license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2986 | 1.0 | 835 | 0.1939 | 0.8077 | | 0.1547 | 2.0 | 1670 | 0.1813 | 0.8351 | | 0.1003 | 3.0 | 2505 | 0.1757 | 0.8513 | | 4ed9ad06d6bd0059fd5cbfbef4da8767 |
apache-2.0 | ['t5-small', 'text2text-generation', 'natural language generation', 'conversational system', 'task-oriented dialog'] | false | t5-small-nlg-multiwoz21 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. | 07e4a46d12aea81d85b9c925fe3bcc6f |
apache-2.0 | ['t5-small', 'text2text-generation', 'natural language generation', 'conversational system', 'task-oriented dialog'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 10.0 | 541df7bd03d2c2779c77a5119de7c4eb |
apache-2.0 | ['translation'] | false | opus-mt-en-bcl * source languages: en * target languages: bcl * OPUS readme: [en-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-bcl/README.md) * dataset: opus+bt * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.zip) * test set translations: [opus+bt-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.test.txt) * test set scores: [opus+bt-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-bcl/opus+bt-2020-02-26.eval.txt) | cd583e34e51139eea6ac082df95f1241 |
apache-2.0 | ['generated_from_trainer'] | false | fnet-large-finetuned-rte This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7528 - Accuracy: 0.6426 | 07b55c41c534262c86a98033e9074e20 |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | a1fd78e73e9cf2e9906183b56b265afb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7105 | 1.0 | 623 | 0.6887 | 0.5740 | | 0.6714 | 2.0 | 1246 | 0.6742 | 0.6209 | | 0.509 | 3.0 | 1869 | 0.7528 | 0.6426 | | bc2c6df6f24109051ba97b273d01134d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - a-photo-of-simbatheog These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "simbatheog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: A photo of simbatheog in a bucket     | 330c543846bc24e602fa4d0a85a89f56 |
afl-3.0 | [] | false | This model is used to detect **abusive speech** in **Code-Mixed Kannada**. It is finetuned on MuRIL model using Code-Mixed Kannada abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABEL_1 :-> Abusive | 86a151d3a4fd11c812710604018a6f06 |
cc0-1.0 | [] | false |  [](https://www.patreon.com/sebastiankamph) | bf2cdccc3c95eac26889959938852e88 |
cc0-1.0 | [] | false | Vintage cream photo film Based on SD 2.1 768x768 **Token word: vntgcrm style** **Example prompt to start out with** RAW candid cinema, woman portrait, vntgcrm style, 16mm, ((remarkable color)), (ultra realistic) Negative: ugly, disfigured, deformed, too many hands, makeup, cartoon, render **Support my work on Patreon for Early access model releases** https://www.patreon.com/sebastiankamph **AI Art, Stable diffusion guides and tutorials on Youtube** https://www.youtube.com/@sebastiankamph **Chat in our community discord** https://discord.com/invite/dFB7zuXyFY **Installation** Download the .ckpt and the .yaml file. Put them inside \stable-diffusion-webui\Models\Stable-diffusion\ https://huggingface.co/SebastianKamphYT/VintageCream/blob/main/VintageCream.ckpt https://huggingface.co/SebastianKamphYT/VintageCream/blob/main/VintageCream.yaml | 1778d3efdcb763e49d7b0e286ba793c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 7405d8cf1316f709a695c7bbc90ec8c5 |
creativeml-openrail-m | [] | false | model by no3 This your waifu-diffusion v1.4 model fine-tuned kat concept taught to waifu-diffusion v1.4 with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_kaatt** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts). | 31cdecbe4799e3fc71bb610a62a830f9 |
creativeml-openrail-m | [] | false | note If you want to to use in UI like [AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) or any UI that's uses .ckpt files just download one or more file from here for your convenience. [katFl-wd-1.4-beta1.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1.ckpt) 5.16 GB [katFl-wd-1.4-beta1-pruned.ckpt](https://huggingface.co/no3/kat-wd-1.4-beta1/resolve/main/katFl-wd-1.4-beta1-pruned.ckpt) 2.58 GB Uses less storage space, but untested yet If you have issues or questions feel free to visit the Community Tab and start discussion about it. Here are images used for training this concept:      | 5fc97281c7e151dce8b2b1b88f6cc78b |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_16_02_2022-14_37_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 | 7d31e9850e81ec6e5274a5396b16e74c |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_rte_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 3.0847 - Accuracy: 0.4874 | 9b0879343b1aadd71f2520e5c0a6100c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2703 | 1.0 | 1136 | 3.2768 | 0.4657 | | 0.0555 | 2.0 | 2272 | 3.0847 | 0.4874 | | 0.0253 | 3.0 | 3408 | 5.4968 | 0.5018 | | 0.0149 | 4.0 | 4544 | 5.6020 | 0.4982 | | 0.0104 | 5.0 | 5680 | 6.6683 | 0.5090 | | 0.0082 | 6.0 | 6816 | 8.2220 | 0.5090 | | 0.0062 | 7.0 | 7952 | 8.2179 | 0.5054 | | 40353f9360f009a82458488c1c1c85a7 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-squadshifts-vanilla-amazon-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: amazon) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 467e9427bc302ed81d3c161eefc2a434 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (amazon) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 1a1a47fb3ccead1c96804dcda1903fef |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-squadshifts-vanilla-amazon-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 39a7de82eb8fb359c94e917520db21d1 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-amazon-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:---------------------------------------------------------------------------| | BERTScore | 92.3 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 28.19 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 18.89 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 12.92 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 9.1 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 23.04 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 62.81 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 27.85 | amazon | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | 0235c24aa2202f89b73ec833162e8c25 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: amazon - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 4 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-large-squadshifts-vanilla-amazon-qg/raw/main/trainer_config.json). | a9a6a520477753decc731f545bdceaab |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 256 - total_train_batch_size: 2048 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 2.0 - training precision: Mixed Precision | 5c33196b83d3427c843d59f34131176c |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/mayo-bert-uncased-wordlevel-block512-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3171 - Epoch: 9 | 13fd01f42551b6bea197a58d9043142d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 - mlm_probability: 0.15 - batch_size: 8 - epochs: 10 | 0cc737e18339582b3f772bfc5894b1d2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 3.0885 | 0 | | 2.8340 | 1 | | 2.7975 | 2 | | 2.6720 | 3 | | 2.4868 | 4 | | 2.1750 | 5 | | 1.8143 | 6 | | 1.0948 | 7 | | 0.4915 | 8 | | 0.3171 | 9 | | f9bd4cad995f6b6dd4294dbdb86005b6 |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | How to use the model
*NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
~~~~
import torch
from transformers import T5Tokenizer, AutoModelForCausalLM
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b")
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b")
if torch.cuda.is_available():
model = model.to("cuda")
text = "西田幾多郎は、"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
max_length=100,
min_length=100,
do_sample=True,
top_k=500,
top_p=0.95,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
bad_word_ids=[[tokenizer.unk_token_id]]
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)
| 4d2e091900107060cc98f7a0e0651cb6 |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | sample output: 西田幾多郎は、その主著の「善の研究」などで、人間の内面に自然とその根源があると指摘し、その根源的な性格は、この西田哲学を象徴しているとして、カントの「純粋理性批判」と「判断力批判」を対比して捉えます。それは、「人が理性的存在であるかぎりにおいて、人はその当人に固有な道徳的に自覚された善悪の基準を持っている」とするもので、この理性的な善悪の観念を否定するのがカントの
~~~~
| aa35cb3813de768c3fbc64b5f491b501 |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | Training
The model was trained on [Japanese C4](https://huggingface.co/datasets/allenai/c4), [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. It reaches around 14 perplexity on a chosen validation set from the same data.
| 6c2db2e55686e2a8ec34354d7f3b03ce |
mit | ['ja', 'japanese', 'gpt', 'text-generation', 'lm', 'nlp'] | false | Tokenization
The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script, and then augmented with emojis and symbols.
