, please cite: ```bibtex @article{cahyawijaya2021indonlg, title={IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation}, author={Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu Leylia and others}, journal={arXiv preprint arXiv:2104.08200}, year={2021} } ``` | 7feb2596466e4a0195678a16ce26e421 |
mit | ['conversational'] | false | Finetuned DialoGPT model on Spanish Conversations This model was finetuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model on subtitles from Spanish movies and telenovelas from the awesome [OpenSubtitle dataset](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles). DialoGPT paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) Some example dialog from the model: |Role | Response | |---------|--------| |User | ¿Cómo está el clima hoy? | | Bot | Muy bien. | |User | Eso es bueno escuchar. ¿Vas a ir a la playa? | | Bot | No, también puedo regresar. | |User | Oh bien, bueno, iré a la playa más tarde. | | Bot | ¡No! | |User | ¿Qué está mal con eso? | | Bot | ¡Cómo que no! | |User | Estoy confundido, ¿por qué no puedo ir a la playa? | | Bot | ¡Cómo que no! | |User | Explícamelo por favor. | | Bot | ¡No! | | c80622cfb9f8db7125450304c66be4b2 |
mit | ['conversational'] | false | Using the model Example code for trying out the model (taken directly from the [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) model card): ```python from transformers import AutoModelWithLMHead, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("ncoop57/DiGPTame-medium") model = AutoModelWithLMHead.from_pretrained("ncoop57/DiGPTame-medium") | d63737faf6510353372a9b9aa489a46f |
mit | ['conversational'] | false | Training your own model If you would like to finetune your own model or finetune this Spanish model, please checkout my blog post on that exact topic! https://nathancooper.io/i-am-a-nerd/chatbot/deep-learning/gpt2/2020/05/12/chatbot-part-1.html | 2ffd9f6e1f1a10ef5aa537acaa09473d |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_xls-r_s235 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 (pl)](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 has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | e75f366932c3ab7dd6cc92938b536c93 |
apache-2.0 | ['generated_from_trainer'] | false | google_electra-small-discriminator_squad This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the **squadV1** dataset. - "eval_exact_match": 76.95364238410596 - "eval_f1": 84.98869246841396 - "eval_samples": 10784 | f53e9d3b5141dac59a90991cbc898ba1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 306aff88cb3e57b34127b4df976d6459 |
apache-2.0 | ['translation'] | false | opus-mt-kwn-en * source languages: kwn * target languages: en * OPUS readme: [kwn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/kwn-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/kwn-en/opus-2020-01-09.eval.txt) | fdb734bb80c1bb63f8967c9279276f13 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab11 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: 1.6269 - Wer: 0.7418 | 0e4787cacd35c62d970d13bfd3a8664f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.6439 | 7.04 | 500 | 3.3083 | 1.0 | | 2.3763 | 14.08 | 1000 | 1.5059 | 0.8146 | | 1.0161 | 21.13 | 1500 | 1.5101 | 0.7488 | | 0.6195 | 28.17 | 2000 | 1.6269 | 0.7418 | | f20ba01de8becb77cc0b95c63ba2099d |
creativeml-openrail-m | ['text-to-image'] | false | A stable diffusion model used to generate Marco's pictures by the prompt **'mkmk woman'** Based on runwayml/stable-diffusion-v1-5 trained by Dreambooth Trained on 39 pics, 3000 steps What is Marco like? <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/IMG_2683.jpeg" width="512" height="512"/> <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/IMG_0537.jpeg" width="512" height="512"/> Some samples generated by this model: <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/0.png" width="512" height="512"/> <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/1.png" width="512" height="512"/> <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/2.png" width="512" height="512"/> <img src="https://huggingface.co/AkiKagura/mkgen-diffusion/resolve/main/samples/3.png" width="512" height="512"/> | 6591fac356e34dc6adc7fe3ef6568cc5 |
mit | [] | false | Trigger Studio on Stable Diffusion This is the `<Trigger Studio>` 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 your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:                  | 55a2957cb61cbf83bc12e16f292613ba |
apache-2.0 | ['image-classification', 'pytorch'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/cspdarknet53").eval() img = Image.open(path_to_an_image).convert("RGB") | fa8d7f1e3cd1dd6c1ad3cd5594ba8616 |
apache-2.0 | ['image-classification', 'pytorch'] | false | Citation Original paper ```bibtex @article{DBLP:journals/corr/abs-1911-11929, author = {Chien{-}Yao Wang and Hong{-}Yuan Mark Liao and I{-}Hau Yeh and Yueh{-}Hua Wu and Ping{-}Yang Chen and Jun{-}Wei Hsieh}, title = {CSPNet: {A} New Backbone that can Enhance Learning Capability of {CNN}}, journal = {CoRR}, volume = {abs/1911.11929}, year = {2019}, url = {http://arxiv.org/abs/1911.11929}, eprinttype = {arXiv}, eprint = {1911.11929}, timestamp = {Tue, 03 Dec 2019 20:41:07 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1911-11929.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Source of this implementation ```bibtex @software{Fernandez_Holocron_2020, author = {Fernandez, François-Guillaume}, month = {5}, title = {{Holocron}}, url = {https://github.com/frgfm/Holocron}, year = {2020} } ``` | da27b0add15e0623d1850a21fbaed058 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_50v6_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v6_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6728 - Precision: 0.0625 - Recall: 0.0005 - F1: 0.0010 - Accuracy: 0.7775 | ade5de7a6b1123f98a61bc450175bbd7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 16 | 0.7728 | 0.0 | 0.0 | 0.0 | 0.7773 | | No log | 2.0 | 32 | 0.6898 | 0.04 | 0.0002 | 0.0005 | 0.7774 | | No log | 3.0 | 48 | 0.6728 | 0.0625 | 0.0005 | 0.0010 | 0.7775 | | 8241b0a4f4edb24f36b01fef6976f32a |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-10front-1body-10rear 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: 0.4366 - Wer: 0.1693 - Mer: 0.1636 - Wil: 0.2493 - Wip: 0.7507 - Hits: 55904 - Substitutions: 6304 - Deletions: 2379 - Insertions: 2249 - Cer: 0.1332 | b1c28fd9fa21d89d76b07dcb774937d7 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 | e27f315ffac778a7a2de389a41270179 |
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 | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6166 | 1.0 | 1457 | 0.4595 | 0.2096 | 0.1979 | 0.2878 | 0.7122 | 54866 | 6757 | 2964 | 3819 | 0.1793 | | 0.4985 | 2.0 | 2914 | 0.4190 | 0.1769 | 0.1710 | 0.2587 | 0.7413 | 55401 | 6467 | 2719 | 2241 | 0.1417 | | 0.4787 | 3.0 | 4371 | 0.4130 | 0.1728 | 0.1670 | 0.2534 | 0.7466 | 55677 | 6357 | 2553 | 2249 | 0.1368 | | 0.4299 | 4.0 | 5828 | 0.4085 | 0.1726 | 0.1665 | 0.2530 | 0.7470 | 55799 | 6381 | 2407 | 2357 | 0.1348 | | 0.3855 | 5.0 | 7285 | 0.4130 | 0.1702 | 0.1644 | 0.2501 | 0.7499 | 55887 | 6309 | 2391 | 2292 | 0.1336 | | 0.3109 | 6.0 | 8742 | 0.4182 | 0.1732 | 0.1668 | 0.2525 | 0.7475 | 55893 | 6317 | 2377 | 2494 | 0.1450 | | 0.3027 | 7.0 | 10199 | 0.4256 | 0.1691 | 0.1633 | 0.2486 | 0.7514 | 55949 | 6273 | 2365 | 2283 | 0.1325 | | 0.2729 | 8.0 | 11656 | 0.4252 | 0.1709 | 0.1649 | 0.2503 | 0.7497 | 55909 | 6283 | 2395 | 2362 | 0.1375 | | 0.2531 | 9.0 | 13113 | 0.4329 | 0.1696 | 0.1639 | 0.2499 | 0.7501 | 55870 | 6322 | 2395 | 2235 | 0.1334 | | 0.2388 | 10.0 | 14570 | 0.4366 | 0.1693 | 0.1636 | 0.2493 | 0.7507 | 55904 | 6304 | 2379 | 2249 | 0.1332 | | 9d703124663008b8ec6749436795004b |
mit | [] | false | kairuno on Stable Diffusion This is the `kairuno` 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 your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:              | a9ad20a55798497ccfc129eb7a871b17 |
apache-2.0 | ['translation'] | false | Download the pretrained model for English-Vietnamese available on the hub model = AutoModelForSeq2SeqLM.from_pretrained("CLAck/indo-mixed") tokenizer = AutoTokenizer.from_pretrained("CLAck/indo-mixed") | 3c4eefa610d09f7381d0a56beee329bc |
apache-2.0 | ['translation'] | false | This token is needed to identify the target language input_sentence = "<2indo> " + sentence translated = model.generate(**tokenizer_en(input_sentence, return_tensors="pt", padding=True)) output_sentence = [tokenizer.decode(t, skip_special_tokens=True) for t in translated] ``` | c753be371652ef76c3e7173349e1a5e0 |
apache-2.0 | ['translation'] | false | Training results MIXED | Epoch | Bleu | |:-----:|:-------:| | 1.0 | 24.2579 | | 2.0 | 30.6287 | | 3.0 | 34.4417 | | 4.0 | 36.2577 | | 5.0 | 37.3488 | FINETUNING | Epoch | Bleu | |:-----:|:-------:| | 6.0 | 34.1676 | | 7.0 | 35.2320 | | 8.0 | 36.7110 | | 9.0 | 37.3195 | | 10.0 | 37.9461 | | 80833135065d3acc029c49e4b823faab |
mit | ['generated_from_trainer'] | false | paraphraser-spanish-t5-small This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 1.1079 - eval_runtime: 4.9573 - eval_samples_per_second: 365.924 - eval_steps_per_second: 36.713 - epoch: 0.83 - step: 43141 | 5f76881cfc061bb2c45729c0657a37f6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 35400bdc5d0db1bde9a28a542dc4850a |
apache-2.0 | ['text2text-generation'] | false | TL;DR If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large). | a0c3330108d3e559cb4855a55ab0c0fb |
apache-2.0 | ['text2text-generation'] | false | Model Description - **Model type:** Language model - **Language(s) (NLP):** English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian - **License:** Apache 2.0 - **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5) - **Original Checkpoints:** [All Original FLAN-T5 Checkpoints](https://github.com/google-research/t5x/blob/main/docs/models.md | 6974152edc627d90403f6fe5888a4462 |
apache-2.0 | ['text2text-generation'] | false | flan-t5-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2210.11416.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face FLAN-T5 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/t5) | b3ffb41287eea97a15ff90b56c9a9742 |
apache-2.0 | ['text2text-generation'] | false | Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> | fa5749823d9aa001ced4f693818608f7 |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto") input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> | 8e766aad92520348749aed96de83cbdb |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> | ab2b82ec81492b11850637fc15f632f3 |
apache-2.0 | ['text2text-generation'] | false | pip install bitsandbytes accelerate from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl") model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", load_in_8bit=True) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` </details> | e539dba7a31902435f5f4f83b1ae718a |
apache-2.0 | ['text2text-generation'] | false | Direct Use and Downstream Use The authors write in [the original paper's model card](https://arxiv.org/pdf/2210.11416.pdf) that: > The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models See the [research paper](https://arxiv.org/pdf/2210.11416.pdf) for further details. | a8f17f0f8cb4cfcf535f80f4d564ffc1 |
apache-2.0 | ['text2text-generation'] | false | Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. | 1a2c222a686143d3b784293577c34957 |
