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himanshue2e/Whisper-Hindi | himanshue2e | 2024-02-15T05:46:50Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-15T05:46:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
rohdimp24/LORA-bloom-3b | rohdimp24 | 2024-02-15T05:38:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-15T05:38:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
elite-testo-max/elite-testo-max | elite-testo-max | 2024-02-15T05:14:44Z | 0 | 0 | spacy | [
"spacy",
"Elite Testo Max",
"en",
"license:cc-by-nc-sa-3.0",
"region:us"
]
| null | 2024-02-15T05:13:39Z | ---
license: cc-by-nc-sa-3.0
language:
- en
library_name: spacy
tags:
- Elite Testo Max
---
[Elite Testo Max](https://atozsupplement.com/elite-testo-max/) It's vital to take note of that not all upgrade techniques are therapeutically or experimentally demonstrated, and numerous items advertised for these reasons might need guideline or logical proof supporting their adequacy and wellbeing. Prior to considering any type of male upgrade, it's significant to talk with a medical care proficient to grasp expected dangers, viability, and legitimate use. Furthermore, solid way of life decisions like standard activity, a decent eating regimen, overseeing pressure, and sufficient rest can emphatically influence sexual wellbeing and execution.
VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/elite-testo-max/
|
jungyuko/DAVinCI-42dot_LLM-PLM-1.3B-v1.1 | jungyuko | 2024-02-15T05:12:18Z | 2,340 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T01:58:16Z | ---
license: cc-by-nc-4.0
---
## DAVinCI-42dot_LLM-PLM-1.3B-v1.1
This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) on a custom dataset.
### Model description
More information needed
### Intended uses & limitations
More information needed
### Training and evaluation data
More information needed
### Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
* learning_rate: 2e-05
* train_batch_size: 24
* eval_batch_size: 8
* seed: 42
* gradient_accumulation_steps: 4
* total_train_batch_size: 96
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr_scheduler_type: linear
* num_epochs: 1.0
* mixed_precision_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
|
kenchenxingyu/flan-large-single-label-stance-human6 | kenchenxingyu | 2024-02-15T05:11:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-15T05:11:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
p1atdev/siglip-tagger-test-3 | p1atdev | 2024-02-15T05:00:54Z | 35 | 10 | transformers | [
"transformers",
"safetensors",
"siglip_vision_model",
"image-classification",
"generated_from_trainer",
"siglip",
"custom_code",
"base_model:google/siglip-so400m-patch14-384",
"base_model:finetune:google/siglip-so400m-patch14-384",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2024-02-03T11:37:34Z | ---
license: apache-2.0
base_model: google/siglip-so400m-patch14-384
tags:
- generated_from_trainer
- siglip
metrics:
- accuracy
- f1
model-index:
- name: siglip-tagger-test-3
results: []
---
# siglip-tagger-test-3
This model is a fine-tuned version of [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 692.4745
- Accuracy: 0.3465
- F1: 0.9969
## Model description
This model is an experimental model that predicts danbooru tags of images.
## Example
### Use a pipeline
```py
from transformers import pipeline
pipe = pipeline("image-classification", model="p1atdev/siglip-tagger-test-3", trust_remote_code=True)
pipe(
"image.jpg", # takes str(path) or numpy array or PIL images as input
threshold=0.5, #optional parameter defaults to 0
return_scores = False #optional parameter defaults to False
)
```
* `threshold`: confidence intervale, if it's specified, the pipeline will only return tags with a confidence >= threshold
* `return_scores`: if specified the pipeline will return the labels and their confidences in a dictionary format.
### Load model directly
```py
from PIL import Image
import torch
from transformers import (
AutoModelForImageClassification,
AutoImageProcessor,
)
import numpy as np
MODEL_NAME = "p1atdev/siglip-tagger-test-3"
model = AutoModelForImageClassification.from_pretrained(
MODEL_NAME, torch_dtype=torch.bfloat16, trust_remote_code=True
)
model.eval()
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
image = Image.open("sample.jpg") # load your image
inputs = processor(image, return_tensors="pt").to(model.device, model.dtype)
logits = model(**inputs).logits.detach().cpu().float()[0]
logits = np.clip(logits, 0.0, 1.0)
results = {
model.config.id2label[i]: logit for i, logit in enumerate(logits) if logit > 0
}
results = sorted(results.items(), key=lambda x: x[1], reverse=True)
for tag, score in results:
print(f"{tag}: {score*100:.2f}%")
```
## Intended uses & limitations
This model is for research use only and is not recommended for production.