| abaefdb35f76504a7ae8ff31bfa84b82 |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4578 - Precision: 0.0060 - Recall: 0.0286 - F1: 0.0099 - Accuracy: 0.4288 | 1570b1ba7de0efe29448b1bcfe388984 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 8 | 1.6449 | 0.0 | 0.0 | 0.0 | 0.3860 | | No log | 2.0 | 16 | 1.5439 | 0.0014 | 0.0071 | 0.0023 | 0.4025 | | No log | 3.0 | 24 | 1.4986 | 0.0068 | 0.0286 | 0.0110 | 0.4176 | | No log | 4.0 | 32 | 1.4603 | 0.0033 | 0.0143 | 0.0054 | 0.4285 | | No log | 5.0 | 40 | 1.4578 | 0.0060 | 0.0286 | 0.0099 | 0.4288 | | afe772310ee9315d9c6e61bac8259fca |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-NL8 (Deep-Narrow version) T5-Efficient-TINY-NL8 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. | cd4b7200656779ed825eea22e7d835ff |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-nl8** - is of model type **Tiny** with the following variations: - **nl** is **8** It has **22.93** million parameters and thus requires *ca.* **91.74 MB** of memory in full precision (*fp32*) or **45.87 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 | | b0a3735b1e68812fa75434389545ca3b |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'Kinyarwanda', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="PaulChimzy/stt_rw_conformer_transducer_large" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | f06fa9b9412bbd1df37627417d31b633 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'Kinyarwanda', 'audio', 'CTC', 'Conformer', 'Transformer', 'NeMo', 'pytorch'] | false | Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. | 79fa14827ae5d7470ad70be27e7ab8a3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-breton Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | ffb5ddd2e9f2a7e9bbb043ee9bfd328f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "br", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") resampler = torchaudio.transforms.Resample(48_000, 16_000) | a33d85cb39cf5a4551ad426972a545d2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` | 143306aa05ec3f0492d319ec2e51d580 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Breton test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "br", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-breton") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | d1a35ba4466093d83dcf08a822ea5ec3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | b3cd3421d034f6616dcc6f1c0b87e6f7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 46.49 % | 25b089e7f3e1941b2811265e4f7de116 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **Object-Taped-To-Wall-Diffusion** This fine-tuned Stable Diffusion v1.5 model was trained for 2000 iterations with a batch size of 4, on a selection of photos of things taped to a wall. Training was performed using [ShivamShrirao/diffusers](https://github.com/ShivamShrirao/diffusers) with full precision, prior-preservation loss, the train-text-encoder feature, and the new [1.5 MSE VAE from Stability AI](https://huggingface.co/stabilityai/sd-vae-ft-mse). A total of 2100 regularization / class images were used from [here](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images). Regularization images were generated using the prompt "artwork style" with 50 DPM++ 2S a Karras steps and a CFG of 7, using the MSE VAE. A negative prompt of "text" was also used for this dataset. Use the tokens **ttw style** in your prompts for the effect. Note that the effect also appears to occur at a much weaker strength on prompts that steer the output towards specific artistic styles. This model will likely not perform well on taping objects that are not traditionally able to be taped to walls. <div align="center"> <img src="https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png"> </div> * [Full Image](https://huggingface.co/ProGamerGov/Object-Taped-To-Wall-Diffusion-V1/resolve/main/v1_size_512x512_t4x8.png) Example images were generated with the v1 2000 iteration model using DPM++ 2S a Karras: ``` ttw style, <object> taped to wall ``` This model was inspired by the 2019 art piece [*Comedian* by Italian artist Maurizio Cattelan](https://en.wikipedia.org/wiki/Comedian_(artwork\)), where a banana was duct taped to a wall. | 51e7792fd538476766a925d77c8c3aba |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-ar This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8342 - Wer: 82.3706 | 151939c55501c59223400a60bcf20e9c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP | ff5b107c5d8251303ad8771ae9eb9111 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6454 | 5.0 | 1000 | 1.8790 | 86.8695 | | 0.0408 | 10.0 | 2000 | 2.4389 | 80.5579 | | 0.0043 | 15.0 | 3000 | 2.7456 | 82.2767 | | 0.002 | 20.0 | 4000 | 2.8342 | 82.3706 | | 609344e9d2b0869b8f8ec21d7b98f1ad |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/gtr-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-base-1](https://tfhub.dev/google/gtr/gtr-base/1) to PyTorch. When using this model, have a look at the publication: [Large Dual Encoders Are Generalizable Retrievers](https://arxiv.org/abs/2112.07899). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-base model. The weights are stored in FP16.
| 1ef987f8dbd9ce2950f02e887f0d56b8 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/gtr-t5-base')
embeddings = model.encode(sentences)
print(embeddings)
```
The model requires sentence-transformers version 2.2.0 or newer.