apache-2.0 | ['text2text-generation'] | false | Ethical considerations and risks > Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. | f6969a18d07ffef4e18389d5791f0904 |
apache-2.0 | ['text2text-generation'] | false | Training Data The model was trained on a mixture of tasks, that includes the tasks described in the table below (from the original paper, figure 2):  | 0f4d86bae2f890c269b4413e1d1dcbb7 |
apache-2.0 | ['text2text-generation'] | false | Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2210.11416.pdf): > These models are based on pretrained T5 (Raffel et al., 2020) and fine-tuned with instructions for better zero-shot and few-shot performance. There is one fine-tuned Flan model per T5 model size. The model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). | d44e0bf60fcc81cd01322f1e3d8f3b11 |
apache-2.0 | ['text2text-generation'] | false | Testing Data, Factors & Metrics The authors evaluated the model on various tasks covering several languages (1836 in total). See the table below for some quantitative evaluation:  For full details, please check the [research paper](https://arxiv.org/pdf/2210.11416.pdf). | e2b526659eb83c373b52a9821edab3a6 |
apache-2.0 | ['text2text-generation'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Google Cloud TPU Pods - TPU v3 or TPU v4 | Number of chips ≥ 4. - **Hours used:** More information needed - **Cloud Provider:** GCP - **Compute Region:** More information needed - **Carbon Emitted:** More information needed | 8455f15ea3ecfaf4002185e59983163c |
apache-2.0 | ['text2text-generation'] | false | Citation **BibTeX:** ```bibtex @misc{https://doi.org/10.48550/arxiv.2210.11416, doi = {10.48550/ARXIV.2210.11416}, url = {https://arxiv.org/abs/2210.11416}, author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Scaling Instruction-Finetuned Language Models}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` | 8c27ed0b0f16758233655b7f7ebf16b0 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-beans-demo-v5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0427 - Accuracy: 0.9925 | e19e2e26b4850f83608361bcb3a15242 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1378 | 1.54 | 100 | 0.1444 | 0.9549 | | 0.0334 | 3.08 | 200 | 0.0427 | 0.9925 | | e456076a1ac2ee87c978eb4c0c4f52ce |
mit | ['image-to-text'] | false | Vit2-DistilGPT2 This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training) | b1b0009af7e2384b9d3b393a31ad5580 |
mit | ['image-to-text'] | false | Usage ```python import Image from transformers import AutoModel, GPT2Tokenizer, ViTFeatureExtractor model = AutoModel.from_pretrained("sachin/vit2distilgpt2") vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") | a4b2e13d527fe9e0c3b342c3be312e05 |
mit | ['image-to-text'] | false | make sure GPT2 appends EOS in begin and end def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] return outputs GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens gpt2_tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") | 3d3e0b256da08dc2d5ade93424f2e021 |
mit | ['image-to-text'] | false | set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token image = (Image.open(image_path).convert("RGB"), return_tensors="pt").pixel_values encoder_outputs = model.generate(image.unsqueeze(0)) generated_sentences = gpt2_tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True) ``` Note that the output sentence may be repeated, hence a post processing step may be required. | 0a736959438db4dec0d35ffe2c4fadee |
agpl-3.0 | ['art'] | false | Introduction: - It's an AI art model for converting text to images, images to images, inpainting, and outpainting using Stable Diffusion. - The AI art model is developed with a focus on the ability to draw anime characters relatively well through fine-tuning using Dreambooth. - The model is aimed at everyone and has limitless usage potential. | 4fbc4e4fda6f97576f95ac27fdd2b7db |
agpl-3.0 | ['art'] | false | Used: - You can use it with any library that is supported, I recommend using the "stable-diffusion-web-ui" by Automatic1111. - You should use it as a supportive tool for creating works of art, and not rely on it completely. - It can be used as a tool for upscaling or rendering anime-style images from 3D modeling software (Blender). - Create an image from a sketch you created from a pure drawing program. (MS Paint) - The `masterpiece` and `best quality` tags are not necessary, as it sometimes leads to contradictory results, but if it is distorted or discolored, add them now. | 488e110829fcc6d1d370f622990ef127 |
agpl-3.0 | ['art'] | false | Training: - **Data**: The model is trained based on a database of various sources from the Internet provided by my friend and images created by another AI. - **Schedule**: Euler Ancestral Discrete. - **Optimizer**: AdamW. - **Precision**: BF16. - **Hardware**: Google Colaboratory Pro - NVIDIA A100 40GB VRAM. | cfa31f7a815ef0548b6030d10443235c |
agpl-3.0 | ['art'] | false | **Limitations:** - Loss of detail, errors, bad human-like (six-fingered hand) details, deformation, blurring, and unclear images are inevitable. - Complex tasks cannot be handled. - ⚠️Content may not be appropriate for all ages: As it is trained on data that includes adult content, the generated images may contain content not suitable for children (depending on your country there will be a specific regulation about it). If you do not want to appear adult content, make sure you have additional safety measures in place, such as adding "nsfw" to the negative prompt. - The results generated by the model are considered impressive. But unfortunately, currently, it only supports the English language, to use multilingual, consider using third-party translation programs. - The model is trained on the `Danbooru` and `Nai` tagging system, so the long text may result in poor results. - Dark, grayscale, white balance loss. The fix is to use image editing software like Photoshop. In the future, I need a more colorful dataset. - My amount of money: 0 USD =((.  | d2a6142efaa92cd31afd1b8e1757ad07 |
agpl-3.0 | ['art'] | false | <p style="color:red">⚠️Prohibited behaviors:<p> - Using for political, terrorist, subversive, racist, disrespectful of law, and lawless purposes. - Stealing, copying, or reproducing someone else's work without permission for commercial or malicious purposes. - Spreading false information. | ccbddc407405001b7ec7d66aa77e65f4 |
agpl-3.0 | ['art'] | false | **Desires:** As it is a version made only by myself and my small associates, the model will not be perfect and may differ from what people expect. Any contributions from everyone will be respected. Want to support me? Thank you, please help me make it better. ❤️ | 1d9e91c4737dcc4bc3806df0b1be62b8 |
agpl-3.0 | ['art'] | false | Special Thank: This wouldn't have happened if they hadn't made a breakthrough. - [Runwayml](https://huggingface.co/runwayml/): Base model. - [d8ahazard](https://github.com/d8ahazard/.sd_dreambooth_extension) : Dreambooth. - [Automatic1111](https://github.com/AUTOMATIC1111/) : Web UI. - [Mikubill](https://github.com/Mikubill/): Where my ideas started. - Chat-GPT: Help me do crazy things that I thought I would never do. - Novel AI: Dataset images. An AI made me thousands of pictures without worrying about copyright or dispute. - Danbooru: Help me write the correct tag. - My friend and others. - YOU! Yes, is you 🫵 | 5b1624d5c4f52a03aaa5b1b2cef4aa7a |
agpl-3.0 | ['art'] | false | Copyright: This license allows anyone to copy, modify, publish, and commercialize the model, but please follow the terms of the GNU General Public License. You can learn more about the GNU General Public License at [here](LICENSE.txt). If any part of the model does not comply with the terms of the GNU General Public License, the copyright and other rights of the model will still be valid. We will not be held responsible for any legal issues you cause. Don't forget me. | a3c87d4bb96b31b769049555dcb6c486 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Usage ```sh pip install transformers accelerate>=0.14.0 diffusers>=0.7.2 ``` ```python import torch from diffusers import StableDiffusionPipeline repo = "Bingsu/my-k-anything-v3-0" pipe = StableDiffusionPipeline.from_pretrained( repo, torch_dtype=torch.float16, ) pipe.to("cuda") pipe.safety_checker = None ``` ```python from typing import Optional import torch def gen_image( prompt: str, negative_prompt: Optional[str] = None, seed: Optional[int] = None, scale: float = 7.5, steps: int = 30, ): if seed is not None: generator = torch.Generator("cuda").manual_seed(seed) else: generator = None image = pipe( prompt=prompt, negative_prompt=negative_prompt, generator=generator, guidance_scale=scale, num_inference_steps=steps, ).images[0] return image ``` ```python prompt = "파란색 포니테일 헤어, 브로치, 정장을 입은 성인 여성, 고퀄리티, 최고품질" negative = "저화질, 저품질, 텍스트" seed = 42467781 scale = 12.0 gen_image(prompt, negative, seed, scale) ```  | 340b8782c9d29e8e3a63a87c545957e9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | d6e50bc1c017dbfcc434ccc32ab91dbb |
apache-2.0 | [] | false | Model Description This model is fine-tuned version of [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large). The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spellchecker/ml_ranging/models/med_ru_roberta_large/fine_tune_ru_roberta_large.py). The model is fine-tuned on a specially collected dataset of over 30,000 medical anamneses in Russian. The collected dataset can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/data/anamnesis/processed/all_anamnesis.csv). This model was created as part of a master's project to develop a method for correcting typos in medical histories using BERT models as a ranking of candidates. The project is open source and can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker). | 8330856140b283034dac4f3e5676d004 |
apache-2.0 | [] | false | How to Get Started With the Model You can use the model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> pipeline = pipeline('fill-mask', model='DmitryPogrebnoy/MedRuRobertaLarge') >>> pipeline("У пациента <mask> боль в грудине.") [{'score': 0.2467374950647354, 'token': 9233, 'token_str': ' сильный', 'sequence': 'У пациента сильный боль в грудине.'}, {'score': 0.16476310789585114, 'token': 27876, 'token_str': ' постоянный', 'sequence': 'У пациента постоянный боль в грудине.'}, {'score': 0.07211139053106308, 'token': 19551, 'token_str': ' острый', 'sequence': 'У пациента острый боль в грудине.'}, {'score': 0.0616639070212841, 'token': 18840, 'token_str': ' сильная', 'sequence': 'У пациента сильная боль в грудине.'}, {'score': 0.029712719842791557, 'token': 40176, 'token_str': ' острая', 'sequence': 'У пациента острая боль в грудине.'}] ``` Or you can load the model and tokenizer and do what you need to do: ```python >>> from transformers import AutoTokenizer, AutoModelForMaskedLM >>> tokenizer = AutoTokenizer.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge") >>> model = AutoModelForMaskedLM.from_pretrained("DmitryPogrebnoy/MedRuRobertaLarge") ``` | 6037ffb0dd9fdb8ac2679b331eb8733f |
apache-2.0 | [] | false | Zabanshenas - Language Detector Zabanshenas is a Transformer-based solution for identifying the most likely language of a written document/text. Zabanshenas is a Persian word that has two meanings: - A person who studies linguistics. - A way to identify the type of written language. | a7880e1008a059063a24f39c463f2078 |