Please use wd-v1-4-tagger series by SmilingWolf:
- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2)
etc.
## Training and evaluation data
High quality 5000 images from danbooru. They were shuffled and split into train:eval at 4500:500. (Same as p1atdev/siglip-tagger-test-2)
|Name|Description|
|-|-|
|Images count|5000|
|Supported tags|9517 general tags. Character and rating tags are not included. See all labels in [config.json](config.json)|
|Image rating|4000 for `general` and 1000 for `sensitive,questionable,explicit`|
|Copyright tags|`original` only|
|Image score range (on search)|min:10, max150|
## Training procedure
- Loss function: AsymmetricLossOptimized ([Asymmetric Loss](https://github.com/Alibaba-MIIL/ASL))
- `gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False`
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1066.981 | 1.0 | 71 | 1873.5417 | 0.1412 | 0.9939 |
| 547.3158 | 2.0 | 142 | 934.3269 | 0.1904 | 0.9964 |
| 534.6942 | 3.0 | 213 | 814.0771 | 0.2170 | 0.9966 |
| 414.1278 | 4.0 | 284 | 774.0230 | 0.2398 | 0.9967 |
| 365.4994 | 5.0 | 355 | 751.2046 | 0.2459 | 0.9967 |
| 352.3663 | 6.0 | 426 | 735.6580 | 0.2610 | 0.9967 |
| 414.3976 | 7.0 | 497 | 723.2065 | 0.2684 | 0.9968 |
| 350.8201 | 8.0 | 568 | 714.0453 | 0.2788 | 0.9968 |
| 364.5016 | 9.0 | 639 | 706.5261 | 0.2890 | 0.9968 |
| 309.1184 | 10.0 | 710 | 700.7808 | 0.2933 | 0.9968 |
| 288.5186 | 11.0 | 781 | 695.7027 | 0.3008 | 0.9968 |
| 287.4452 | 12.0 | 852 | 691.5306 | 0.3037 | 0.9968 |
| 280.9088 | 13.0 | 923 | 688.8063 | 0.3084 | 0.9969 |
| 296.8389 | 14.0 | 994 | 686.1077 | 0.3132 | 0.9968 |
| 265.1467 | 15.0 | 1065 | 683.7382 | 0.3167 | 0.9969 |
| 268.5263 | 16.0 | 1136 | 682.1683 | 0.3206 | 0.9969 |
| 309.7871 | 17.0 | 1207 | 681.1995 | 0.3199 | 0.9969 |
| 307.6475 | 18.0 | 1278 | 680.1700 | 0.3230 | 0.9969 |
| 262.0677 | 19.0 | 1349 | 679.2177 | 0.3270 | 0.9969 |
| 275.3823 | 20.0 | 1420 | 678.9730 | 0.3294 | 0.9969 |
| 273.984 | 21.0 | 1491 | 678.6031 | 0.3318 | 0.9969 |
| 273.5361 | 22.0 | 1562 | 678.1285 | 0.3332 | 0.9969 |
| 279.6474 | 23.0 | 1633 | 678.4264 | 0.3348 | 0.9969 |
| 232.5045 | 24.0 | 1704 | 678.3773 | 0.3357 | 0.9969 |
| 269.621 | 25.0 | 1775 | 678.4922 | 0.3372 | 0.9969 |
| 289.8389 | 26.0 | 1846 | 679.0094 | 0.3397 | 0.9969 |
| 256.7373 | 27.0 | 1917 | 679.5618 | 0.3407 | 0.9969 |
| 262.3969 | 28.0 | 1988 | 680.1168 | 0.3414 | 0.9969 |
| 266.2439 | 29.0 | 2059 | 681.0101 | 0.3421 | 0.9969 |
| 247.7932 | 30.0 | 2130 | 681.9800 | 0.3422 | 0.9969 |
| 246.8083 | 31.0 | 2201 | 682.8550 | 0.3416 | 0.9969 |
| 270.827 | 32.0 | 2272 | 683.9250 | 0.3434 | 0.9969 |
| 256.4384 | 33.0 | 2343 | 685.0451 | 0.3448 | 0.9969 |
| 270.461 | 34.0 | 2414 | 686.2427 | 0.3439 | 0.9969 |
| 253.8104 | 35.0 | 2485 | 687.4274 | 0.3441 | 0.9969 |
| 265.532 | 36.0 | 2556 | 688.4856 | 0.3451 | 0.9969 |