| ca6618633304c4d0f49015f4028f1085 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-base)
| 0baa2dd50e9c653ffa32ea1871da77c2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-small-finer-longer This model is a fine-tuned version of [muhtasham/bert-small-finer](https://huggingface.co/muhtasham/bert-small-finer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4264 | 3b4a300506ade3f8e549bd89cea061ef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 20 | d8130228621c2c2d94b2161a47294a7d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 0.49 | 500 | 1.6683 | | 1.5941 | 0.97 | 1000 | 1.6569 | | 1.5941 | 1.46 | 1500 | 1.6436 | | 1.5605 | 1.94 | 2000 | 1.6173 | | 1.5605 | 2.43 | 2500 | 1.6073 | | 1.5297 | 2.91 | 3000 | 1.6001 | | 1.5297 | 3.4 | 3500 | 1.5815 | | 1.5022 | 3.89 | 4000 | 1.5756 | | 1.5022 | 4.37 | 4500 | 1.5568 | | 1.4753 | 4.86 | 5000 | 1.5458 | | 1.4753 | 5.34 | 5500 | 1.5399 | | 1.4537 | 5.83 | 6000 | 1.5273 | | 1.4537 | 6.32 | 6500 | 1.5192 | | 1.433 | 6.8 | 7000 | 1.5099 | | 1.433 | 7.29 | 7500 | 1.5083 | | 1.4169 | 7.77 | 8000 | 1.4957 | | 1.4169 | 8.26 | 8500 | 1.4914 | | 1.3982 | 8.75 | 9000 | 1.4859 | | 1.3982 | 9.23 | 9500 | 1.4697 | | 1.3877 | 9.72 | 10000 | 1.4711 | | 1.3877 | 10.2 | 10500 | 1.4608 | | 1.3729 | 10.69 | 11000 | 1.4583 | | 1.3729 | 11.18 | 11500 | 1.4513 | | 1.3627 | 11.66 | 12000 | 1.4498 | | 1.3627 | 12.15 | 12500 | 1.4396 | | 1.357 | 12.63 | 13000 | 1.4415 | | 1.357 | 13.12 | 13500 | 1.4347 | | 1.3484 | 13.61 | 14000 | 1.4316 | | 1.3484 | 14.09 | 14500 | 1.4319 | | 1.3442 | 14.58 | 15000 | 1.4268 | | 1.3442 | 15.06 | 15500 | 1.4293 | | 1.3387 | 15.55 | 16000 | 1.4217 | | 1.3387 | 16.03 | 16500 | 1.4241 | | 1.3358 | 16.52 | 17000 | 1.4250 | | 1.3358 | 17.01 | 17500 | 1.4196 | | 1.3344 | 17.49 | 18000 | 1.4193 | | 1.3344 | 17.98 | 18500 | 1.4200 | | 1.3274 | 18.46 | 19000 | 1.4250 | | 1.3274 | 18.95 | 19500 | 1.4168 | | 1.3348 | 19.44 | 20000 | 1.4164 | | 1.3348 | 19.92 | 20500 | 1.4264 | | f2d56c4b78e8e67059c228476984f91b |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k A timm specific MaxxViT-V2 (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k pretraining and ImageNet-1k fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances.. | 16e07ef387d050b086aed1d33cfc899f |
apache-2.0 | ['image-classification', 'timm'] | false | Model Variants in [maxxvit.py](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/maxxvit.py) MaxxViT covers a number of related model architectures that share a common structure including: - CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages. - MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid). - CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate. Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released. | 4ec58fa1e25b841f7be3679158af66d2 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 116.1 - GMACs: 24.2 - Activations (M): 62.8 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-12k | 82d40b2ca0a060a0c7f71c07d5a8e703 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True) model = model.eval() | b0d1b13f2ab5a6453f18d30083a09514 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() | 6dcd6cf7d8c203d70a9bfbe1e6b71db1 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k', pretrained=True, num_classes=0, | bbcab2004fabc13fb5f0057020be0d50 |
apache-2.0 | ['image-classification', 'timm'] | false | By Top-1 |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| | 24b26c10c551a267a7bbc60f4943ae18 |
apache-2.0 | ['image-classification', 'timm'] | false | By Throughput (samples / sec) |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| | f7350c185e37a9384f05d4ffd11f3d29 |