apache-2.0 | [] | false | By Paragraph | language | precision | recall | f1-score | |:--------------------------------------:|:---------:|:--------:|:--------:| | Achinese (ace) | 1.000000 | 0.982143 | 0.990991 | | Afrikaans (afr) | 1.000000 | 1.000000 | 1.000000 | | Alemannic German (als) | 1.000000 | 0.946429 | 0.972477 | | Amharic (amh) | 1.000000 | 0.982143 | 0.990991 | | Old English (ang) | 0.981818 | 0.964286 | 0.972973 | | Arabic (ara) | 0.846154 | 0.982143 | 0.909091 | | Aragonese (arg) | 1.000000 | 1.000000 | 1.000000 | | Egyptian Arabic (arz) | 0.979592 | 0.857143 | 0.914286 | | Assamese (asm) | 0.981818 | 0.964286 | 0.972973 | | Asturian (ast) | 0.964912 | 0.982143 | 0.973451 | | Avar (ava) | 0.941176 | 0.905660 | 0.923077 | | Aymara (aym) | 0.964912 | 0.982143 | 0.973451 | | South Azerbaijani (azb) | 0.965517 | 1.000000 | 0.982456 | | Azerbaijani (aze) | 1.000000 | 1.000000 | 1.000000 | | Bashkir (bak) | 1.000000 | 0.978261 | 0.989011 | | Bavarian (bar) | 0.843750 | 0.964286 | 0.900000 | | Central Bikol (bcl) | 1.000000 | 0.982143 | 0.990991 | | Belarusian (Taraschkewiza) (be-tarask) | 1.000000 | 0.875000 | 0.933333 | | Belarusian (bel) | 0.870968 | 0.964286 | 0.915254 | | Bengali (ben) | 0.982143 | 0.982143 | 0.982143 | | Bhojpuri (bho) | 1.000000 | 0.928571 | 0.962963 | | Banjar (bjn) | 0.981132 | 0.945455 | 0.962963 | | Tibetan (bod) | 1.000000 | 0.982143 | 0.990991 | | Bosnian (bos) | 0.552632 | 0.375000 | 0.446809 | | Bishnupriya (bpy) | 1.000000 | 0.982143 | 0.990991 | | Breton (bre) | 1.000000 | 0.964286 | 0.981818 | | Bulgarian (bul) | 1.000000 | 0.964286 | 0.981818 | | Buryat (bxr) | 0.946429 | 0.946429 | 0.946429 | | Catalan (cat) | 0.982143 | 0.982143 | 0.982143 | | Chavacano (cbk) | 0.914894 | 0.767857 | 0.834951 | | Min Dong (cdo) | 1.000000 | 0.982143 | 0.990991 | | Cebuano (ceb) | 1.000000 | 1.000000 | 1.000000 | | Czech (ces) | 1.000000 | 1.000000 | 1.000000 | | Chechen (che) | 1.000000 | 1.000000 | 1.000000 | | Cherokee (chr) | 1.000000 | 0.963636 | 0.981481 | | Chuvash (chv) | 0.938776 | 0.958333 | 0.948454 | | Central Kurdish (ckb) | 1.000000 | 1.000000 | 1.000000 | | Cornish (cor) | 1.000000 | 1.000000 | 1.000000 | | Corsican (cos) | 1.000000 | 0.982143 | 0.990991 | | Crimean Tatar (crh) | 1.000000 | 0.946429 | 0.972477 | | Kashubian (csb) | 1.000000 | 0.963636 | 0.981481 | | Welsh (cym) | 1.000000 | 1.000000 | 1.000000 | | Danish (dan) | 1.000000 | 1.000000 | 1.000000 | | German (deu) | 0.828125 | 0.946429 | 0.883333 | | Dimli (diq) | 0.964912 | 0.982143 | 0.973451 | | Dhivehi (div) | 1.000000 | 1.000000 | 1.000000 | | Lower Sorbian (dsb) | 1.000000 | 0.982143 | 0.990991 | | Doteli (dty) | 0.940000 | 0.854545 | 0.895238 | | Emilian (egl) | 1.000000 | 0.928571 | 0.962963 | | Modern Greek (ell) | 1.000000 | 1.000000 | 1.000000 | | English (eng) | 0.588889 | 0.946429 | 0.726027 | | Esperanto (epo) | 1.000000 | 0.982143 | 0.990991 | | Estonian (est) | 0.963636 | 0.946429 | 0.954955 | | Basque (eus) | 1.000000 | 0.982143 | 0.990991 | | Extremaduran (ext) | 0.982143 | 0.982143 | 0.982143 | | Faroese (fao) | 1.000000 | 1.000000 | 1.000000 | | Persian (fas) | 0.948276 | 0.982143 | 0.964912 | | Finnish (fin) | 1.000000 | 1.000000 | 1.000000 | | French (fra) | 0.710145 | 0.875000 | 0.784000 | | Arpitan (frp) | 1.000000 | 0.946429 | 0.972477 | | Western Frisian (fry) | 0.982143 | 0.982143 | 0.982143 | | Friulian (fur) | 1.000000 | 0.982143 | 0.990991 | | Gagauz (gag) | 0.981132 | 0.945455 | 0.962963 | | Scottish Gaelic (gla) | 0.982143 | 0.982143 | 0.982143 | | Irish (gle) | 0.949153 | 1.000000 | 0.973913 | | Galician (glg) | 1.000000 | 1.000000 | 1.000000 | | Gilaki (glk) | 0.981132 | 0.945455 | 0.962963 | | Manx (glv) | 1.000000 | 1.000000 | 1.000000 | | Guarani (grn) | 1.000000 | 0.964286 | 0.981818 | | Gujarati (guj) | 1.000000 | 0.982143 | 0.990991 | | Hakka Chinese (hak) | 0.981818 | 0.964286 | 0.972973 | | Haitian Creole (hat) | 1.000000 | 1.000000 | 1.000000 | | Hausa (hau) | 1.000000 | 0.945455 | 0.971963 | | Serbo-Croatian (hbs) | 0.448276 | 0.464286 | 0.456140 | | Hebrew (heb) | 1.000000 | 0.982143 | 0.990991 | | Fiji Hindi (hif) | 0.890909 | 0.890909 | 0.890909 | | Hindi (hin) | 0.981481 | 0.946429 | 0.963636 | | Croatian (hrv) | 0.500000 | 0.636364 | 0.560000 | | Upper Sorbian (hsb) | 0.955556 | 1.000000 | 0.977273 | | Hungarian (hun) | 1.000000 | 1.000000 | 1.000000 | | Armenian (hye) | 1.000000 | 0.981818 | 0.990826 | | Igbo (ibo) | 0.918033 | 1.000000 | 0.957265 | | Ido (ido) | 1.000000 | 1.000000 | 1.000000 | | Interlingue (ile) | 1.000000 | 0.962264 | 0.980769 | | Iloko (ilo) | 0.947368 | 0.964286 | 0.955752 | | Interlingua (ina) | 1.000000 | 1.000000 | 1.000000 | | Indonesian (ind) | 0.761905 | 0.872727 | 0.813559 | | Icelandic (isl) | 1.000000 | 1.000000 | 1.000000 | | Italian (ita) | 0.861538 | 1.000000 | 0.925620 | | Jamaican Patois (jam) | 1.000000 | 0.946429 | 0.972477 | | Javanese (jav) | 0.964912 | 0.982143 | 0.973451 | | Lojban (jbo) | 1.000000 | 1.000000 | 1.000000 | | Japanese (jpn) | 1.000000 | 1.000000 | 1.000000 | | Karakalpak (kaa) | 0.965517 | 1.000000 | 0.982456 | | Kabyle (kab) | 1.000000 | 0.964286 | 0.981818 | | Kannada (kan) | 0.982143 | 0.982143 | 0.982143 | | Georgian (kat) | 1.000000 | 0.964286 | 0.981818 | | Kazakh (kaz) | 0.980769 | 0.980769 | 0.980769 | | Kabardian (kbd) | 1.000000 | 0.982143 | 0.990991 | | Central Khmer (khm) | 0.960784 | 0.875000 | 0.915888 | | Kinyarwanda (kin) | 0.981132 | 0.928571 | 0.954128 | | Kirghiz (kir) | 1.000000 | 1.000000 | 1.000000 | | Komi-Permyak (koi) | 0.962264 | 0.910714 | 0.935780 | | Konkani (kok) | 0.964286 | 0.981818 | 0.972973 | | Komi (kom) | 1.000000 | 0.962264 | 0.980769 | | Korean (kor) | 1.000000 | 1.000000 | 1.000000 | | Karachay-Balkar (krc) | 1.000000 | 0.982143 | 0.990991 | | Ripuarisch (ksh) | 1.000000 | 0.964286 | 0.981818 | | Kurdish (kur) | 1.000000 | 0.964286 | 0.981818 | | Ladino (lad) | 1.000000 | 1.000000 | 1.000000 | | Lao (lao) | 0.961538 | 0.909091 | 0.934579 | | Latin (lat) | 0.877193 | 0.943396 | 0.909091 | | Latvian (lav) | 0.963636 | 0.946429 | 0.954955 | | Lezghian (lez) | 1.000000 | 0.964286 | 0.981818 | | Ligurian (lij) | 1.000000 | 0.964286 | 0.981818 | | Limburgan (lim) | 0.938776 | 1.000000 | 0.968421 | | Lingala (lin) | 0.980769 | 0.927273 | 0.953271 | | Lithuanian (lit) | 0.982456 | 1.000000 | 0.991150 | | Lombard (lmo) | 1.000000 | 1.000000 | 1.000000 | | Northern Luri (lrc) | 1.000000 | 0.928571 | 0.962963 | | Latgalian (ltg) | 1.000000 | 0.982143 | 0.990991 | | Luxembourgish (ltz) | 0.949153 | 1.000000 | 0.973913 | | Luganda (lug) | 1.000000 | 1.000000 | 1.000000 | | Literary Chinese (lzh) | 1.000000 | 1.000000 | 1.000000 | | Maithili (mai) | 0.931034 | 0.964286 | 0.947368 | | Malayalam (mal) | 1.000000 | 0.982143 | 0.990991 | | Banyumasan (map-bms) | 0.977778 | 0.785714 | 0.871287 | | Marathi (mar) | 0.949153 | 1.000000 | 0.973913 | | Moksha (mdf) | 0.980000 | 0.890909 | 0.933333 | | Eastern Mari (mhr) | 0.981818 | 0.964286 | 0.972973 | | Minangkabau (min) | 1.000000 | 1.000000 | 1.000000 | | Macedonian (mkd) | 1.000000 | 0.981818 | 0.990826 | | Malagasy (mlg) | 0.981132 | 1.000000 | 0.990476 | | Maltese (mlt) | 0.982456 | 1.000000 | 0.991150 | | Min Nan Chinese (nan) | 1.000000 | 1.000000 | 1.000000 | | Mongolian (mon) | 1.000000 | 0.981818 | 0.990826 | | Maori (mri) | 1.000000 | 1.000000 | 1.000000 | | Western Mari (mrj) | 0.982456 | 1.000000 | 0.991150 | | Malay (msa) | 0.862069 | 0.892857 | 0.877193 | | Mirandese (mwl) | 1.000000 | 0.982143 | 0.990991 | | Burmese (mya) | 1.000000 | 1.000000 | 1.000000 | | Erzya (myv) | 0.818182 | 0.964286 | 0.885246 | | Mazanderani (mzn) | 0.981481 | 1.000000 | 0.990654 | | Neapolitan (nap) | 1.000000 | 0.981818 | 0.990826 | | Navajo (nav) | 1.000000 | 1.000000 | 1.000000 | | Classical Nahuatl (nci) | 0.981481 | 0.946429 | 0.963636 | | Low German (nds) | 0.982143 | 0.982143 | 0.982143 | | West Low German (nds-nl) | 1.000000 | 1.000000 | 1.000000 | | Nepali (macrolanguage) (nep) | 0.881356 | 0.928571 | 0.904348 | | Newari (new) | 1.000000 | 0.909091 | 0.952381 | | Dutch (nld) | 0.982143 | 0.982143 | 0.982143 | | Norwegian Nynorsk (nno) | 1.000000 | 1.000000 | 1.000000 | | Bokmål (nob) | 1.000000 | 1.000000 | 1.000000 | | Narom (nrm) | 0.981818 | 0.964286 | 0.972973 | | Northern Sotho (nso) | 1.000000 | 1.000000 | 1.000000 | | Occitan (oci) | 0.903846 | 0.839286 | 0.870370 | | Livvi-Karelian (olo) | 0.982456 | 1.000000 | 0.991150 | | Oriya (ori) | 0.964912 | 0.982143 | 0.973451 | | Oromo (orm) | 0.982143 | 0.982143 | 0.982143 | | Ossetian (oss) | 0.982143 | 1.000000 | 0.990991 | | Pangasinan (pag) | 0.980000 | 0.875000 | 0.924528 | | Pampanga (pam) | 0.928571 | 0.896552 | 0.912281 | | Panjabi (pan) | 1.000000 | 1.000000 | 1.000000 | | Papiamento (pap) | 1.000000 | 0.964286 | 0.981818 | | Picard (pcd) | 0.849057 | 0.849057 | 0.849057 | | Pennsylvania German (pdc) | 0.854839 | 0.946429 | 0.898305 | | Palatine German (pfl) | 0.946429 | 0.946429 | 0.946429 | | Western Panjabi (pnb) | 0.981132 | 0.962963 | 0.971963 | | Polish (pol) | 0.933333 | 1.000000 | 0.965517 | | Portuguese (por) | 0.774648 | 0.982143 | 0.866142 | | Pushto (pus) | 1.000000 | 0.910714 | 0.953271 | | Quechua (que) | 0.962963 | 0.928571 | 0.945455 | | Tarantino dialect (roa-tara) | 1.000000 | 0.964286 | 0.981818 | | Romansh (roh) | 1.000000 | 0.928571 | 0.962963 | | Romanian (ron) | 0.965517 | 1.000000 | 0.982456 | | Rusyn (rue) | 0.946429 | 0.946429 | 0.946429 | | Aromanian (rup) | 0.962963 | 0.928571 | 0.945455 | | Russian (rus) | 0.859375 | 0.982143 | 0.916667 | | Yakut (sah) | 1.000000 | 0.982143 | 0.990991 | | Sanskrit (san) | 0.982143 | 0.982143 | 0.982143 | | Sicilian (scn) | 1.000000 | 1.000000 | 1.000000 | | Scots (sco) | 0.982143 | 0.982143 | 0.982143 | | Samogitian (sgs) | 1.000000 | 0.982143 | 0.990991 | | Sinhala (sin) | 0.964912 | 0.982143 | 0.973451 | | Slovak (slk) | 1.000000 | 0.982143 | 0.990991 | | Slovene (slv) | 1.000000 | 0.981818 | 0.990826 | | Northern Sami (sme) | 0.962264 | 0.962264 | 0.962264 | | Shona (sna) | 0.933333 | 1.000000 | 0.965517 | | Sindhi (snd) | 1.000000 | 1.000000 | 1.000000 | | Somali (som) | 0.948276 | 1.000000 | 0.973451 | | Spanish (spa) | 0.739130 | 0.910714 | 0.816000 | | Albanian (sqi) | 0.982143 | 0.982143 | 0.982143 | | Sardinian (srd) | 1.000000 | 0.982143 | 0.990991 | | Sranan (srn) | 1.000000 | 1.000000 | 1.000000 | | Serbian (srp) | 1.000000 | 0.946429 | 0.972477 | | Saterfriesisch (stq) | 1.000000 | 0.964286 | 0.981818 | | Sundanese (sun) | 1.000000 | 0.977273 | 0.988506 | | Swahili (macrolanguage) (swa) | 1.000000 | 1.000000 | 1.000000 | | Swedish (swe) | 1.000000 | 1.000000 | 1.000000 | | Silesian (szl) | 1.000000 | 0.981481 | 0.990654 | | Tamil (tam) | 0.982143 | 1.000000 | 0.990991 | | Tatar (tat) | 1.000000 | 1.000000 | 1.000000 | | Tulu (tcy) | 0.982456 | 1.000000 | 0.991150 | | Telugu (tel) | 1.000000 | 0.920000 | 0.958333 | | Tetum (tet) | 1.000000 | 0.964286 | 0.981818 | | Tajik (tgk) | 1.000000 | 1.000000 | 1.000000 | | Tagalog (tgl) | 1.000000 | 1.000000 | 1.000000 | | Thai (tha) | 0.932203 | 0.982143 | 0.956522 | | Tongan (ton) | 1.000000 | 0.964286 | 0.981818 | | Tswana (tsn) | 1.000000 | 1.000000 | 1.000000 | | Turkmen (tuk) | 1.000000 | 0.982143 | 0.990991 | | Turkish (tur) | 0.901639 | 0.982143 | 0.940171 | | Tuvan (tyv) | 1.000000 | 0.964286 | 0.981818 | | Udmurt (udm) | 1.000000 | 0.982143 | 0.990991 | | Uighur (uig) | 1.000000 | 0.982143 | 0.990991 | | Ukrainian (ukr) | 0.963636 | 0.946429 | 0.954955 | | Urdu (urd) | 1.000000 | 0.982143 | 0.990991 | | Uzbek (uzb) | 1.000000 | 1.000000 | 1.000000 | | Venetian (vec) | 1.000000 | 0.982143 | 0.990991 | | Veps (vep) | 0.982456 | 1.000000 | 0.991150 | | Vietnamese (vie) | 0.964912 | 0.982143 | 0.973451 | | Vlaams (vls) | 1.000000 | 0.982143 | 0.990991 | | Volapük (vol) | 1.000000 | 1.000000 | 1.000000 | | Võro (vro) | 0.964286 | 0.964286 | 0.964286 | | Waray (war) | 1.000000 | 0.982143 | 0.990991 | | Walloon (wln) | 1.000000 | 1.000000 | 1.000000 | | Wolof (wol) | 0.981481 | 0.963636 | 0.972477 | | Wu Chinese (wuu) | 0.981481 | 0.946429 | 0.963636 | | Xhosa (xho) | 1.000000 | 0.964286 | 0.981818 | | Mingrelian (xmf) | 1.000000 | 0.964286 | 0.981818 | | Yiddish (yid) | 1.000000 | 1.000000 | 1.000000 | | Yoruba (yor) | 0.964912 | 0.982143 | 0.973451 | | Zeeuws (zea) | 1.000000 | 0.982143 | 0.990991 | | Cantonese (zh-yue) | 0.981481 | 0.946429 | 0.963636 | | Standard Chinese (zho) | 0.932203 | 0.982143 | 0.956522 | | accuracy | 0.963055 | 0.963055 | 0.963055 | | macro avg | 0.966424 | 0.963216 | 0.963891 | | weighted avg | 0.966040 | 0.963055 | 0.963606 | | 194af85c38752debe1c396b97953b784 |