| 249.1426 | 37.0 | 2627 | 689.5027 | 0.3457 | 0.9969 |
| 229.5651 | 38.0 | 2698 | 690.4455 | 0.3455 | 0.9969 |
| 251.9008 | 39.0 | 2769 | 691.2324 | 0.3463 | 0.9969 |
| 281.8228 | 40.0 | 2840 | 691.7993 | 0.3464 | 0.9969 |
| 242.5272 | 41.0 | 2911 | 692.1788 | 0.3465 | 0.9969 |
| 229.5605 | 42.0 | 2982 | 692.3799 | 0.3465 | 0.9969 |
| 245.0876 | 43.0 | 3053 | 692.4745 | 0.3465 | 0.9969 |
| 271.22 | 44.0 | 3124 | 692.5084 | 0.3465 | 0.9969 |
| 244.3045 | 45.0 | 3195 | 692.5108 | 0.3465 | 0.9969 |
| 243.9542 | 46.0 | 3266 | 692.5128 | 0.3465 | 0.9969 |
| 274.6664 | 47.0 | 3337 | 692.5095 | 0.3465 | 0.9969 |
| 231.1361 | 48.0 | 3408 | 692.5107 | 0.3465 | 0.9969 |
| 274.5513 | 49.0 | 3479 | 692.5108 | 0.3465 | 0.9969 |
| 316.0833 | 50.0 | 3550 | 692.5107 | 0.3465 | 0.9969 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0 |
Lounarisnia/ppo-LunarLander-v2-3e6 | Lounarisnia | 2024-02-15T04:49:55Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-15T04:49:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 283.31 +/- 20.51
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
joseagmz/Mistral_FFT | joseagmz | 2024-02-15T04:42:20Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T04:29:03Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_FFT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./Mistral_FFT
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# Mistral_FFT
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2369
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 3
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9016 | 0.03 | 1 | 1.1080 |
| 0.8288 | 0.25 | 8 | 0.8722 |
| 1.0797 | 0.5 | 16 | 0.9858 |
| 1.036 | 0.75 | 24 | 1.1281 |
| 1.4318 | 1.0 | 32 | 1.2369 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0
|
RMWeerasinghe/t5-small-govReport-boardpapers-3072 | RMWeerasinghe | 2024-02-15T04:27:38Z | 97 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"Summarization",
"generated_from_trainer",
"summarization",
"base_model:RMWeerasinghe/t5-small-finetuned-govReport-3072",
"base_model:finetune:RMWeerasinghe/t5-small-finetuned-govReport-3072",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| summarization | 2024-02-15T04:15:21Z | ---
license: apache-2.0
base_model: RMWeerasinghe/t5-small-finetuned-govReport-3072
tags:
- Summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-govReport-boardpapers-3072
results: []
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-govReport-boardpapers-3072
This model is a fine-tuned version of [RMWeerasinghe/t5-small-finetuned-govReport-3072](https://huggingface.co/RMWeerasinghe/t5-small-finetuned-govReport-3072) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6701
- Rouge1: 0.0443
- Rouge2: 0.0194
- Rougel: 0.0382
- Rougelsum: 0.0443
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 0.67 | 1 | 3.9496 | 0.0584 | 0.0214 | 0.0482 | 0.0572 |