apache-2.0 | ['image-classification', 'timm'] | false | Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{tu2022maxvit, title={MaxViT: Multi-Axis Vision Transformer}, author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, journal={ECCV}, year={2022}, } ``` ```bibtex @article{dai2021coatnet, title={CoAtNet: Marrying Convolution and Attention for All Data Sizes}, author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing}, journal={arXiv preprint arXiv:2106.04803}, year={2021} } ``` | 1bae4a6e7491becb24b6fe58072355bd |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wrapped_poincare_vae") ``` | 3def247a415f137eb607eb5cbc3baa4f |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of the official implementation of [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | PoincareVAE | MNIST | NLL (500 IS) | 101.66 (0.00) | 101.47 (0.01) | [1] Mathieu, E., Le Lan, C., Maddison, C. J., Tomioka, R., & Teh, Y. W. (2019). Continuous hierarchical representations with poincaré variational auto-encoders. Advances in neural information processing systems, 32. | dbc2433a1c7bf33739074b7ff3e31a94 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the norweigen-fjords concept trained by StatsGary on the StatsGary/dreambooth-hackathon-images dataset. This is a Stable Diffusion model fine-tuned on the norweigen-fjords concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a viking on the fjords** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 73bcdc3913d1d9d3ac456a6b4d94d56d |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Lobster swimming in a Fjord The below example uses a prompt similar to *lobster swimming in a fjord* to generate the output:  | 506a2b94644318402faab3dce5216cf8 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Viking warrior in a Fjord This represents a generated Viking warrior on or near a Fjord. The prompt used to generate is **prompt**=*a viking warrior on a fjord*:  | da8b581a7ecce700774b65cf04baba33 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | A yellow submarine (inspired by The Beetles) Here, we see a yellow submarine inspired by the popular Beetles album. The prompt used to generate is **prompt**=a beetles like yellow submarines on a fjord*:  | 80f69ec60764403b5965449f9d18b24c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | A cruise ship on a fjord This is based on the **prompt**=*a cruise ship on a fjord*:  | 98add435e7dde382ddc1bd85fac7c390 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Taj Mahal on a Fjord This generates landmarks near or on the fjord:  | 8b0113ff090a95d23b880e77c6d9398a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Watersports on a Fjord This is an example of a kayaker on a fjord - generated using *prompt*="a kayaker on a fjord":  What about a surfer on a fjord:  | 9a37a540bd09363a1727734612740279 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Godzilla wading through a Fjord This one is a generated image of Godzilla wading through a Fjord:  | 54a4947d7473fd2f91ef93b718681987 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | How about T-Rex On the theme of Godzilla, what about T-Rex:  | 33f48d2b1b37ca0d211bbac8136e1494 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Paintings on a Fjord We could explore what a **Da Vinci** type painting would look like on a Fjord:  | 1b2f35907457ae9fe1e1f7404e544840 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Generating your own predictions The following Python code will allow you to get up and running quickly, just replace the *prompt* field for your own generation, wait for HuggingFace to compute and you should have your own Stable Diffusion object generated against a backdrop of the fjords. Idyllic! ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('StatsGary/norweigen-fjords-fjords') image = pipeline(prompt='a viking on a fjord').images[0] image ``` | f714aa7f902cf761509d25419a5324c3 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Supporting article(s) I have undertaken a blog to explain this: - Fjord stable diffusion model: https://hutsons-hacks.info/stable-diffusion-model-for-generating-images-of-fjords - Stable diffusion application with Streamlit: https://hutsons-hacks.info/stable-diffusion-application-with-streamlit | 422c27650e8fa3539e37328257ab4b4f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2133 - Accuracy: 0.9265 - F1: 0.9265 | 0bf12517150c5d1f0dba9117933f1e5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8401 | 1.0 | 250 | 0.3144 | 0.9085 | 0.9058 | | 0.2524 | 2.0 | 500 | 0.2133 | 0.9265 | 0.9265 | | e4dd44016b3cf3fbda081d4ac96f00ff |