apache-2.0 | [] | false | By Sentence | language | precision | recall | f1-score | |:--------------------------------------:|:---------:|:--------:|:--------:| | Achinese (ace) | 0.754545 | 0.873684 | 0.809756 | | Afrikaans (afr) | 0.708955 | 0.940594 | 0.808511 | | Alemannic German (als) | 0.870130 | 0.752809 | 0.807229 | | Amharic (amh) | 1.000000 | 0.820000 | 0.901099 | | Old English (ang) | 0.966667 | 0.906250 | 0.935484 | | Arabic (ara) | 0.907692 | 0.967213 | 0.936508 | | Aragonese (arg) | 0.921569 | 0.959184 | 0.940000 | | Egyptian Arabic (arz) | 0.964286 | 0.843750 | 0.900000 | | Assamese (asm) | 0.964286 | 0.870968 | 0.915254 | | Asturian (ast) | 0.880000 | 0.795181 | 0.835443 | | Avar (ava) | 0.864198 | 0.843373 | 0.853659 | | Aymara (aym) | 1.000000 | 0.901961 | 0.948454 | | South Azerbaijani (azb) | 0.979381 | 0.989583 | 0.984456 | | Azerbaijani (aze) | 0.989899 | 0.960784 | 0.975124 | | Bashkir (bak) | 0.837209 | 0.857143 | 0.847059 | | Bavarian (bar) | 0.741935 | 0.766667 | 0.754098 | | Central Bikol (bcl) | 0.962963 | 0.928571 | 0.945455 | | Belarusian (Taraschkewiza) (be-tarask) | 0.857143 | 0.733333 | 0.790419 | | Belarusian (bel) | 0.775510 | 0.752475 | 0.763819 | | Bengali (ben) | 0.861111 | 0.911765 | 0.885714 | | Bhojpuri (bho) | 0.965517 | 0.933333 | 0.949153 | | Banjar (bjn) | 0.891566 | 0.880952 | 0.886228 | | Tibetan (bod) | 1.000000 | 1.000000 | 1.000000 | | Bosnian (bos) | 0.375000 | 0.323077 | 0.347107 | | Bishnupriya (bpy) | 0.986301 | 1.000000 | 0.993103 | | Breton (bre) | 0.951613 | 0.893939 | 0.921875 | | Bulgarian (bul) | 0.945055 | 0.877551 | 0.910053 | | Buryat (bxr) | 0.955556 | 0.843137 | 0.895833 | | Catalan (cat) | 0.692308 | 0.750000 | 0.720000 | | Chavacano (cbk) | 0.842857 | 0.641304 | 0.728395 | | Min Dong (cdo) | 0.972973 | 1.000000 | 0.986301 | | Cebuano (ceb) | 0.981308 | 0.954545 | 0.967742 | | Czech (ces) | 0.944444 | 0.915385 | 0.929687 | | Chechen (che) | 0.875000 | 0.700000 | 0.777778 | | Cherokee (chr) | 1.000000 | 0.970588 | 0.985075 | | Chuvash (chv) | 0.875000 | 0.836957 | 0.855556 | | Central Kurdish (ckb) | 1.000000 | 0.983051 | 0.991453 | | Cornish (cor) | 0.979592 | 0.969697 | 0.974619 | | Corsican (cos) | 0.986842 | 0.925926 | 0.955414 | | Crimean Tatar (crh) | 0.958333 | 0.907895 | 0.932432 | | Kashubian (csb) | 0.920354 | 0.904348 | 0.912281 | | Welsh (cym) | 0.971014 | 0.943662 | 0.957143 | | Danish (dan) | 0.865169 | 0.777778 | 0.819149 | | German (deu) | 0.721311 | 0.822430 | 0.768559 | | Dimli (diq) | 0.915966 | 0.923729 | 0.919831 | | Dhivehi (div) | 1.000000 | 0.991228 | 0.995595 | | Lower Sorbian (dsb) | 0.898876 | 0.879121 | 0.888889 | | Doteli (dty) | 0.821429 | 0.638889 | 0.718750 | | Emilian (egl) | 0.988095 | 0.922222 | 0.954023 | | Modern Greek (ell) | 0.988636 | 0.966667 | 0.977528 | | English (eng) | 0.522727 | 0.784091 | 0.627273 | | Esperanto (epo) | 0.963855 | 0.930233 | 0.946746 | | Estonian (est) | 0.922222 | 0.873684 | 0.897297 | | Basque (eus) | 1.000000 | 0.941176 | 0.969697 | | Extremaduran (ext) | 0.925373 | 0.885714 | 0.905109 | | Faroese (fao) | 0.855072 | 0.887218 | 0.870849 | | Persian (fas) | 0.879630 | 0.979381 | 0.926829 | | Finnish (fin) | 0.952830 | 0.943925 | 0.948357 | | French (fra) | 0.676768 | 0.943662 | 0.788235 | | Arpitan (frp) | 0.867925 | 0.807018 | 0.836364 | | Western Frisian (fry) | 0.956989 | 0.890000 | 0.922280 | | Friulian (fur) | 1.000000 | 0.857143 | 0.923077 | | Gagauz (gag) | 0.939024 | 0.802083 | 0.865169 | | Scottish Gaelic (gla) | 1.000000 | 0.879121 | 0.935673 | | Irish (gle) | 0.989247 | 0.958333 | 0.973545 | | Galician (glg) | 0.910256 | 0.922078 | 0.916129 | | Gilaki (glk) | 0.964706 | 0.872340 | 0.916201 | | Manx (glv) | 1.000000 | 0.965517 | 0.982456 | | Guarani (grn) | 0.983333 | 1.000000 | 0.991597 | | Gujarati (guj) | 1.000000 | 0.991525 | 0.995745 | | Hakka Chinese (hak) | 0.955224 | 0.955224 | 0.955224 | | Haitian Creole (hat) | 0.833333 | 0.666667 | 0.740741 | | Hausa (hau) | 0.936709 | 0.913580 | 0.925000 | | Serbo-Croatian (hbs) | 0.452830 | 0.410256 | 0.430493 | | Hebrew (heb) | 0.988235 | 0.976744 | 0.982456 | | Fiji Hindi (hif) | 0.936709 | 0.840909 | 0.886228 | | Hindi (hin) | 0.965517 | 0.756757 | 0.848485 | | Croatian (hrv) | 0.443820 | 0.537415 | 0.486154 | | Upper Sorbian (hsb) | 0.951613 | 0.830986 | 0.887218 | | Hungarian (hun) | 0.854701 | 0.909091 | 0.881057 | | Armenian (hye) | 1.000000 | 0.816327 | 0.898876 | | Igbo (ibo) | 0.974359 | 0.926829 | 0.950000 | | Ido (ido) | 0.975000 | 0.987342 | 0.981132 | | Interlingue (ile) | 0.880597 | 0.921875 | 0.900763 | | Iloko (ilo) | 0.882353 | 0.821918 | 0.851064 | | Interlingua (ina) | 0.952381 | 0.895522 | 0.923077 | | Indonesian (ind) | 0.606383 | 0.695122 | 0.647727 | | Icelandic (isl) | 0.978261 | 0.882353 | 0.927835 | | Italian (ita) | 0.910448 | 0.910448 | 0.910448 | | Jamaican Patois (jam) | 0.988764 | 0.967033 | 0.977778 | | Javanese (jav) | 0.903614 | 0.862069 | 0.882353 | | Lojban (jbo) | 0.943878 | 0.929648 | 0.936709 | | Japanese (jpn) | 1.000000 | 0.764706 | 0.866667 | | Karakalpak (kaa) | 0.940171 | 0.901639 | 0.920502 | | Kabyle (kab) | 0.985294 | 0.837500 | 0.905405 | | Kannada (kan) | 0.975806 | 0.975806 | 0.975806 | | Georgian (kat) | 0.953704 | 0.903509 | 0.927928 | | Kazakh (kaz) | 0.934579 | 0.877193 | 0.904977 | | Kabardian (kbd) | 0.987952 | 0.953488 | 0.970414 | | Central Khmer (khm) | 0.928571 | 0.829787 | 0.876404 | | Kinyarwanda (kin) | 0.953125 | 0.938462 | 0.945736 | | Kirghiz (kir) | 0.927632 | 0.881250 | 0.903846 | | Komi-Permyak (koi) | 0.750000 | 0.776786 | 0.763158 | | Konkani (kok) | 0.893491 | 0.872832 | 0.883041 | | Komi (kom) | 0.734177 | 0.690476 | 0.711656 | | Korean (kor) | 0.989899 | 0.989899 | 0.989899 | | Karachay-Balkar (krc) | 0.928571 | 0.917647 | 0.923077 | | Ripuarisch (ksh) | 0.915789 | 0.896907 | 0.906250 | | Kurdish (kur) | 0.977528 | 0.935484 | 0.956044 | | Ladino (lad) | 0.985075 | 0.904110 | 0.942857 | | Lao (lao) | 0.896552 | 0.812500 | 0.852459 | | Latin (lat) | 0.741935 | 0.831325 | 0.784091 | | Latvian (lav) | 0.710526 | 0.878049 | 0.785455 | | Lezghian (lez) | 0.975309 | 0.877778 | 0.923977 | | Ligurian (lij) | 0.951807 | 0.897727 | 0.923977 | | Limburgan (lim) | 0.909091 | 0.921053 | 0.915033 | | Lingala (lin) | 0.942857 | 0.814815 | 0.874172 | | Lithuanian (lit) | 0.892857 | 0.925926 | 0.909091 | | Lombard (lmo) | 0.766234 | 0.951613 | 0.848921 | | Northern Luri (lrc) | 0.972222 | 0.875000 | 0.921053 | | Latgalian (ltg) | 0.895349 | 0.865169 | 0.880000 | | Luxembourgish (ltz) | 0.882353 | 0.750000 | 0.810811 | | Luganda (lug) | 0.946429 | 0.883333 | 0.913793 | | Literary Chinese (lzh) | 1.000000 | 1.000000 | 1.000000 | | Maithili (mai) | 0.893617 | 0.823529 | 0.857143 | | Malayalam (mal) | 1.000000 | 0.975000 | 0.987342 | | Banyumasan (map-bms) | 0.924242 | 0.772152 | 0.841379 | | Marathi (mar) | 0.874126 | 0.919118 | 0.896057 | | Moksha (mdf) | 0.771242 | 0.830986 | 0.800000 | | Eastern Mari (mhr) | 0.820000 | 0.860140 | 0.839590 | | Minangkabau (min) | 0.973684 | 0.973684 | 0.973684 | | Macedonian (mkd) | 0.895652 | 0.953704 | 0.923767 | | Malagasy (mlg) | 1.000000 | 0.966102 | 0.982759 | | Maltese (mlt) | 0.987952 | 0.964706 | 0.976190 | | Min Nan Chinese (nan) | 0.975000 | 1.000000 | 0.987342 | | Mongolian (mon) | 0.954545 | 0.933333 | 0.943820 | | Maori (mri) | 0.985294 | 1.000000 | 0.992593 | | Western Mari (mrj) | 0.966292 | 0.914894 | 0.939891 | | Malay (msa) | 0.770270 | 0.695122 | 0.730769 | | Mirandese (mwl) | 0.970588 | 0.891892 | 0.929577 | | Burmese (mya) | 1.000000 | 0.964286 | 0.981818 | | Erzya (myv) | 0.535714 | 0.681818 | 0.600000 | | Mazanderani (mzn) | 0.968750 | 0.898551 | 0.932331 | | Neapolitan (nap) | 0.892308 | 0.865672 | 0.878788 | | Navajo (nav) | 0.984375 | 0.984375 | 0.984375 | | Classical Nahuatl (nci) | 0.901408 | 0.761905 | 0.825806 | | Low German (nds) | 0.896226 | 0.913462 | 0.904762 | | West Low German (nds-nl) | 0.873563 | 0.835165 | 0.853933 | | Nepali (macrolanguage) (nep) | 0.704545 | 0.861111 | 0.775000 | | Newari (new) | 0.920000 | 0.741935 | 0.821429 | | Dutch (nld) | 0.925926 | 0.872093 | 0.898204 | | Norwegian Nynorsk (nno) | 0.847059 | 0.808989 | 0.827586 | | Bokmål (nob) | 0.861386 | 0.852941 | 0.857143 | | Narom (nrm) | 0.966667 | 0.983051 | 0.974790 | | Northern Sotho (nso) | 0.897436 | 0.921053 | 0.909091 | | Occitan (oci) | 0.958333 | 0.696970 | 0.807018 | | Livvi-Karelian (olo) | 0.967742 | 0.937500 | 0.952381 | | Oriya (ori) | 0.933333 | 1.000000 | 0.965517 | | Oromo (orm) | 0.977528 | 0.915789 | 0.945652 | | Ossetian (oss) | 0.958333 | 0.841463 | 0.896104 | | Pangasinan (pag) | 0.847328 | 0.909836 | 0.877470 | | Pampanga (pam) | 0.969697 | 0.780488 | 0.864865 | | Panjabi (pan) | 1.000000 | 1.000000 | 1.000000 | | Papiamento (pap) | 0.876190 | 0.920000 | 0.897561 | | Picard (pcd) | 0.707317 | 0.568627 | 0.630435 | | Pennsylvania German (pdc) | 0.827273 | 0.827273 | 0.827273 | | Palatine German (pfl) | 0.882353 | 0.914634 | 0.898204 | | Western Panjabi (pnb) | 0.964286 | 0.931034 | 0.947368 | | Polish (pol) | 0.859813 | 0.910891 | 0.884615 | | Portuguese (por) | 0.535714 | 0.833333 | 0.652174 | | Pushto (pus) | 0.989362 | 0.902913 | 0.944162 | | Quechua (que) | 0.979167 | 0.903846 | 0.940000 | | Tarantino dialect (roa-tara) | 0.964912 | 0.901639 | 0.932203 | | Romansh (roh) | 0.914894 | 0.895833 | 0.905263 | | Romanian (ron) | 0.880597 | 0.880597 | 0.880597 | | Rusyn (rue) | 0.932584 | 0.805825 | 0.864583 | | Aromanian (rup) | 0.783333 | 0.758065 | 0.770492 | | Russian (rus) | 0.517986 | 0.765957 | 0.618026 | | Yakut (sah) | 0.954023 | 0.922222 | 0.937853 | | Sanskrit (san) | 0.866667 | 0.951220 | 0.906977 | | Sicilian (scn) | 0.984375 | 0.940299 | 0.961832 | | Scots (sco) | 0.851351 | 0.900000 | 0.875000 | | Samogitian (sgs) | 0.977011 | 0.876289 | 0.923913 | | Sinhala (sin) | 0.406154 | 0.985075 | 0.575163 | | Slovak (slk) | 0.956989 | 0.872549 | 0.912821 | | Slovene (slv) | 0.907216 | 0.854369 | 0.880000 | | Northern Sami (sme) | 0.949367 | 0.892857 | 0.920245 | | Shona (sna) | 0.936508 | 0.855072 | 0.893939 | | Sindhi (snd) | 0.984962 | 0.992424 | 0.988679 | | Somali (som) | 0.949153 | 0.848485 | 0.896000 | | Spanish (spa) | 0.584158 | 0.746835 | 0.655556 | | Albanian (sqi) | 0.988095 | 0.912088 | 0.948571 | | Sardinian (srd) | 