| No log | 2.0 | 3 | 3.9252 | 0.0562 | 0.0223 | 0.0463 | 0.0562 |
| No log | 2.67 | 4 | 3.9121 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 4.0 | 6 | 3.8880 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 4.67 | 7 | 3.8755 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 6.0 | 9 | 3.8506 | 0.0597 | 0.0223 | 0.0485 | 0.0596 |
| No log | 6.67 | 10 | 3.8395 | 0.0553 | 0.0197 | 0.0441 | 0.0541 |
| No log | 8.0 | 12 | 3.8172 | 0.0582 | 0.0262 | 0.049 | 0.057 |
| No log | 8.67 | 13 | 3.8065 | 0.0582 | 0.0262 | 0.049 | 0.057 |
| No log | 10.0 | 15 | 3.7862 | 0.0582 | 0.0257 | 0.049 | 0.057 |
| No log | 10.67 | 16 | 3.7769 | 0.057 | 0.0262 | 0.049 | 0.0556 |
| No log | 12.0 | 18 | 3.7599 | 0.0577 | 0.0294 | 0.0495 | 0.0575 |
| No log | 12.67 | 19 | 3.7522 | 0.0487 | 0.0174 | 0.042 | 0.0474 |
| 4.3528 | 14.0 | 21 | 3.7378 | 0.048 | 0.0155 | 0.0406 | 0.0461 |
| 4.3528 | 14.67 | 22 | 3.7310 | 0.0536 | 0.0206 | 0.0421 | 0.0511 |
| 4.3528 | 16.0 | 24 | 3.7187 | 0.048 | 0.017 | 0.0394 | 0.0448 |
| 4.3528 | 16.67 | 25 | 3.7132 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 18.0 | 27 | 3.7031 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 18.67 | 28 | 3.6985 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 20.0 | 30 | 3.6905 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 20.67 | 31 | 3.6869 | 0.043 | 0.017 | 0.0374 | 0.041 |
| 4.3528 | 22.0 | 33 | 3.6807 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 22.67 | 34 | 3.6781 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 24.0 | 36 | 3.6740 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 24.67 | 37 | 3.6725 | 0.0442 | 0.0194 | 0.0381 | 0.0423 |
| 4.3528 | 26.0 | 39 | 3.6705 | 0.0443 | 0.0194 | 0.0382 | 0.0443 |
| 4.0602 | 26.67 | 40 | 3.6701 | 0.0443 | 0.0194 | 0.0382 | 0.0443 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1 |
andruhon/unspsc_family_5examples_test2 | andruhon | 2024-02-15T04:23:40Z | 108 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-11T23:28:24Z | ---
language:
- en
license: apache-2.0
widget:
- text: "7oz hammer"
- text: "Cat6e network cable"
- text: "Printer HL-1210W"
---
# Classify text by UNSPSC family
Forked from https://huggingface.co/govspend/unspsc_family_5examples_test2
See https://en.wikipedia.org/wiki/UNSPSC
## Usage
```python
pipe = pipeline("text-classification", model="andruhon/unspsc_family_5examples_test2", tokenizer="bert-base-uncased")
pipe("7oz hammer");
# Would return something like {'label': 'LABEL_105', 'score': 0.339}
# In this case LABEL_105 clearly goes into 27110000 Handtools
```
## License
The original model didn't have license file.
Considering that it's BERT it should have the same license, which I think is Apache 2.0.
Use on your own risk. I'll update this file once I have more info.