mit | ['generated_from_trainer'] | false | bertdbmdzIhate This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6880 - Accuracy: 0.726 - F1: 0.4170 | a65aff55e04485c8627f185645d798b7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab_2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3801 - Wer: 0.3035 | e1e2793af861e000cf5a623d5e3447b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.7227 | 3.52 | 500 | 2.6961 | 1.0 | | 1.1237 | 7.04 | 1000 | 0.6088 | 0.5315 | | 0.4886 | 10.56 | 1500 | 0.4709 | 0.4353 | | 0.3148 | 14.08 | 2000 | 0.4341 | 0.3942 | | 0.2229 | 17.61 | 2500 | 0.4035 | 0.3616 | | 0.1693 | 21.13 | 3000 | 0.3868 | 0.3289 | | 0.1393 | 24.65 | 3500 | 0.3993 | 0.3135 | | 0.118 | 28.17 | 4000 | 0.3801 | 0.3035 | | 800fb9da01d570e2f06db69c60bd67ad |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-scratch This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 6.6235 | 6ffb26161b2b2ce186150249b4efd074 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.388 | 1.0 | 157 | 7.3651 | | 6.9902 | 2.0 | 314 | 6.7300 | | 6.659 | 3.0 | 471 | 6.6304 | | c309b1e8853ed40d3b4f4dd4117ea531 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3623 - Accuracy: 0.903 - F1: 0.9003 | 3e15d5160a9436a5df32ed73c8de76d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | a6579000e307eef4b701503c8b8707d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5960 | 0.8025 | 0.7750 | | 0.7853 | 2.0 | 250 | 0.3623 | 0.903 | 0.9003 | | 63c520407a1cef1948ab55907dccfc84 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-base-224_finetuned_on_ImageIn_annotations This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0749 - Precision: 0.9722 - Recall: 0.9811 - F1: 0.9765 - Accuracy: 0.9824 | 97e11c7d07739b01290608e700f76cfb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP | 7ef5ce248809cf8ad3b35a3d3adca18a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 83 | 0.1368 | 0.9748 | 0.9632 | 0.9688 | 0.9772 | | No log | 2.0 | 166 | 0.0734 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | No log | 3.0 | 249 | 0.0693 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | No log | 4.0 | 332 | 0.0698 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | No log | 5.0 | 415 | 0.0688 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | No log | 6.0 | 498 | 0.0690 | 0.9729 | 0.9751 | 0.9740 | 0.9807 | | 0.0947 | 7.0 | 581 | 0.0666 | 0.9689 | 0.9800 | 0.9743 | 0.9807 | | 0.0947 | 8.0 | 664 | 0.0642 | 0.9689 | 0.9800 | 0.9743 | 0.9807 | | 0.0947 | 9.0 | 747 | 0.0790 | 0.9763 | 0.9763 | 0.9763 | 0.9824 | | 0.0947 | 10.0 | 830 | 0.0813 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | 0.0947 | 11.0 | 913 | 0.0797 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | 0.0947 | 12.0 | 996 | 0.0791 | 0.9763 | 0.9763 | 0.9763 | 0.9824 | | 0.0205 | 13.0 | 1079 | 0.0871 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | 0.0205 | 14.0 | 1162 | 0.0716 | 0.9722 | 0.9811 | 0.9765 | 0.9824 | | 0.0205 | 15.0 | 1245 | 0.0746 | 0.9776 | 0.9799 | 0.9787 | 0.9842 | | 0.0205 | 16.0 | 1328 | 0.0917 | 0.9738 | 0.9692 | 0.9714 | 0.9789 | | 0.0205 | 17.0 | 1411 | 0.0694 | 0.9776 | 0.9799 | 0.9787 | 0.9842 | | 0.0205 | 18.0 | 1494 | 0.0697 | 0.9768 | 0.9859 | 0.9812 | 0.9859 | | 0.0166 | 19.0 | 1577 | 0.0689 | 0.9702 | 0.9835 | 0.9766 | 0.9824 | | 0.0166 | 20.0 | 1660 | 0.0995 | 0.9738 | 0.9692 | 0.9714 | 0.9789 | | 0.0166 | 21.0 | 1743 | 0.0847 | 0.9776 | 0.9799 | 0.9787 | 0.9842 | | 0.0166 | 22.0 | 1826 | 0.0843 | 0.9776 | 0.9799 | 0.9787 | 0.9842 | | 0.0166 | 23.0 | 1909 | 0.0869 | 0.9750 | 0.9727 | 0.9739 | 0.9807 | | 0.0166 | 24.0 | 1992 | 0.0762 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0125 | 25.0 | 2075 | 0.0778 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0125 | 26.0 | 2158 | 0.0834 | 0.9763 | 0.9763 | 0.9763 | 0.9824 | | 0.0125 | 27.0 | 2241 | 0.0818 | 0.9776 | 0.9799 | 0.9787 | 0.9842 | | 0.0125 | 28.0 | 2324 | 0.0756 | 0.9684 | 0.9859 | 0.9768 | 0.9824 | | 0.0125 | 29.0 | 2407 | 0.1150 | 0.9591 | 0.9824 | 0.9700 | 0.9772 | | 0.0125 | 30.0 | 2490 | 0.0781 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0111 | 31.0 | 2573 | 0.0793 | 0.9716 | 0.9871 | 0.9790 | 0.9842 | | 0.0111 | 32.0 | 2656 | 0.0713 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0111 | 33.0 | 2739 | 0.0802 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0111 | 34.0 | 2822 | 0.0636 | 0.9802 | 0.9870 | 0.9835 | 0.9877 | | 0.0111 | 35.0 | 2905 | 0.0702 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0111 | 36.0 | 2988 | 0.0773 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0145 | 37.0 | 3071 | 0.0663 | 0.9781 | 0.9894 | 0.9836 | 0.9877 | | 0.0145 | 38.0 | 3154 | 0.0721 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0145 | 39.0 | 3237 | 0.0708 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0145 | 40.0 | 3320 | 0.0729 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0145 | 41.0 | 3403 | 0.0760 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0145 | 42.0 | 3486 | 0.0771 | 0.9716 | 0.9871 | 0.9790 | 0.9842 | | 0.0106 | 43.0 | 3569 | 0.0713 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0106 | 44.0 | 3652 | 0.0721 | 0.9748 | 0.9883 | 0.9813 | 0.9859 | | 0.0106 | 45.0 | 3735 | 0.0732 | 0.9768 | 0.9859 | 0.9812 | 0.9859 | | 0.0106 | 46.0 | 3818 | 0.0783 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0106 | 47.0 | 3901 | 0.0770 | 0.9789 | 0.9835 | 0.9811 | 0.9859 | | 0.0106 | 48.0 | 3984 | 0.0744 | 0.9735 | 0.9847 | 0.9789 | 0.9842 | | 0.0082 | 49.0 | 4067 | 0.0752 | 0.9722 | 0.9811 | 0.9765 | 0.9824 | | 0.0082 | 50.0 | 4150 | 0.0749 | 0.9722 | 0.9811 | 0.9765 | 0.9824 | | db4017cdb27b4bce58492a43d97da171 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.6744 - Rouge1: 13.2843 - Rouge2: 2.006 - Rougel: 10.6541 - Rougelsum: 12.0343 - Gen Len: 18.9984 | f09cb3c682761f7e45b1cf208b240c10 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.8822 | 1.0 | 17040 | 3.6744 | 13.2843 | 2.006 | 10.6541 | 12.0343 | 18.9984 | | 210ae00df6bd6f12de8b6378a51b0a37 |
apache-2.0 | ['generated_from_trainer'] | false |  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the SGH news articles and summaries dataset. It achieves the following results on the evaluation set: - Loss: 1.9680 - Rouge1 Precision: 0.4404 - Rouge1 Recall: 0.5874 - Rouge1 Fmeasure: 0.4653 - Rouge2 Precision: 0.2673 - Rouge2 Recall: 0.3871 - Rouge2 Fmeasure: 0.2897 - Rougel Precision: 0.3059 - Rougel Recall: 0.4418 - Rougel Fmeasure: 0.3308 - Rougelsum Precision: 0.3059 - Rougelsum Recall: 0.4418 - Rougelsum Fmeasure: 0.3308 | 151f6ccc4a73904776484e42513e4404 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:| | 1.4834 | 0.43 | 10 | 1.7001 | 0.2304 | 0.6761 | 0.3152 | 0.1326 | 0.4034 | 0.1797 | 0.1495 | 0.4624 | 0.2069 | 0.1495 | 0.4624 | 0.2069 | | 1.5011 | 0.87 | 20 | 1.6051 | 0.4301 | 0.5372 | 0.4087 | 0.2481 | 0.3439 | 0.245 | 0.2878 | 0.3928 | 0.2834 | 0.2878 | 0.3928 | 0.2834 | | 0.9289 | 1.3 | 30 | 1.5501 | 0.431 | 0.597 | 0.4364 | 0.2653 | 0.393 | 0.2736 | 0.3007 | 0.4233 | 0.3037 | 0.3007 | 0.4233 | 0.3037 | | 1.0895 | 1.74 | 40 | 1.5969 | 0.4661 | 0.5481 | 0.4486 | 0.2736 | 0.3439 | 0.2689 | 0.3318 | 0.4045 | 0.3221 | 0.3318 | 0.4045 | 0.3221 | | 0.7785 | 2.17 | 50 | 1.5875 | 0.4527 | 0.5405 | 0.4209 | 0.2942 | 0.3634 | 0.272 | 0.3268 | 0.4047 | 0.3042 | 0.3268 | 0.4047 | 0.3042 | | 0.635 | 2.61 | 60 | 1.6081 | 0.4142 | 0.5649 | 0.4172 | 0.242 | 0.3659 | 0.2549 | 0.2787 | 0.4156 | 0.2909 | 0.2787 | 0.4156 | 0.2909 | | 0.514 | 3.04 | 70 | 1.6150 | 0.4431 | 0.5665 | 0.4569 | 0.2656 | 0.3754 | 0.2853 | 0.3252 | 0.441 | 0.3434 | 0.3252 | 0.441 | 0.3434 | | 0.5617 | 3.48 | 80 | 1.6447 | 0.3956 | 0.6304 | 0.451 | 0.2353 | 0.425 | 0.2776 | 0.2883 | 0.4904 | 0.3332 | 0.2883 | 0.4904 | 0.3332 | | 0.396 | 3.91 | 90 | 1.7423 | 0.4276 | 0.609 | 0.4506 | 0.2657 | 0.4142 | 0.2858 | 0.3091 | 0.4677 | 0.3316 | 0.3091 | 0.4677 | 0.3316 | | 0.3427 | 4.35 | 100 | 1.7572 | 0.3877 | 0.5633 | 0.4169 | 0.216 | 0.3635 | 0.2468 | 0.2706 | 0.4314 | 0.3018 | 0.2706 | 0.4314 | 0.3018 | | 0.3059 | 4.78 | 110 | 1.7705 | 0.4255 | 0.5524 | 0.4429 | 0.2495 | 0.3488 | 0.2671 | 0.3184 | 0.4275 | 0.3358 | 0.3184 | 0.4275 | 0.3358 | | 0.2083 | 5.22 | 120 | 1.7840 | 0.4533 | 0.5896 | 0.4655 | 0.284 | 0.4142 | 0.308 | 0.3164 | 0.4442 | 0.3376 | 0.3164 | 0.4442 | 0.3376 | | 0.2591 | 5.65 | 130 | 1.8396 | 0.4391 | 0.5315 | 0.4209 | 0.2768 | 0.3661 | 0.2707 | 0.3194 | 0.4124 | 0.3111 | 0.3194 | 0.4124 | 0.3111 | | 0.2609 | 6.09 | 140 | 1.8220 | 0.4425 | 0.5712 | 0.4465 | 0.2642 | 0.3738 | 0.2727 | 0.3093 | 0.4349 | 0.3208 | 0.3093 | 0.4349 | 0.3208 | | 0.1696 | 6.52 | 150 | 1.8916 | 0.475 | 0.5557 | 0.4686 | 0.2959 | 0.3783 | 0.3019 | 0.3409 | 0.4268 | 0.3442 | 0.3409 | 0.4268 | 0.3442 | | 0.2683 | 6.96 | 160 | 1.8957 | 0.445 | 0.5918 | 0.4748 | 0.285 | 0.4021 | 0.3075 | 0.3249 | 0.4551 | 0.3522 | 0.3249 | 0.4551 | 0.3522 | | 0.1259 | 7.39 | 170 | 1.9371 | 0.4473 | 0.5368 | 0.4664 | 0.2608 | 0.3355 | 0.282 | 0.3276 | 0.4071 | 0.3492 | 0.3276 | 0.4071 | 0.3492 | | 0.1919 | 7.83 | 180 | 1.9521 | 0.4026 | 0.5528 | 0.438 | 0.2362 | 0.3427 | 0.2604 | 0.2751 | 0.3957 | 0.3042 | 0.2751 | 0.3957 | 0.3042 | | 0.1279 | 8.26 | 190 | 1.9398 | 0.413 | 0.6053 | 0.4575 | 0.2511 | 0.403 | 0.2881 | 0.2662 | 0.4195 | 0.3027 | 0.2662 | 0.4195 | 0.3027 | | 0.1176 | 8.7 | 200 | 1.9556 | 0.4363 | 0.565 | 0.4492 | 0.2591 | 0.3727 | 0.2806 | 0.3107 | 0.428 | 0.3289 | 0.3107 | 0.428 | 0.3289 | | 0.1299 | 9.13 | 210 | 1.9642 | 0.4385 | 0.5728 | 0.4587 | 0.2687 | 0.3744 | 0.2888 | 0.3212 | 0.436 | 0.3404 | 0.3212 | 0.436 | 0.3404 | | 0.1303 | 9.57 | 220 | 1.9649 | 0.43 | 0.5648 | 0.439 | 0.2605 | 0.3624 | 0.2691 | 0.2958 | 0.4135 | 0.3067 | 0.2958 | 0.4135 | 0.3067 | | 0.1129 | 10.0 | 230 | 1.9680 | 0.4404 | 0.5874 | 0.4653 | 0.2673 | 0.3871 | 0.2897 | 0.3059 | 0.4418 | 0.3308 | 0.3059 | 0.4418 | 0.3308 | | e51201e1800b4a507006a962a8cc2dde |
mit | ['generated_from_trainer'] | false | deberta-base-finetuned-squad1 This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.8037 | d4d7bb60704b3bbc524e57df081c4254 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.7928 | 1.0 | 7380 | 0.7810 | | 0.5795 | 2.0 | 14760 | 0.8037 | | fa1b3e75f136a2a633d0be8d5fd3479b |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-es This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8937 - Rouge1: 32.6939 - Rouge2: 11.794 - Rougel: 31.9982 - Rougelsum: 31.9902 - Gen Len: 15.7947 | 00cf71e599f21f2c6f9e6c195ae031e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.251 | 1.0 | 7061 | 1.8937 | 32.6939 | 11.794 | 31.9982 | 31.9902 | 15.7947 | | 9e333da2a6a47f43e705d23f87dcc3ed |
mit | [] | false | Introduction XDoc is a unified pre-trained model that deals with different document formats in a single model. With only 36.7% parameters, XDoc achieves comparable or better performance on downstream tasks, which is cost-effective for real-world deployment. [XDoc: Unified Pre-training for Cross-Format Document Understanding](https://arxiv.org/abs/2210.02849) Jingye Chen, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei, [EMNLP 2022]( | 88d53faedacbce1939a4e434ff2a89cb |
mit | [] | false | Citation If you find XDoc helpful, please cite us: ``` @article{chen2022xdoc, title={XDoc: Unified Pre-training for Cross-Format Document Understanding}, author={Chen, Jingye and Lv, Tengchao and Cui, Lei and Zhang, Cha and Wei, Furu}, journal={arXiv preprint arXiv:2210.02849}, year={2022} } ``` | 278376f25378f09a250a5e31d7e91ae1 |
apache-2.0 | ['automatic-speech-recognition', 'fa'] | false | exp_w2v2t_fa_vp-sv_s689 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fa)](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. | 262c2850b42a8c3e0efe66c5ba59d3a4 |
mit | [] | false | Collage3 on Stable Diffusion This is the `<Collage3>` 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`:                         | 63b3bdd1cb7229281c6a779e0297d527 |
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