0.957746 | 0.931507 | 0.944444 | | Sranan (srn) | 0.985714 | 0.945205 | 0.965035 | | Serbian (srp) | 0.950980 | 0.889908 | 0.919431 | | Saterfriesisch (stq) | 0.962500 | 0.875000 | 0.916667 | | Sundanese (sun) | 0.778846 | 0.910112 | 0.839378 | | Swahili (macrolanguage) (swa) | 0.915493 | 0.878378 | 0.896552 | | Swedish (swe) | 0.989247 | 0.958333 | 0.973545 | | Silesian (szl) | 0.944444 | 0.904255 | 0.923913 | | Tamil (tam) | 0.990000 | 0.970588 | 0.980198 | | Tatar (tat) | 0.942029 | 0.902778 | 0.921986 | | Tulu (tcy) | 0.980519 | 0.967949 | 0.974194 | | Telugu (tel) | 0.965986 | 0.965986 | 0.965986 | | Tetum (tet) | 0.898734 | 0.855422 | 0.876543 | | Tajik (tgk) | 0.974684 | 0.939024 | 0.956522 | | Tagalog (tgl) | 0.965909 | 0.934066 | 0.949721 | | Thai (tha) | 0.923077 | 0.882353 | 0.902256 | | Tongan (ton) | 0.970149 | 0.890411 | 0.928571 | | Tswana (tsn) | 0.888889 | 0.926316 | 0.907216 | | Turkmen (tuk) | 0.968000 | 0.889706 | 0.927203 | | Turkish (tur) | 0.871287 | 0.926316 | 0.897959 | | Tuvan (tyv) | 0.948454 | 0.859813 | 0.901961 | | Udmurt (udm) | 0.989362 | 0.894231 | 0.939394 | | Uighur (uig) | 1.000000 | 0.953333 | 0.976109 | | Ukrainian (ukr) | 0.893617 | 0.875000 | 0.884211 | | Urdu (urd) | 1.000000 | 1.000000 | 1.000000 | | Uzbek (uzb) | 0.636042 | 0.886700 | 0.740741 | | Venetian (vec) | 1.000000 | 0.941176 | 0.969697 | | Veps (vep) | 0.858586 | 0.965909 | 0.909091 | | Vietnamese (vie) | 1.000000 | 0.940476 | 0.969325 | | Vlaams (vls) | 0.885714 | 0.898551 | 0.892086 | | Volapük (vol) | 0.975309 | 0.975309 | 0.975309 | | Võro (vro) | 0.855670 | 0.864583 | 0.860104 | | Waray (war) | 0.972222 | 0.909091 | 0.939597 | | Walloon (wln) | 0.742138 | 0.893939 | 0.810997 | | Wolof (wol) | 0.882979 | 0.954023 | 0.917127 | | Wu Chinese (wuu) | 0.961538 | 0.833333 | 0.892857 | | Xhosa (xho) | 0.934066 | 0.867347 | 0.899471 | | Mingrelian (xmf) | 0.958333 | 0.929293 | 0.943590 | | Yiddish (yid) | 0.984375 | 0.875000 | 0.926471 | | Yoruba (yor) | 0.868421 | 0.857143 | 0.862745 | | Zeeuws (zea) | 0.879518 | 0.793478 | 0.834286 | | Cantonese (zh-yue) | 0.896552 | 0.812500 | 0.852459 | | Standard Chinese (zho) | 0.906250 | 0.935484 | 0.920635 | | accuracy | 0.881051 | 0.881051 | 0.881051 | | macro avg | 0.903245 | 0.880618 | 0.888996 | | weighted avg | 0.894174 | 0.881051 | 0.884520 | | 4e2da79c41dfb8f8e9815ed3543e4ee7 |
apache-2.0 | [] | false | By Token (3 to 5) | language | precision | recall | f1-score | |:--------------------------------------:|:---------:|:--------:|:--------:| | Achinese (ace) | 0.873846 | 0.827988 | 0.850299 | | Afrikaans (afr) | 0.638060 | 0.732334 | 0.681954 | | Alemannic German (als) | 0.673780 | 0.547030 | 0.603825 | | Amharic (amh) | 0.997743 | 0.954644 | 0.975717 | | Old English (ang) | 0.840816 | 0.693603 | 0.760148 | | Arabic (ara) | 0.768737 | 0.840749 | 0.803132 | | Aragonese (arg) | 0.493671 | 0.505181 | 0.499360 | | Egyptian Arabic (arz) | 0.823529 | 0.741935 | 0.780606 | | Assamese (asm) | 0.948454 | 0.893204 | 0.920000 | | Asturian (ast) | 0.490000 | 0.508299 | 0.498982 | | Avar (ava) | 0.813636 | 0.655678 | 0.726166 | | Aymara (aym) | 0.795833 | 0.779592 | 0.787629 | | South Azerbaijani (azb) | 0.832836 | 0.863777 | 0.848024 | | Azerbaijani (aze) | 0.867470 | 0.800000 | 0.832370 | | Bashkir (bak) | 0.851852 | 0.750000 | 0.797688 | | Bavarian (bar) | 0.560897 | 0.522388 | 0.540958 | | Central Bikol (bcl) | 0.708229 | 0.668235 | 0.687651 | | Belarusian (Taraschkewiza) (be-tarask) | 0.615635 | 0.526462 | 0.567568 | | Belarusian (bel) | 0.539952 | 0.597855 | 0.567430 | | Bengali (ben) | 0.830275 | 0.885086 | 0.856805 | | Bhojpuri (bho) | 0.723118 | 0.691517 | 0.706965 | | Banjar (bjn) | 0.619586 | 0.726269 | 0.668699 | | Tibetan (bod) | 0.999537 | 0.991728 | 0.995617 | | Bosnian (bos) | 0.330849 | 0.403636 | 0.363636 | | Bishnupriya (bpy) | 0.941634 | 0.949020 | 0.945312 | | Breton (bre) | 0.772222 | 0.745308 | 0.758527 | | Bulgarian (bul) | 0.771505 | 0.706897 | 0.737789 | | Buryat (bxr) | 0.741935 | 0.753149 | 0.747500 | | Catalan (cat) | 0.528716 | 0.610136 | 0.566516 | | Chavacano (cbk) | 0.409449 | 0.312625 | 0.354545 | | Min Dong (cdo) | 0.951264 | 0.936057 | 0.943599 | | Cebuano (ceb) | 0.888298 | 0.876640 | 0.882431 | | Czech (ces) | 0.806045 | 0.758294 | 0.781441 | | Chechen (che) | 0.857143 | 0.600000 | 0.705882 | | Cherokee (chr) | 0.997840 | 0.952577 | 0.974684 | | Chuvash (chv) | 0.874346 | 0.776744 | 0.822660 | | Central Kurdish (ckb) | 0.984848 | 0.953545 | 0.968944 | | Cornish (cor) | 0.747596 | 0.807792 | 0.776529 | | Corsican (cos) | 0.673913 | 0.708571 | 0.690808 | | Crimean Tatar (crh) | 0.498801 | 0.700337 | 0.582633 | | Kashubian (csb) | 0.797059 | 0.794721 | 0.795888 | | Welsh (cym) | 0.829609 | 0.841360 | 0.835443 | | Danish (dan) | 0.649789 | 0.622222 | 0.635707 | | German (deu) | 0.559406 | 0.763514 | 0.645714 | | Dimli (diq) | 0.835580 | 0.763547 | 0.797941 | | Dhivehi (div) | 1.000000 | 0.980645 | 0.990228 | | Lower Sorbian (dsb) | 0.740484 | 0.694805 | 0.716918 | | Doteli (dty) | 0.616314 | 0.527132 | 0.568245 | | Emilian (egl) | 0.822993 | 0.769625 | 0.795414 | | Modern Greek (ell) | 0.972043 | 0.963753 | 0.967880 | | English (eng) | 0.260492 | 0.724346 | 0.383183 | | Esperanto (epo) | 0.766764 | 0.716621 | 0.740845 | | Estonian (est) | 0.698885 | 0.673835 | 0.686131 | | Basque (eus) | 0.882716 | 0.841176 | 0.861446 | | Extremaduran (ext) | 0.570605 | 0.511628 | 0.539510 | | Faroese (fao) | 0.773987 | 0.784017 | 0.778970 | | Persian (fas) | 0.709836 | 0.809346 | 0.756332 | | Finnish (fin) | 0.866261 | 0.796089 | 0.829694 | | French (fra) | 0.496263 | 0.700422 | 0.580927 | | Arpitan (frp) | 0.663366 | 0.584302 | 0.621329 | | Western Frisian (fry) | 0.750000 | 0.756148 | 0.753061 | | Friulian (fur) | 0.713555 | 0.675545 | 0.694030 | | Gagauz (gag) | 0.728125 | 0.677326 | 0.701807 | | Scottish Gaelic (gla) | 0.831601 | 0.817996 | 0.824742 | | Irish (gle) | 0.868852 | 0.801296 | 0.833708 | | Galician (glg) | 0.469816 | 0.454315 | 0.461935 | | Gilaki (glk) | 0.703883 | 0.687204 | 0.695444 | | Manx (glv) | 0.873047 | 0.886905 | 0.879921 | | Guarani (grn) | 0.848580 | 0.793510 | 0.820122 | | Gujarati (guj) | 0.995643 | 0.926978 | 0.960084 | | Hakka Chinese (hak) | 0.898403 | 0.904971 | 0.901675 | | Haitian Creole (hat) | 0.719298 | 0.518987 | 0.602941 | | Hausa (hau) | 0.815353 | 0.829114 | 0.822176 | | Serbo-Croatian (hbs) | 0.343465 | 0.244589 | 0.285714 | | Hebrew (heb) | 0.891304 | 0.933941 | 0.912125 | | Fiji Hindi (hif) | 0.662577 | 0.664615 | 0.663594 | | Hindi (hin) | 0.782301 | 0.778169 | 0.780229 | | Croatian (hrv) | 0.360308 | 0.374000 | 0.367026 | | Upper Sorbian (hsb) | 0.745763 | 0.611111 | 0.671756 | | Hungarian (hun) | 0.876812 | 0.846154 | 0.861210 | | Armenian (hye) | 0.988201 | 0.917808 | 0.951705 | | Igbo (ibo) | 0.825397 | 0.696429 | 0.755448 | | Ido (ido) | 0.760479 | 0.814103 | 0.786378 | | Interlingue (ile) | 0.701299 | 0.580645 | 0.635294 | | Iloko (ilo) | 0.688356 | 0.844538 | 0.758491 | | Interlingua (ina) | 0.577889 | 0.588235 | 0.583016 | | Indonesian (ind) | 0.415879 | 0.514019 | 0.459770 | | Icelandic (isl) | 0.855263 | 0.790754 | 0.821745 | | Italian (ita) | 0.474576 | 0.561247 | 0.514286 | | Jamaican Patois (jam) | 0.826087 | 0.791667 | 0.808511 | | Javanese (jav) | 0.670130 | 0.658163 | 0.664093 | | Lojban (jbo) | 0.896861 | 0.917431 | 0.907029 | | Japanese (jpn) | 0.931373 | 0.848214 | 0.887850 | | Karakalpak (kaa) | 0.790393 | 0.827744 | 0.808637 | | Kabyle (kab) | 0.828571 | 0.759162 | 0.792350 | | Kannada (kan) | 0.879357 | 0.847545 | 0.863158 | | Georgian (kat) | 0.916399 | 0.907643 | 0.912000 | | Kazakh (kaz) | 0.900901 | 0.819672 | 0.858369 | | Kabardian (kbd) | 0.923345 | 0.892256 | 0.907534 | | Central Khmer (khm) | 0.976667 | 0.816156 | 0.889226 | | Kinyarwanda (kin) | 0.824324 | 0.726190 | 0.772152 | | Kirghiz (kir) | 0.674766 | 0.779698 | 0.723447 | | Komi-Permyak (koi) | 0.652830 | 0.633700 | 0.643123 | | Konkani (kok) | 0.778865 | 0.728938 | 0.753075 | | Komi (kom) | 0.737374 | 0.572549 | 0.644592 | | Korean (kor) | 0.984615 | 0.967603 | 0.976035 | | Karachay-Balkar (krc) | 0.869416 | 0.857627 | 0.863481 | | Ripuarisch (ksh) | 0.709859 | 0.649485 | 0.678331 | | Kurdish (kur) | 0.883777 | 0.862884 | 0.873206 | | Ladino (lad) | 0.660920 | 0.576441 | 0.615797 | | Lao (lao) | 0.986175 | 0.918455 | 0.951111 | | Latin (lat) | 0.581250 | 0.636986 | 0.607843 | | Latvian (lav) | 0.824513 | 0.797844 | 0.810959 | | Lezghian (lez) | 0.898955 | 0.793846 | 0.843137 | | Ligurian (lij) | 0.662903 | 0.677100 | 0.669927 | | Limburgan (lim) | 0.615385 | 0.581818 | 0.598131 | | Lingala (lin) | 0.836207 | 0.763780 | 0.798354 | | Lithuanian (lit) | 0.756329 | 0.804714 | 0.779772 | | Lombard (lmo) | 0.556818 | 0.536986 | 0.546722 | | Northern Luri (lrc) | 0.838574 | 0.753296 | 0.793651 | | Latgalian (ltg) | 0.759531 | 0.755102 | 0.757310 | | Luxembourgish (ltz) | 0.645062 | 0.614706 | 0.629518 | | Luganda (lug) | 0.787535 | 0.805797 | 0.796562 | | Literary Chinese (lzh) | 0.921951 | 0.949749 | 0.935644 | | Maithili (mai) | 0.777778 | 0.761658 | 0.769634 | | Malayalam (mal) | 0.993377 | 0.949367 | 0.970874 | | Banyumasan (map-bms) | 0.531429 | 0.453659 | 0.489474 | | Marathi (mar) | 0.748744 | 0.818681 | 0.782152 | | Moksha (mdf) | 0.728745 | 0.800000 | 0.762712 | | Eastern Mari (mhr) | 0.790323 | 0.760870 | 0.775316 | | Minangkabau (min) | 0.953271 | 0.886957 | 0.918919 | | Macedonian (mkd) | 0.816399 | 0.849722 | 0.832727 | | Malagasy (mlg) | 0.925187 | 0.918317 | 0.921739 | | Maltese (mlt) | 0.869421 | 0.890017 | 0.879599 | | Min Nan Chinese (nan) | 0.743707 | 0.820707 | 0.780312 | | Mongolian (mon) | 0.852194 | 0.838636 | 0.845361 | | Maori (mri) | 0.934726 | 0.937173 | 0.935948 | | Western Mari (mrj) | 0.818792 | 0.827119 | 0.822934 | | Malay (msa) | 0.508065 | 0.376119 | 0.432247 | | Mirandese (mwl) | 0.650407 | 0.685225 | 0.667362 | | Burmese (mya) | 0.995968 | 0.972441 | 0.984064 | | Erzya (myv) | 0.475783 | 0.503012 | 0.489019 | | Mazanderani (mzn) | 0.775362 | 0.701639 | 0.736661 | | Neapolitan (nap) | 0.628993 | 0.595349 | 0.611708 | | Navajo (nav) | 0.955882 | 0.937500 | 0.946602 | | Classical Nahuatl (nci) | 0.679758 | 0.589005 | 0.631136 | | Low German (nds) | 0.669789 | 0.690821 | 0.680143 | | West Low German (nds-nl) | 0.513889 | 0.504545 | 0.509174 | | Nepali (macrolanguage) (nep) | 0.640476 | 0.649758 | 0.645084 | | Newari (new) | 0.928571 | 0.745902 | 0.827273 | | Dutch (nld) | 0.553763 | 0.553763 | 0.553763 | | Norwegian Nynorsk (nno) | 0.569277 | 0.519231 | 0.543103 | | Bokmål (nob) | 0.519856 | 0.562500 | 0.540338 | | Narom (nrm) | 0.691275 | 0.605882 | 0.645768 | | Northern Sotho (nso) | 0.950276 | 0.815166 | 0.877551 | | Occitan (oci) | 0.483444 | 0.366834 | 0.417143 | | Livvi-Karelian (olo) | 0.816850 | 0.790780 | 0.803604 | | Oriya (ori) | 0.981481 | 0.963636 | 0.972477 | | Oromo (orm) | 0.885714 | 0.829218 | 0.856536 | | Ossetian (oss) | 0.822006 | 0.855219 | 0.838284 | | Pangasinan (pag) | 0.842105 | 0.715655 | 0.773748 | | Pampanga (pam) | 0.770000 | 0.435028 | 0.555957 | | Panjabi (pan) | 0.996154 | 0.984791 | 0.990440 | | Papiamento (pap) | 0.674672 | 0.661670 | 0.668108 | | Picard (pcd) | 0.407895 | 0.356322 | 0.380368 | | Pennsylvania German (pdc) | 0.487047 | 0.509485 | 0.498013 | | Palatine German (pfl) | 0.614173 | 0.570732 | 0.591656 | | Western Panjabi (pnb) | 0.926267 | 0.887417 | 0.906426 | | Polish (pol) | 0.797059 | 0.734417 | 