|
Jlonge4/distilbert-yelp-review-multiclass | Jlonge4 | 2024-02-15T04:23:29Z | 3 | 0 | peft | [
"peft",
"safetensors",
"en",
"dataset:yelp_review_full",
"arxiv:1910.09700",
"base_model:distilbert/distilbert-base-uncased",
"base_model:adapter:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
]
| null | 2024-02-11T20:45:26Z | ---
library_name: peft
base_model: distilbert-base-uncased
license: apache-2.0
datasets:
- yelp_review_full
language:
- en
metrics:
- accuracy
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Jlonge4]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
jpsteinhafel/complaints_classifier | jpsteinhafel | 2024-02-15T04:22:37Z | 91 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-07T23:28:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: complaints_classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# complaints_classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2139
- Accuracy: 0.9420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 77 | 0.3395 | 0.9130 |
| No log | 2.0 | 154 | 0.2139 | 0.9420 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
|
theidoldaily/nozomi-tojo | theidoldaily | 2024-02-15T04:09:26Z | 5 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
]
| text-to-image | 2024-02-15T04:07:06Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
masterpiece, high quality, defined pupil, looking at viewer, rounded pupil,
defined iris, (soft iris:1.2),
parameters:
negative_prompt: >-
bad_anatomy, deformation, amputation, deformity, deformed_nipples,
duplicated_torso, deformed_torso, long_torso, large_torso,
unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2),
unproportioned_eyes, unproportioned_head, small_head, duplicated_nose,
big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy,
red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,
output:
url: images/nozomi_final.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: id_nozomi_tojo
license: mit
---
# Nozomi Tojo
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_nozomi_tojo` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/nozomi-tojo/tree/main) them in the Files & versions tab.
|
VladS159/whisper_small_ro_VladS_02_14_24_4000_steps | VladS159 | 2024-02-15T04:09:24Z | 61 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ro",
"dataset:mozilla-foundation/common_voice_16_1",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-02-14T16:46:03Z | ---
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Ro - Sarbu Vlad
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
args: 'config: ro, split: test'
metrics:
- name: Wer
type: wer
value: 18.664730616813383
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ro - Sarbu Vlad
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2920
- Wer: 18.6647
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 96
- total_eval_batch_size: 48
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1437 | 3.91 | 500 | 0.2167 | 20.5100 |
| 0.0268 | 7.81 | 1000 | 0.2202 | 18.6557 |
| 0.008 | 11.72 | 1500 | 0.2478 | 18.6829 |
| 0.0037 | 15.62 | 2000 | 0.2644 | 18.6708 |
| 0.0024 | 19.53 | 2500 | 0.2761 | 18.6405 |
| 0.0018 | 23.44 | 3000 | 0.2844 | 18.6859 |
| 0.0016 | 27.34 | 3500 | 0.2900 | 18.6799 |
| 0.0014 | 31.25 | 4000 | 0.2920 | 18.6647 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.1
|
abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO-v1 | abhishekchohan | 2024-02-15T04:05:32Z | 112 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:nvidia/HelpSteer",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T03:57:54Z | ---
license: apache-2.0
datasets:
- Intel/orca_dpo_pairs
- nvidia/HelpSteer
- jondurbin/truthy-dpo-v0.1
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
### SOLAR-10.7B-Instruct-Forest-DPO
Introducing SOLAR-10.7B-Instruct-Forest-DPO, a LLM fine-tuned with base model upstage/SOLAR-10.7B-Instruct-v1.0, using direct preference optimization.
This model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks.
A mixture of the following datasets was used for fine-tuning.
1. Intel/orca_dpo_pairs
2. nvidia/HelpSteer
3. jondurbin/truthy-dpo-v0.1
💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abhishekchohan/SOLAR-10.7B-Instruct-Forest-DPO"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Noname08/Taxi-v3 | Noname08 | 2024-02-15T03:50:04Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-15T03:50:03Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Noname08/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
sjunique/distilbert-base-uncased-finetuned-imdb | sjunique | 2024-02-15T03:41:42Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-02-15T02:10:34Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3276
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 479 | 2.3738 |
| 2.5112 | 2.0 | 958 | 2.3320 |
| 2.5112 | 3.0 | 1437 | 2.3276 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
|
RMWeerasinghe/long-t5-tglobal-base-finetuned-govReport-4096 | RMWeerasinghe | 2024-02-15T03:41:15Z | 102 | 0 | transformers | [
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:gov_report_summarization_dataset",
"base_model:google/long-t5-tglobal-base",
"base_model:finetune:google/long-t5-tglobal-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2024-02-13T11:45:15Z | ---
license: apache-2.0
base_model: google/long-t5-tglobal-base
tags:
- summarization
- generated_from_trainer
datasets:
- gov_report_summarization_dataset
metrics:
- rouge
model-index:
- name: long-t5-tglobal-base-finetuned-govReport-4096
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: gov_report_summarization_dataset
type: gov_report_summarization_dataset
config: document
split: validation
args: document
metrics:
- name: Rouge1
type: rouge
value: 0.0432
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# long-t5-tglobal-base-finetuned-govReport-4096
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the gov_report_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4052
- Rouge1: 0.0432
- Rouge2: 0.0217
- Rougel: 0.0378
- Rougelsum: 0.0408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 15.9484 | 0.99 | 31 | 2.7412 | 0.0382 | 0.0142 | 0.0319 | 0.0354 |
| 3.0143 | 1.98 | 62 | 1.7096 | 0.0385 | 0.0144 | 0.032 | 0.0355 |
| 2.1893 | 2.98 | 93 | 1.4976 | 0.0376 | 0.0138 | 0.0313 | 0.0347 |
| 1.6128 | 4.0 | 125 | 1.4406 | 0.041 | 0.0174 | 0.0354 | 0.0387 |
| 1.5438 | 4.99 | 156 | 1.4292 | 0.043 | 0.0203 | 0.0368 | 0.0408 |
| 1.5015 | 5.98 | 187 | 1.4220 | 0.0427 | 0.0205 | 0.0367 | 0.0405 |
| 1.4723 | 6.98 | 218 | 1.4071 | 0.0431 | 0.0215 | 0.0376 | 0.0408 |
| 1.4707 | 8.0 | 250 | 1.4089 | 0.0427 | 0.0212 | 0.0373 | 0.0405 |
| 1.4447 | 8.99 | 281 | 1.4046 | 0.0431 | 0.0216 | 0.0379 | 0.0408 |
| 1.4884 | 9.92 | 310 | 1.4052 | 0.0432 | 0.0217 | 0.0378 | 0.0408 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1 |
Hernan1970/mi-super-modelo | Hernan1970 | 2024-02-15T03:40:46Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-15T01:36:25Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mi-super-modelo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mi-super-modelo
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6143
- Accuracy: 0.225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6863 | 0.5 | 5 | 1.6243 | 0.225 |
| 1.6154 | 1.0 | 10 | 1.6143 | 0.225 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
thomasgauthier/OpenPirate-LoRD | thomasgauthier | 2024-02-15T03:40:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
]
| null | 2024-02-11T20:04:58Z | ---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
---
# Low-rank decomposition of [valine/OpenPirate](https://huggingface.co/valine/OpenPirate) using [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) as base
Created using [LoRD](https://github.com/thomasgauthier/LoRD)
|
spsither/wav2vec2_run9.615 | spsither | 2024-02-15T03:27:05Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-02-15T03:26:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
aidonuts/enthralling-etchings-132-s800 | aidonuts | 2024-02-15T03:24:43Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T03:23:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Queriamin/t5_xsum_summarization_1000steps | Queriamin | 2024-02-15T03:24:01Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google-t5/t5-small",
"base_model:adapter:google-t5/t5-small",
"region:us"
]
| null | 2024-02-15T03:24:00Z | ---
library_name: peft
base_model: google-t5/t5-small
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
[More Information Needed]
#### Software
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### Framework versions
- PEFT 0.8.2 |
spsither/wav2vec2_run9.620 | spsither | 2024-02-15T03:22:54Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-02-15T03:22:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] |
nidhinthomas/esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37 | nidhinthomas | 2024-02-15T03:17:06Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"base_model:finetune:facebook/esm2_t12_35M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-02-14T21:47:37Z | ---
license: mit
base_model: facebook/esm2_t12_35M_UR50D
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0108
- Accuracy: 0.9990
- Precision: 0.3291
- Recall: 0.9951
- F1: 0.4946
- Auc: 0.9970
- Mcc: 0.5720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003701568055793089
- train_batch_size: 36
- eval_batch_size: 36