0.764457 | | Portuguese (por) | 0.500914 | 0.586724 | 0.540434 | | Pushto (pus) | 0.941489 | 0.898477 | 0.919481 | | Quechua (que) | 0.854167 | 0.797665 | 0.824950 | | Tarantino dialect (roa-tara) | 0.669794 | 0.724138 | 0.695906 | | Romansh (roh) | 0.745527 | 0.760649 | 0.753012 | | Romanian (ron) | 0.805486 | 0.769048 | 0.786845 | | Rusyn (rue) | 0.718543 | 0.645833 | 0.680251 | | Aromanian (rup) | 0.288482 | 0.730245 | 0.413580 | | Russian (rus) | 0.530120 | 0.690583 | 0.599805 | | Yakut (sah) | 0.853521 | 0.865714 | 0.859574 | | Sanskrit (san) | 0.931343 | 0.896552 | 0.913616 | | Sicilian (scn) | 0.734139 | 0.618321 | 0.671271 | | Scots (sco) | 0.571429 | 0.540816 | 0.555701 | | Samogitian (sgs) | 0.829167 | 0.748120 | 0.786561 | | Sinhala (sin) | 0.909474 | 0.935065 | 0.922092 | | Slovak (slk) | 0.738235 | 0.665782 | 0.700139 | | Slovene (slv) | 0.671123 | 0.662269 | 0.666667 | | Northern Sami (sme) | 0.800676 | 0.825784 | 0.813036 | | Shona (sna) | 0.761702 | 0.724696 | 0.742739 | | Sindhi (snd) | 0.950172 | 0.946918 | 0.948542 | | Somali (som) | 0.849462 | 0.802030 | 0.825065 | | Spanish (spa) | 0.325234 | 0.413302 | 0.364017 | | Albanian (sqi) | 0.875899 | 0.832479 | 0.853637 | | Sardinian (srd) | 0.750000 | 0.711061 | 0.730012 | | Sranan (srn) | 0.888889 | 0.771084 | 0.825806 | | Serbian (srp) | 0.824561 | 0.814356 | 0.819427 | | Saterfriesisch (stq) | 0.790087 | 0.734417 | 0.761236 | | Sundanese (sun) | 0.764192 | 0.631769 | 0.691700 | | Swahili (macrolanguage) (swa) | 0.763496 | 0.796247 | 0.779528 | | Swedish (swe) | 0.838284 | 0.723647 | 0.776758 | | Silesian (szl) | 0.819788 | 0.750809 | 0.783784 | | Tamil (tam) | 0.985765 | 0.955172 | 0.970228 | | Tatar (tat) | 0.469780 | 0.795349 | 0.590674 | | Tulu (tcy) | 0.893300 | 0.873786 | 0.883436 | | Telugu (tel) | 1.000000 | 0.913690 | 0.954899 | | Tetum (tet) | 0.765116 | 0.744344 | 0.754587 | | Tajik (tgk) | 0.828418 | 0.813158 | 0.820717 | | Tagalog (tgl) | 0.751468 | 0.757396 | 0.754420 | | Thai (tha) | 0.933884 | 0.807143 | 0.865900 | | Tongan (ton) | 0.920245 | 0.923077 | 0.921659 | | Tswana (tsn) | 0.873397 | 0.889070 | 0.881164 | | Turkmen (tuk) | 0.898438 | 0.837887 | 0.867107 | | Turkish (tur) | 0.666667 | 0.716981 | 0.690909 | | Tuvan (tyv) | 0.857143 | 0.805063 | 0.830287 | | Udmurt (udm) | 0.865517 | 0.756024 | 0.807074 | | Uighur (uig) | 0.991597 | 0.967213 | 0.979253 | | Ukrainian (ukr) | 0.771341 | 0.702778 | 0.735465 | | Urdu (urd) | 0.877647 | 0.855505 | 0.866434 | | Uzbek (uzb) | 0.655652 | 0.797040 | 0.719466 | | Venetian (vec) | 0.611111 | 0.527233 | 0.566082 | | Veps (vep) | 0.672862 | 0.688213 | 0.680451 | | Vietnamese (vie) | 0.932406 | 0.914230 | 0.923228 | | Vlaams (vls) | 0.594427 | 0.501305 | 0.543909 | | Volapük (vol) | 0.765625 | 0.942308 | 0.844828 | | Võro (vro) | 0.797203 | 0.740260 | 0.767677 | | Waray (war) | 0.930876 | 0.930876 | 0.930876 | | Walloon (wln) | 0.636804 | 0.693931 | 0.664141 | | Wolof (wol) | 0.864220 | 0.845601 | 0.854809 | | Wu Chinese (wuu) | 0.848921 | 0.830986 | 0.839858 | | Xhosa (xho) | 0.837398 | 0.759214 | 0.796392 | | Mingrelian (xmf) | 0.943396 | 0.874126 | 0.907441 | | Yiddish (yid) | 0.955729 | 0.897311 | 0.925599 | | Yoruba (yor) | 0.812010 | 0.719907 | 0.763190 | | Zeeuws (zea) | 0.617737 | 0.550409 | 0.582133 | | Cantonese (zh-yue) | 0.859649 | 0.649007 | 0.739623 | | Standard Chinese (zho) | 0.845528 | 0.781955 | 0.812500 | | accuracy | 0.749527 | 0.749527 | 0.749527 | | macro avg | 0.762866 | 0.742101 | 0.749261 | | weighted avg | 0.762006 | 0.749527 | 0.752910 | | fa9f60aaf78c2b83520e854c1097df68 |
creativeml-openrail-m | ['text-to-image', 'isometric', 'art', 'stable diffusion', 'stable diffusion 1.5', 'duskfallcrew'] | false | [](https://huggingface.co/spaces/Duskfallcrew/isometric-dreams-sd-1-5) | 35ee6623996d14090f606e469da7c247 |
creativeml-openrail-m | ['text-to-image', 'isometric', 'art', 'stable diffusion', 'stable diffusion 1.5', 'duskfallcrew'] | false | Isometric Dreams SD 1.5 trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! | 8d79f8725c452fead7328273132fdb76 |
creativeml-openrail-m | ['text-to-image', 'isometric', 'art', 'stable diffusion', 'stable diffusion 1.5', 'duskfallcrew'] | false | If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk duskametrick15 (use that on your prompt) | 0e0042af2a0a99f5a5fc0fcf456cede2 |
mit | ['generated_from_keras_callback'] | false | nandysoham/Pub-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3449 - Train End Logits Accuracy: 0.9097 - Train Start Logits Accuracy: 0.875 - Validation Loss: 0.8311 - Validation End Logits Accuracy: 0.7692 - Validation Start Logits Accuracy: 0.8462 - Epoch: 0 | 629ce181366b482015eb83c7387f5352 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3449 | 0.9097 | 0.875 | 0.8311 | 0.7692 | 0.8462 | 0 | | be94841eb878f3dc2f2e55b48bfb9809 |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 77fce65312877a132bbae01917ad26b74f6e2e14 pip install -e . cd egs2/iwslt22_dialect/st1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/brianyan918_iwslt22_dialect_st_transformer_fisherlike_4gpu_bbins16m_fix ``` <!-- Generated by scripts/utils/show_st_results.sh --> | bb6838f3909743780072ae53223bdd9e |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | Environments - date: `Tue Feb 8 13:29:21 EST 2022` - python version: `3.8.12 (default, Oct 12 2021, 13:49:34) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1` - Git hash: `77fce65312877a132bbae01917ad26b74f6e2e14` - Commit date: `Tue Feb 8 10:48:10 2022 -0500` | 80a28c2e7055f7360971909b2d4435d7 |
cc-by-4.0 | ['espnet', 'audio', 'speech-translation'] | false | ST config <details><summary>expand</summary> ``` config: conf/tuning/transformer_fisherlike_4gpu_bbins16m_fix.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/st_transformer_fisherlike_4gpu_bbins16m_fix_raw_bpe_tc1000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 36641 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 3 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 16000000 valid_batch_bins: null train_shape_file: - exp/st_stats_raw_bpe1000_sp/train/speech_shape - exp/st_stats_raw_bpe1000_sp/train/text_shape.bpe - exp/st_stats_raw_bpe1000_sp/train/src_text_shape.bpe valid_shape_file: - exp/st_stats_raw_bpe1000_sp/valid/speech_shape - exp/st_stats_raw_bpe1000_sp/valid/text_shape.bpe - exp/st_stats_raw_bpe1000_sp/valid/src_text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - /scratch/iwslt22dump//raw/train_sp/wav.scp - speech - kaldi_ark - - /scratch/iwslt22dump//raw/train_sp/text.tc.en - text - text - - /scratch/iwslt22dump//raw/train_sp/text.tc.rm.ta - src_text - text valid_data_path_and_name_and_type: - - /scratch/iwslt22dump//raw/dev/wav.scp - speech - kaldi_ark - - /scratch/iwslt22dump//raw/dev/text.tc.en - text - text - - /scratch/iwslt22dump//raw/dev/text.tc.rm.ta - src_text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 12.5 scheduler: noamlr scheduler_conf: model_size: 256 warmup_steps: 25000 token_list: - <blank> - <unk> - s - ▁ - apo - '&' - ; - ▁i - ▁you - t - ▁it - ▁the - ▁and - ▁to - ▁that - ▁a - n - a - ▁he - ▁me - m - d - ▁yes - ▁she - ▁no - ▁in - ▁what - ▁for - ▁we - ing - ll - ▁they - re - ▁are - ▁did - ▁god - ▁is - e - ed - ▁so - ▁her - ▁do - ▁have - ▁of - ▁with - ▁go - ▁know - ▁not - ▁was - ▁on - ▁don - y - ▁him - ▁one - ▁like - ▁there - '%' - ▁pw - ▁be - ▁at - ▁told - ▁good - ▁will - ▁my - ▁all - ▁or - c - er - p - ▁how - ▁ah - r - ▁but - ▁them - ▁see - ▁get - ▁can - i - ▁when - ▁going - ▁about - ▁mean - ▁this - k - ▁your - ▁by - ▁if - u - ▁come - ▁up - ▁tell - g - ▁said - ▁then - ▁now - ▁yeah - o - ▁out - al - ra - ▁because - ▁time - ▁well - ▁would - ▁p - ▁from - h - ar - f - ▁swear - ▁went - b - ▁really - or - ▁want - ri - ▁home - ▁work - ve - ▁take - ▁got - ▁just - l - ▁uh - ▁why - en - ▁even - ▁am - ▁who - ▁make - ▁day - '-' - in - ▁something - ▁some - ou - ▁us - ▁okay - ▁where - ▁does - ▁has - ▁thank - ▁c - ▁his - th - ▁back - ▁fine - ▁today - ly - ▁b - ▁oh - ▁doing - ▁everything - ▁here - le - ▁thing - ▁two - ▁anyway - li - ▁had - ▁still - ▁say - ro - ▁after - ce - ▁hello - ▁ma - ▁call - w - ▁listen - il - ▁should - ▁girl - ▁f - z - ▁too - ▁let - ▁understand - ▁may - ▁much - ▁think - ch - ir - ha - ▁other - ▁tomorrow - ▁were - ▁people - es - ▁year - di - ba - ▁right - el - ▁things - ▁house - v - ▁actually - un - ▁an - ▁give - ▁only - ▁better - pe - ▁need - ▁buy - ▁de - ne - ▁ha - ur - ion - ▁made - la - ▁willing - ▁nothing - ▁called - ▁night - ▁yesterday - se - ▁came - ▁lot - ter - ▁g - po - ▁find - ry - ▁car - ▁over - ic - ▁stay - ▁eat - ent - ▁always - ▁very - 'on' - ▁put - ▁ramadan - ▁those - ▁hear - is - ▁talk - ▁three - ▁anything - ▁mo - ▁little - ▁been - ▁already - fi - ation - ke - ▁first - ▁look - it - ▁won - ▁mom - ▁way - ▁before - ▁ok - ▁last - fa - ▁cook - vi - ▁hi - ▁same - ▁thought - ▁also - um - ate - ▁money - ▁start - ▁place - us - ▁morning - ▁could - ▁ask - ▁bring - ▁bit - ▁lo - ▁leave - ▁man - ▁left - ine - ▁days - ge - ▁la - ▁week - ▁friend - ▁problem - ▁sister - ▁allah - ▁feel - ▁every - ▁more - fe - ▁long - ▁hundred - ▁j - ▁eh - ho - ca - em - ▁talking - ▁exam - ▁next - ▁new - ▁fun - ▁took - ▁alright - co - ▁w - ▁um - ▁eid - ▁brother - ▁our - gh - ow - ▁o - ▁four - ni - wa - ▁else - ▁finish - bo - ▁sleep - ▁bless - ▁dear - ▁since - ▁play - ▁name - hi - ▁coming - ▁many - et - ▁usual - ▁con - ▁maybe - ▁off - bi - ▁than - ▁any - ▁mother - ▁son - om - ▁their - ▁keep - ▁dinner - ▁ten - ▁half - ▁help - ▁bad - and - ▁pass - ▁hot - ▁guy - ▁least - ▁down - ▁bought - ▁dinars - ▁working - ▁around - ▁normal - ▁poor - ▁stuff - ▁hope - ▁used - ▁again - ▁bro - ul - ▁phone - ▁ex - ▁done - ▁six - ▁na - ▁month - ▁tired - ▁check - ▁show - ▁together - oo - ▁later - ▁past - ▁five - ▁watch - ya - ▁coffee - ment - ut - ▁plan - ▁great - ▁daughter - j - ▁another - side - ▁change - ▁yet - ting - ▁until - ▁honestly - ▁whole - ol - ▁care - ▁sure - able - id - ▁big - ▁spend - ▁exactly - ▁boy - ▁course - ▁end - ▁please - ▁started - he - up - ▁found - ▁saw - ▁family - ▁asked - ▁enough - ▁during - ▁rest - ▁which - ▁gave - ▁true - ▁while - ▁job - ▁el - ▁each - ▁away - ▁kids - ▁goes - less - ▁twenty - ▁eight - ▁someone - ▁cha - ▁clothes - ah - ▁myself - ▁nice - ▁late - ▁old - ▁real - age - ant - ▁fast - ▁add - ▁hard - ▁these - ful - im - ▁close - ive - ▁dad - ▁pay - ies - ▁dude - ▁alone - ▁far - ance - ▁dis - ▁seven - ▁isn - ▁pro - our - ▁thousand - ▁break - ▁hour - ▁wait - ▁brought - ▁open - ▁un - ▁wedding - ▁walk - ▁father - ▁ka - ▁second - x - ▁saturday - ▁salad - ▁win - ▁everyone - ▁water - ▁tunis - ▁remember - ity - ▁wake - ▁minute - ▁school - ▁sunday - ▁own - ▁shop - ▁cold - ▁meet - ▁wear - ever - ▁send - ▁early - ▁gra - tic - ▁short - ▁use - ▁sometimes - hou - ▁love - ▁prepare - ▁sea - ▁study - ure - ▁com - qui - ▁hand - ▁both - ja - ▁summer - ▁wrong - ▁wanted - che - ▁miss - ▁try - ▁iftar - ▁yourself - q - ▁live - war - ▁expensive - ▁getting - ▁waiting - ▁once - ▁kh - ▁forgot - ▁nine - ▁anymore - ▁soup - ▁uncle - ▁beach - ▁saying - ▁into - ▁having - ▁brik - ▁room - ▁food - ▁visit - ▁matter - ▁thirty - ▁taking - ▁rain - ▁aunt - ▁never - ▁pick - ▁tunisia - ▁health - ▁head - ▁cut - ▁fasting - ▁sick - ▁friday - ▁forget - ▁monday - ▁become - ▁dress - ated - ▁most - wi - ▁hang - ▁life - ▁fish - ▁happy - ▁delicious - ▁deal - ▁finished - ble - ▁studying - ▁weather - ▁making - ▁cost - ▁bl - ▁stayed - ▁guess - ▁teach - ▁stop - ▁near - ▁watching - ▁without - ▁imagine - ▁seriously - fl - ▁speak - ▁idea - ▁must - ▁normally - ▁turn - ize - ▁clean - ▁tv - ▁meat - ▁woke - ▁example - ▁easy - ▁sent - ▁sell - over - ▁fifty - ▁amazing - ▁beautiful - ▁whatever - ▁enjoy - ▁talked - ▁believe - ▁thinking - ▁count - ▁almost - ▁longer - ▁afternoon - ▁hair - ▁front - ▁earlier - ▁mind - ▁kind - ▁tea - ▁best - ▁rent - ▁picture - ▁cooked - ▁price - ight - ▁soon - ▁woman - ▁otherwise - ▁happened - ▁story - ▁luck - ▁high - ▁happen - ▁arrive - ▁paper - ga - ▁quickly - ▁looking - ub - ▁number - ▁staying - ▁sit - man - ack - ▁important - ▁either - ▁person - ▁small - ▁free - ▁crazy - ▁playing - ▁kept - ▁part - ▁game - law - ▁till - uck - ▁ready - ▁might - ▁gone - ▁full - ▁fix - ▁subject - ▁laugh - ▁doctor - ▁welcome - ▁eleven - ▁sleeping - ▁heat - ▁probably - ▁such - ▁café - ▁fat - ▁sweet - ▁married - ▁drink - ▁move - ▁outside - ▁especially - ▁group - ji - ▁market - ▁through - ▁train - ▁protect - ▁turned - ▁red - ▁busy - ▁light - ▁noise - ▁street - ▁manage - ▁piece - ▁sitting - gue - ▁sake - ▁party - ish - ▁young - ▁case - ▁cool - huh - ▁marwa - ▁drive - ▁pray - clock - ▁couscous - ▁spent - ▁felt - ▁hopefully - ▁everybody - ▁living - ▁pain - line - ▁between - ▁match - ▁prayer - que - ian - ▁facebook - ▁spi - ▁eye - ▁children - ▁tonight - ▁mohamed - ▁understood - ▁black - ▁husband - ▁rid - ▁kitchen - ▁face - ▁swim - ▁kid - ▁invite - ▁cup - ▁grilled - ▁wife - ▁cousin - ▁drop - ▁wow - ▁table - ▁du - ▁bored - ▁neighborhood - ▁agree - ▁bread - ▁hamma - ▁straight - ▁tuesday - ▁anyone - ▁lunch - ade - ▁himself - ▁gather - ▁wish - ▁fifteen - ▁wednesday - ▁die - ▁thursday - ▁color - ▁asleep - ▁different - ▁whether - ▁ago - ▁middle - ▁class - ▁cake - shirt - ▁fight - ▁clear - ▁test - ▁plus - ▁sousse - ▁beginning - ▁result - ▁learn - ▁crowded - ▁slept - ▁shoes - ▁august - ▁pretty - ▁white - ▁apparently - ▁reach - ▁mariem - ▁return - ▁road - ▁million - ▁stand - ▁paid - ▁word - ious - ▁few - ▁breakfast - ▁post - ▁kilo - ▁chicken - ▁grade - ▁read - ▁accept - ▁birthday - ▁exhaust - ▁point - ▁july - ▁patience - ▁studies - ▁trouble - ▁along - ▁worry - ▁follow - ▁hurt - ▁afraid - ▁trip - ▁ahmed - ▁remain - ▁succeed - ▁mercy - ▁difficult - ▁weekend - ▁answer - ▁cheap - ▁repeat - ▁auntie - ▁sign - ▁hold - ▁under - ▁olive - ▁mahdi - ▁sfax - ▁annoy - ▁dishes - ▁message - ▁business - ▁french - ▁serious - ▁travel - ▁office - ▁wonder - ▁student - ▁internship - ▁pepper - ▁knew - ▁kill - ▁sauce - ▁herself - ▁hammamet - ▁damn - ▁mix - ▁suit - ▁medicine - ▁remove - ▁gonna - ▁company - ▁quarter - ▁shopping - ▁correct - ▁throw - ▁grow - ▁voice - ▁series - gotten - ▁taste - ▁driving - ▁hospital - ▁sorry - ▁aziz - ▁milk - ▁green - ▁baccalaureate - ▁running - ▁lord - ▁explain - ▁angry - ▁build - ▁fruit - ▁photo - é - ▁crying - ▁baby - ▁store - ▁project - ▁france - ▁twelve - ▁decide - ▁swimming - ▁world - ▁preparing - ▁special - ▁session - ▁behind - ▁vegetable - ▁strong - ▁fatma - ▁treat - ▁cream - ▁situation - ▁settle - ▁totally - ▁stopped - ▁book - ▁honest - ▁solution - ▁vacation - ▁cheese - ▁ahead - ▁sami - ▁focus - ▁scared - ▁club - ▁consider - ▁final - ▁naturally - ▁barely - ▁issue - ▁floor - ▁birth - ▁almighty - ▁engagement - ▁blue - ▁empty - ▁soccer - ▁prophet - ▁ticket - ▁indeed - ▁write - ▁present - ▁patient - ▁available - ▁holiday - ▁leaving - ▁became - ▁reason - ▁apart - ▁impossible - ▁shame - ▁worried - ▁body - ▁continue - ▁program - ▁stress - ▁arabic - ▁round - ▁taxi - ▁transport - ▁third - ▁certain - ▁downstairs - ▁neighbor - ▁directly - ▁giving - ▁june - ▁mini - ▁upstairs - ▁mistake - ▁period - ▁catch - ▁buddy - ▁success - ▁tajine - ▁excuse - ▁organize - ▁question - ▁suffer - ▁remind - ▁university - ▁downtown - ▁sugar - ▁twice - ▁women - ▁couple - ▁everyday - ▁condition - ▁obvious - ▁nobody - ▁complete - ▁stomach - ▁account - ▁september - ▁choose - ▁bottle - ▁figure - ▁instead - ▁salary - '0' - '1' - '3' - '2' - '5' - '7' - '4' - '9' - '8' - / - ° - '6' - è - $ - ï - <sos/eos> src_token_list: - <blank> - <unk> - ّ - ي - ا - ِ - ل - َ - و - ه - ة - م - ر - ك - ▁ما - ُ - ب - ش - د - ت - ▁في - َّ - ▁ن - ▁ي - ▁ت - ن - ▁لا - ح - ▁ه - س - وا - ▁م - ف - ▁إي - ع - ▁ب - ها - ط - ى - ق - ▁الل - ▁أ - ج - ▁والل - ▁و - ▁إيه - ▁ا - ▁يا - ز - ▁تو - ▁بش - ص - ▁أه - خ - ات - ▁إنت - ▁أنا - نا - ▁شن - ▁ق - ▁ش - ▁ك - يت - ين - ▁ف - ار - ▁قال - ▁باهي - ▁ع - ▁من - ▁ل - ▁مش - ▁كان - ▁حت - ▁ول - هم - ▁ر - ان - ▁س - ض - ني - ▁بال - ▁على - ▁متاع - ▁كي - ▁ال - ▁ح - ▁كل - ▁آنا - ▁الم - ▁خ - ▁الس - ▁وال - ون - ور - ▁أم - ▁هك - ▁آش - ▁الد - ▁عاد - ▁ج - ▁معناها - ▁مع - اش - ▁الص - ▁نهار - ▁لل - لها - ▁تي - ▁رب - ▁خاطر - ▁أكهو - غ - ▁شي - الل - ام - تها - ▁ون - ▁آك - ▁فهمت - وم - ▁موش - مشي - ▁ص - ▁اليوم - ▁مر - ست - ▁الب - ▁لاباس - تلي - ▁الكل - ▁عال - ذ - ▁فم - ▁الك - ▁حاجة - ▁شوي - اكا - ▁ياخي - ▁هاني - ▁صح - اس - ▁آه - ▁برشة - ▁الن - ▁وت - ▁الج - لك - ▁راهو - سم - ▁الح - مت - ▁الت - ▁بعد - اج - عد - ▁انشا - وش - لت - ▁وين - ث - ▁ولا - ▁باش - ▁فيها - نت - ▁إ - ▁الأ - ▁الف - ▁إم - ▁واحد - ▁ألو - ▁عندي - ▁أك - ▁خل - ▁وي - ▁تعمل - أ - ▁ريت - ▁وأ - ▁تعرف - بت - ▁الع - ▁مشيت - ▁وه - ▁حاصيلو - ▁بالل - ▁نعمل - ▁غ - ▁تجي - ▁يجي - ▁كيفاش - ▁عملت - ظ - اك - ▁هاو - ▁اش - ▁قد - ▁نق - ▁د - ▁زادا - ▁فيه - رة - ▁بر - ▁الش - ▁ز - ▁كيما - ▁الا - ند - عم - ▁نح - ▁بنتي - ▁نمشي - ▁عليك - ▁نعرفش - ▁كهو - ▁وم - ▁ط - تي - ▁خير - ▁آ - مش - ▁عليه - له - حت - ▁إيا - ▁أحنا - ▁تع - الا - عب - ▁ديما - ▁تت - ▁جو - ▁مالا - ▁أو - ▁قلتلك - ▁معنتها - لنا - ▁شكون - ▁تحب - بر - ▁الر - ▁وا - ▁الق - اء - ▁عل - ▁البارح - ▁وخ - ▁سافا - ▁هوما - ▁ولدي - ▁ - ▁نعرف - يف - رت - ▁وب - ▁روح - ▁علاش - ▁هاذاك - ▁رو - وس - ▁جا - ▁كيف - طر - ▁غادي - يكا - عمل - ▁نحب - ▁عندك - ▁وما - ▁فر - اني - ▁قلتله - ▁الط - فر - ▁دار - ▁عليها - ▁يعمل - ▁نت - ▁تح - باح - ▁ماهو - ▁وكل - ▁وع - قت - ▁فهمتك - عر - ▁وس - ▁تر - ▁سي - يلة - ▁قلت - ▁رمضان - صل - ▁آما - ▁الواحد - ▁بيه - ▁ثلاثة - ▁فهمتني - ▁ها - بط - ▁مازال - قل - ▁بالك - ▁معناتها - ▁ور - ▁قلتلها - ▁يس - رب - ▁ام - ▁وبعد - ▁الث - ▁وإنت - ▁بحذا - ▁لازم - ْ - ▁بن - قرا - سك - ▁يت - خل - ▁فه - عت - ▁هاك - ▁تق - ▁قبل - ▁وك - ▁نقول - ▁الز - حم - ▁عادش - حكي - وها - بة - نس - طل - ▁علاه - ذا - ▁سا - ▁طل - الي - ▁يق - ▁دو - حوا - حد - ▁نشوف - نة - ▁لي - ▁تك - ▁نا - ▁هاذ - ▁خويا - ▁المر - ▁وينك - ▁البر - ▁أتو - ينا - ▁حل - ولي - ▁ثم - ▁عم - ▁آي - ▁قر - از - ▁وح - كش - بعة - ▁كيفاه - ▁نع - ▁الحمدلله - ▁ياسر - ▁الخ - ▁معاك - ▁معاه - ▁تقول - دة - ▁حكاية - تش - ▁حس - ▁غدوا - ▁بالحق - روا - وز - ▁تخ - ▁العيد - رجع - ▁بالي - ▁جات - ▁وج - حة - ▁وش - ▁آخر - ▁طا - ▁مت - لقا - تك - ▁مس - ▁راني - كون - ▁صاحب - ▁هاكا - ▁قول - ▁عر - ▁عنده - ▁يلزم - ▁هاذا - ▁يخ - ▁وقتاش - ▁وقت - بع - ▁العش - ▁هاذي - هاش - ينة - ▁هاذاكا - عطي - ▁تنج - ▁باهية - نيا - فت - ▁يحب - ▁تف - ▁أهلا - وف - ▁غدوة - ▁بيك - ▁بد - عن - ▁در - ▁ننج - هار - ▁الحكاية - مون - وق - ▁نورمال - ▁عندها - خر - ▁بو - ▁حب - ▁آكا - ▁وف - ▁هاذيكا - ▁ديجا - ▁وق - ▁طي - لتل - بعث - ▁تص - رك - ▁مانيش - ▁العادة - ▁شوف - ضر - ▁يمشي - ▁نعملوا - ▁عرفت - ▁زال - ▁متع - ▁عمل - ▁بيها - ▁نحكي - اع - ▁نج - معة - ▁والكل - عناها - ▁يعي - ▁نجي - ستن - ▁هاذيك - ▁عام - ▁فلوس - قة - تين - ▁بالقدا - لهم - ▁تخدم - ▁ٱ - ▁شيء - ▁راهي - ▁جاب - ولاد - ابل - ▁ماك - عة - ▁نمشيوا - وني - شري - بار - انس - ▁وقتها - ▁جديد - ▁يز - ▁كر - ▁حاسيلو - ▁شق - ▁اه - ▁سايي - ▁انشالل - رج - مني - ▁بلا - ▁صحيح - ▁غير - ▁يخدم - مان - وكا - ▁عند - ▁قاعدة - ▁تس - ربة - ▁راس - ▁حط - ▁نكل - تني - ▁الو - سيون - ▁عندنا - ▁لو - ▁ست - صف - ▁ض - ▁كامل - ▁نخدم - ▁يبدا - ▁دونك - ▁أمور - رات - ▁تونس - بدا - ▁تحكي - ▁سو - ▁جاي - ▁وحدة - ▁ساعة - حنا - ▁بكري - ▁إل - ▁وبر - ▁كم - ▁تبدا - ارة - ادي - رق - لوا - ▁يمكن - ▁خاط - ▁وص - جين - ▁هاذاي - ▁هز - قد - ▁قل - ▁وكهو - ▁نص - ▁دي - لقى - ▁وأنا - سين - ▁يح - ▁ماشي - ▁شو - ▁خذيت - امات - ▁كنت - خرج - ▁لقيت - رتاح - كس - ▁حاجات - ▁مريق - ▁مل - ليفون - اوا - ▁شفت - ▁عاملة - ▁تن - ▁والا - سأل - ▁حد - ▁قاللك - ▁العباد - ▁عالاخ - ▁وآك - ▁ماني - ▁ناخذ - ▁حم - ▁الإ - ▁ماضي - ▁ث - الة - ▁أخرى - رين - ▁تشوف - ▁نخرج - ▁أربعة - ▁ألف - نيش - ▁هاي - آ - ▁فيك - رشة - ولة - فلة - ▁بابا - ▁أما - ▁روحي - ▁فيهم - ▁رج - ▁ليك - ونس - يرة - ▁وأكهو - ندي - ▁صار - شك - ▁نرو - ▁آكهو - ▁تش - ▁غاديكا - ▁معاها - ▁لب - ▁أذاكا - ▁آني - ▁يوم - عملوا - ▁نقعد - دوا - ▁عد - سمع - متني - ▁الخدمة - ▁مازلت - ▁قعدت - ايا - ▁برك - قعد - ▁خرجت - ضح - ▁قالل - ▁يقول - ▁وفي - ▁حق - ختي - ▁يعني - خدم - ▁جيت - ▁نرمال - طف - ▁عجب - ▁تقعد - ▁مشينا - اية - ▁خدمة - لدي - روف - ▁الفطر - ▁مشكل - ▁سل - ▁وآنا - الط - ▁بالس - ▁هانا - ▁أوه - ▁أذيكا - ▁وإ - ▁عليهم - ▁حالة - جت - قضي - ▁لق - ▁ونصف - سعة - عطيه - عاو - خانة - ▁مخ - ▁شبيك - بيعة - ▁أهوك - يني - ▁تعد - ▁خال - ▁قريب - ▁راك - ▁قالت - ▁لتو - ▁أكثر - اعة - ▁يظهرلي - ▁ماشية - سمعني - ▁نسيت - ▁ينج - ▁الحمدلل - هدي - ▁وشن - ▁تطي - ▁هنا - ▁نسمع - ▁إنتوما - ▁نحكيلك - ▁قاعد - ▁اسمعني - خرين - إ - ماعة - ▁بالر - ▁دا - ▁عمر - ▁نشري - ▁قهوة - ▁تبارك - ▁صب - ▁مشات - غر - ▁شريت - ▁عامل - ▁زوج - ثنين - ▁برب - ريق - ▁نكم - ▁لم - بيب - ▁مياة - ▁مالل - ▁قعد - ▁سخون - قس - ▁وحده - ▁اسمع - ▁خمسة - ▁غالي - ▁الأو - رلي - ▁العظيم - ▁ترو - تهم - كري - ▁نجيب - ▁جملة - قول - ▁قلتلي - ▁إيجا - ▁يقعد - ▁إيام - ▁يعطيك - ▁نخل - ▁دب - يمة - رهبة - ▁نهز - ▁محم - ▁بين - غار - ▁نحنا - ▁بون - ▁الغ - ▁شهر - ▁بار - رقة - ▁نطي - ئ - ترو - ▁ملا - ▁الكرهبة - ▁باه - ▁عالإخ - ▁عباد - ▁بلاصة - ▁مشى - بيع - ▁نفس - ▁عملنا - ▁واح - ▁أحلاه - ▁بحذاك - ▁لأ - ▁دخ - باب - ▁ودر - ▁غالب - ▁ناكل - ▁مثلا - ء - ▁راقد - ▁تفر - ▁الوقت - ▁تاخذ - حذا - نتر - ▁نبدا - ▁حال - ▁مريم - الم - ▁جمعة - رجول - ▁معايا - ▁تخرج - ▁باس - ▁ساعات - ▁عندهم - ▁نتفر - مسة - ▁الجمعة - بعين - ▁أكاهو - ▁ميش - مراة - ▁خذا - ▁ظ - ▁سيدي - ▁معاي - ▁شبيه - ▁حكا - ▁سف - ▁بعضنا - ▁بالض - ▁ليلة - ▁زعما - ▁الحق - مضان - ▁صعيب - ▁قالتلك - ً - ملة - ▁بق - عرف - لاطة - ▁خرج - ▁أخت - ▁تقوللي - ▁معانا - ▁صغير - ▁إسمه - ▁بعض - ▁العام - ▁علينا - ▁يتع - ▁فاش - ▁شع - ▁معاهم - ▁يسالش - ▁لهنا - ▁سمعت - ▁البار - ▁نتصو - ▁الاخ - ▁وكان - وبة - دمة - ▁كون - ▁مبعد - ▁تسمع - ▁بعيد - ▁تاكل - ▁نلقا - لامة - لاثة - ▁ذ - ▁تحس - ▁الواح - ▁لدار - ▁فاتت - ▁تاو - ▁أحوالك - ▁عاملين - ▁كبيرة - عجب - ▁بنت - ▁بيدي - ▁حكيت - ▁تحط - ▁مسكينة - ▁هاذوكم - ▁نزيد - لاث - ▁عشرة - ▁عيني - ▁تعب - ▁ياكل - ▁وزيد - ▁طول - ▁حمدلله - ▁وقتاه - ▁معناه - ▁وآش - ▁ووه - ▁وواحد - ▁نشوفوا - ▁عيد - ▁بصراحة - ▁بحذانا - ▁قاعدين - ▁راجل - ▁وحدي - ▁وعشرين - ▁لين - ▁خايب - ▁قالتله - ▁تهز - عيد - ▁كبير - ▁يعرف - ▁عارف - ▁الفلوس - ▁زايد - ▁خدمت - ▁هاذوما - ▁سلاطة - ▁فارغة - ▁ساعتين - ▁تبد - ▁راو - ▁مائة - ▁بعضهم - ▁ظاهرلي - ▁الفازة - كتب - ▁القهوة - سبوك - ▁زاد - ▁ضرب - حكيلي - ▁فوق - ▁عاود - ▁راي - ▁ومبعد - ▁حوايج - ▁دخلت - ▁يقوللك - ▁زيد - ▁زلت - لفزة - ▁وقال - ▁يهب - ▁يلزمني - ▁الحمد - ▁أذي - طبيعت - ▁دورة - ▁عالأقل - ▁آذاك - ▁وبال - ▁الجاي - عطيني - ▁ياخذ - ▁احكيلي - ▁نهبط - ▁رقدت - بلاصة - ▁عزيز - ▁صغار - ▁أقسم - ▁جيب - ▁وصلت - ▁أحوال - ▁جيست - ▁جماعة - سئل - ▁خوذ - ▁يهز - ▁الأخرى - ▁آلاف - ▁إسمع - ▁الحقيقة - ▁ناقص - ▁حاط - ▁موجود - عباد - ▁آذيك - ▁خارج - ▁الخير - ▁البنات - بقى - ▁طرف - ▁سينون - ▁ماذاب - ▁البحر - ▁نرقد - مدلله - ▁إيجى - ▁خالتي - ▁فازة - ▁بريك - ▁شريبتك - ▁تطلع - ؤ - ▁المشكلة - ▁طري - ▁مادام - ▁طلبت - ▁يلعب - ▁نعاود - ▁وحدك - ▁ظاهر - ٱ - ژ - ٍ - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: asr_weight: 0.3 mt_weight: 0.0 mtlalpha: 1.0 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: bpe src_token_type: bpe bpemodel: data/token_list/tgt_bpe_unigram1000/bpe.model src_bpemodel: data/token_list/src_bpe_unigram1000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/st_stats_raw_bpe1000_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: transformer encoder_conf: input_layer: conv2d num_blocks: 12 linear_units: 2048 dropout_rate: 0.1 output_size: 256 attention_heads: 4 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed num_blocks: 6 linear_units: 2048 dropout_rate: 0.1 extra_asr_decoder: transformer extra_asr_decoder_conf: input_layer: embed num_blocks: 2 linear_units: 2048 dropout_rate: 0.1 extra_mt_decoder: transformer extra_mt_decoder_conf: input_layer: embed num_blocks: 2 linear_units: 2048 dropout_rate: 0.1 required: - output_dir - src_token_list - token_list version: 0.10.6a1 distributed: true ``` </details> | 040698fe7f35875fda9c2e028dda0602 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 40k (uncased) Seed 2 intermediate checkpoint 40k 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/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 61993b4de7185a326073be421e415683 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | 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-2-40k') model = BertModel.from_pretrained("multiberts-seed-2-40k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | d41228264ca9aa5bef3346fd5ca46fe4 |
apache-2.0 | ['translation'] | false | fi-en * source group: Finnish * target group: English * OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md) * model: transformer-align * source language(s): fin * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opusTCv20210807+bt-2021-08-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-08-25.zip) * test set translations: [opusTCv20210807+bt-2021-08-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-08-25.test.txt) * test set scores: [opusTCv20210807+bt-2021-08-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-08-25.eval.txt) | 5d6a370b63d54a5dcb566f4582d8448c |
apache-2.0 | ['translation'] | false | words | BP | |---------|-------|-------|-------|--------|----| | newsdev2015-enfi.fin-eng | 27.1 | 0.550 | 1500 | 32104 | 0.988 | | newstest2015-enfi.fin-eng | 28.5 | 0.560 | 1370 | 27356 | 0.980 | | newstest2016-enfi.fin-eng | 31.7 | 0.586 | 3000 | 63043 | 1.000 | | newstest2017-enfi.fin-eng | 34.6 | 0.610 | 3002 | 61936 | 0.988 | | newstest2018-enfi.fin-eng | 25.4 | 0.530 | 3000 | 62325 | 0.981 | | newstest2019-fien.fin-eng | 30.6 | 0.577 | 1996 | 36227 | 0.994 | | newstestB2016-enfi.fin-eng | 25.8 | 0.538 | 3000 | 63043 | 0.987 | | newstestB2017-enfi.fin-eng | 29.6 | 0.572 | 3002 | 61936 | 0.999 | | newstestB2017-fien.fin-eng | 29.6 | 0.572 | 3002 | 61936 | 0.999 | | Tatoeba-test-v2021-08-07.fin-eng | 54.1 | 0.700 | 10000 | 75212 | 0.988 | | a63597b129473ae19d27de464116cb97 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fi-en - source_languages: fin - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'en'] - src_constituents: ('Finnish', {'fin'}) - tgt_constituents: ('English', {'eng'}) - src_multilingual: False - tgt_multilingual: False - long_pair: fin-eng - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-08-25.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-08-25.test.txt - src_alpha3: fin - tgt_alpha3: eng - chrF2_score: 0.7 - bleu: 54.1 - src_name: Finnish - tgt_name: English - train_date: 2021-08-25 00:00:00 - src_alpha2: fi - tgt_alpha2: en - prefer_old: False - short_pair: fi-en - helsinki_git_sha: 2ef219d5b67f0afb0c6b732cd07001d84181f002 - transformers_git_sha: 12b4d66a80419db30a15e7b9d4208ceb9887c03b - port_machine: LM0-400-22516.local - port_time: 2021-11-04-21:36 | 8fc4d23d27d85a429d3ea8066c5a476f |
mit | ['generated_from_trainer'] | false | nbme-roberta-large This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7825 | 7250d2b56042c1a1684a7b167ff868a6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1117 | 1.0 | 1850 | 0.9610 | | 0.8911 | 2.0 | 3700 | 0.8466 | | 0.8158 | 3.0 | 5550 | 0.7825 | | f1beb166cf9cb23f032a7873e7173ae7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_finetuned_Balance_Upsampling_SPEECH_TEXT_DISPLAY_v1 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: 2.6982 - Accuracy: 0.7759 - F1: 0.7743 | 3478be8dc89cd5c84f0c6076afa96241 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 04fb21edc90d4ea47c3489e1abcefd34 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.5321 | 1.0 | 7958 | 1.3225 | 0.7271 | 0.7391 | | 0.2967 | 2.0 | 15916 | 1.3868 | 0.7574 | 0.7601 | | 0.1821 | 3.0 | 23874 | 1.4753 | 0.7513 | 0.7515 | | 0.1193 | 4.0 | 31832 | 1.7028 | 0.7588 | 0.7596 | | 0.0722 | 5.0 | 39790 | 1.8155 | 0.7615 | 0.7599 | | 0.041 | 6.0 | 47748 | 2.1622 | 0.7695 | 0.7678 | | 0.0258 | 7.0 | 55706 | 2.3871 | 0.75 | 0.7462 | | 0.0149 | 8.0 | 63664 | 2.6135 | 0.7571 | 0.7524 | | 0.0076 | 9.0 | 71622 | 2.7974 | 0.7648 | 0.7617 | | 0.0051 | 10.0 | 79580 | 2.6982 | 0.7759 | 0.7743 | | 2a9b12458e76929699e173f672041cae |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-SMALL-EL16-DL1 (Deep-Narrow version) T5-Efficient-SMALL-EL16-DL1 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 was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. | b11b2ed9b9b91272a89bf25bb7183334 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-small-el16-dl1** - is of model type **Small** with the following variations: - **el** is **16** - **dl** is **1** It has **71.01** million parameters and thus requires *ca.* **284.04 MB** of memory in full precision (*fp32*) or **142.02 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | | 169a27a923e921cbb4a92f48e055a7f3 |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-4 14B is a L40-D5120 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. Use https://github.com/BlinkDL/ChatRWKV to run it. ctx_len = 1024 n_layer = 40 n_embd = 5120 Final checkpoint: RWKV-4-Pile-14B-20230213-8019.pth : Trained on the Pile for 331B tokens. * Pile loss 1.7579 * LAMBADA ppl 3.81, acc 71.05% * PIQA acc 77.42% * SC2016 acc 75.57% * Hellaswag acc_norm 70.24% * WinoGrande acc 62.98% | fbd7f778fc81f18d671803828bed087f |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | mnist-digit-classification-2022-09-04 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the mnist dataset. It achieves the following results on the evaluation set: - Loss: 0.0319 - Accuracy: 0.9923 | d96b1eb3d3e72914082657bca24a048c |
apache-2.0 | ['image-classification', 'vision', '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: 5.0 | 0ce2c4ce729005984f21542a4dd4cb34 |
apache-2.0 | ['translation'] | false | tur-lit * source group: Turkish * target group: Lithuanian * OPUS readme: [tur-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-lit/README.md) * model: transformer-align * source language(s): tur * target language(s): lit * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-lit/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-lit/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-lit/opus-2020-06-17.eval.txt) | 2e4221e3e94e6356e38cb44c8474644c |
apache-2.0 | ['translation'] | false | System Info: - hf_name: tur-lit - source_languages: tur - target_languages: lit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-lit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tr', 'lt'] - src_constituents: {'tur'} - tgt_constituents: {'lit'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-lit/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-lit/opus-2020-06-17.test.txt - src_alpha3: tur - tgt_alpha3: lit - short_pair: tr-lt - chrF2_score: 0.631 - bleu: 35.6 - brevity_penalty: 0.9490000000000001 - ref_len: 8285.0 - src_name: Turkish - tgt_name: Lithuanian - train_date: 2020-06-17 - src_alpha2: tr - tgt_alpha2: lt - prefer_old: False - long_pair: tur-lit - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | a954cbe4cc6c0c2a1e852cf4ea07fd77 |
apache-2.0 | ['generated_from_keras_callback'] | false | NAOKITY/bert-finetuned-squad 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: 2.1438 - Validation Loss: 0.0 - Epoch: 2 | 3ccd158e0467205187ac35bc0a091284 |
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': 1149, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 0e14a0fab8689f4226d97a7c5c0bfa7d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1483 | 0.0 | 0 | | 2.1484 | 0.0 | 1 | | 2.1438 | 0.0 | 2 | | 7a393ba0a13e018718768c7800ac0c0c |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-lowR100-2-cased-DA-20 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7801 | 6d22904c5ab921e140f64ab937890816 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 - mixed_precision_training: Native AMP | d028852d3032a3c69559b78efa8cabbc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.4515 | 1.0 | 1 | 8.1791 | | 6.4671 | 2.0 | 2 | 6.0155 | | 6.533 | 3.0 | 3 | 5.9784 | | 5.8654 | 4.0 | 4 | 5.2092 | | 5.5458 | 5.0 | 5 | 6.1062 | | 5.1806 | 6.0 | 6 | 5.0913 | | 4.8797 | 7.0 | 7 | 4.3025 | | 4.6975 | 8.0 | 8 | 4.8598 | | 4.2859 | 9.0 | 9 | 4.2301 | | 4.3584 | 10.0 | 10 | 4.0683 | | 4.0203 | 11.0 | 11 | 2.7986 | | 3.977 | 12.0 | 12 | 4.1575 | | 3.4077 | 13.0 | 13 | 3.6507 | | 3.313 | 14.0 | 14 | 2.8674 | | 3.0962 | 15.0 | 15 | 2.5103 | | 2.8883 | 16.0 | 16 | 3.1318 | | 2.9623 | 17.0 | 17 | 2.1316 | | 2.5544 | 18.0 | 18 | 2.7741 | | 2.9957 | 19.0 | 19 | 2.9045 | | 2.749 | 20.0 | 20 | 2.8824 | | 2.291 | 21.0 | 21 | 2.7450 | | 2.3373 | 22.0 | 22 | 2.3774 | | 2.6506 | 23.0 | 23 | 2.5515 | | 2.6736 | 24.0 | 24 | 2.2106 | | 2.3845 | 25.0 | 25 | 2.3166 | | 2.3762 | 26.0 | 26 | 2.3221 | | 2.4184 | 27.0 | 27 | 2.8996 | | 2.6826 | 28.0 | 28 | 2.1793 | | 2.4678 | 29.0 | 29 | 2.4268 | | 2.2998 | 30.0 | 30 | 1.8153 | | 2.7085 | 31.0 | 31 | 2.4401 | | 2.1231 | 32.0 | 32 | 3.3329 | | 2.1349 | 33.0 | 33 | 1.9675 | | 2.4647 | 34.0 | 34 | 3.0172 | | 2.3552 | 35.0 | 35 | 1.8550 | | 2.2843 | 36.0 | 36 | 2.7737 | | 2.2164 | 37.0 | 37 | 3.4890 | | 2.2118 | 38.0 | 38 | 3.4251 | | 2.3133 | 39.0 | 39 | 2.6806 | | 1.9773 | 40.0 | 40 | 2.7801 | | 775f3fc66b0bcd941d9b49b33152cd0e |
mit | [] | false | Turkish ELECTRA model We release a base ELEC**TR**A model for Turkish, that was trained on the same data as *BERTurk*. > ELECTRA is a new method for self-supervised language representation learning. It can be used to > pre-train transformer networks using relatively little compute. ELECTRA models are trained to > distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to > the discriminator of a GAN. More details about ELECTRA can be found in the [ICLR paper](https://openreview.net/forum?id=r1xMH1BtvB) or in the [official ELECTRA repository](https://github.com/google-research/electra) on GitHub. | 6c1dbc4d589a9aa2d40455b1d46c5ff7 |
mit | [] | false | Stats The current version of the model is trained on a filtered and sentence segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). The final training corpus has a size of 35GB and 44,04,976,662 tokens. Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model on a TPU v3-8 for 1M steps. | f7eebe83e57c5f18e2bd3b98ea7ea329 |
mit | [] | false | Model weights [Transformers](https://github.com/huggingface/transformers) compatible weights for both PyTorch and TensorFlow are available. | Model | Downloads | ------------------------------------------------ | --------------------------------------------------------------------------------------------------------------- | `dbmdz/electra-base-turkish-cased-discriminator` | [`config.json`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-turkish-cased-discriminator/vocab.txt) | 1aa4bb0158b66cb29859141eecc9cce1 |
mit | [] | false | Usage With Transformers >= 2.8 our ELECTRA base cased model can be loaded like: ```python from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator") model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator") ``` | 244e4a7b14e22f991c7989455292cecf |
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