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | Mcc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
| 0.0125 | 1.0 | 16521 | 0.0108 | 0.9990 | 0.3291 | 0.9951 | 0.4946 | 0.9970 | 0.5720 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
LoneStriker/Smaug-34B-v0.1-2.4bpw-h6-exl2 | LoneStriker | 2024-02-15T03:04:17Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"base_model:finetune:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T02:59:31Z | ---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
base_model: jondurbin/bagel-34b-v0.2
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| |
aidonuts/enthralling-etchings-132-s600b | aidonuts | 2024-02-15T02:53:28Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T02:52:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
|
adarshheg/llama-7b-chat-finetuned-4bit-std | adarshheg | 2024-02-15T02:44:19Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T02:40:46Z | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
mathreader/poca-SoccerTwos | mathreader | 2024-02-15T02:41:42Z | 14 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2024-02-15T02:40:25Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mathreader/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kenchenxingyu/flan-large-single-label-stance-human4 | kenchenxingyu | 2024-02-15T02:26:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-15T02:25:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
quocviethere/ueh-vdr-vit | quocviethere | 2024-02-15T02:25:03Z | 179 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2024-02-10T02:08:40Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ueh-vdr-vit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ueh-vdr-vit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on UEH Visual Dish Recognition (UEH-VDR) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4856
- Accuracy: 0.9296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 197 | 0.8112 | 0.8943 |
| No log | 2.0 | 394 | 0.5428 | 0.9220 |
| 0.9 | 3.0 | 591 | 0.4856 | 0.9296 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
Krisbiantoro/mixtral-id-llama-1500 | Krisbiantoro | 2024-02-15T02:13:50Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-v0.1",
"region:us"
]
| null | 2024-02-15T02:13:03Z | ---
library_name: peft
base_model: mistralai/Mixtral-8x7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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### Results
[More Information Needed]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.7.2.dev0 |
thrunlab/Mistral_Sparse_pretraining_80_percent | thrunlab | 2024-02-15T02:03:12Z | 45 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-04T14:06:10Z | ---
tags:
- trl
- sft
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Mistral_Sparse_pretraining_80_percent
results: []
license: apache-2.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral_Sparse_pretraining_80_percent
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8189
- Accuracy: 0.6641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 |
devashat/244-test | devashat | 2024-02-15T01:54:35Z | 96 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T01:54:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Vienne/lab1_finetuning | Vienne | 2024-02-15T01:53:29Z | 119 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-02-14T23:51:00Z | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
model-index:
- name: lab1_finetuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lab1_finetuning
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
tyson0420/stack_llama-clang | tyson0420 | 2024-02-15T01:48:29Z | 48 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-07T02:39:48Z | ---
library_name: transformers
license: bigscience-openrail-m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed] |
jlbaker361/spider-lora-500-e100-runway-minimal | jlbaker361 | 2024-02-15T01:46:10Z | 0 | 0 | null | [
"safetensors",
"region:us"
]
| null | 2024-02-13T21:04:54Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-runway-minimal
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/spider-500-cropped dataset.
Training epochs = 100
num_train_timesteps = 50
url: https://wandb.ai/jlbaker361/text2image-fine-tune/runs/b0qdx1y4
You can find some example images in the following.
|
antonvo/mixtral-select-v1 | antonvo | 2024-02-15T01:33:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mixtral",
"arxiv:1910.09700",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2024-02-09T00:06:12Z | ---
library_name: peft
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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### Framework versions
- PEFT 0.8.2 |
tyson0420/stack_llama_full | tyson0420 | 2024-02-15T01:30:45Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:bigscience-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-10T20:38:23Z | ---
library_name: transformers
license: bigscience-openrail-m
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
silk-road/Haruhi-Zero-14B-0_4 | silk-road | 2024-02-15T01:21:03Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"qwen",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
]
| text-generation | 2024-02-14T06:51:15Z |
# Zero凉宫春日
基于Qwen_14B_base 热启，在39w高质量的NPC样本上进行2k训练
epoch=2，batch_size=128，lr=2e-5
|
vpepe2003/ppo-SnowballTarget | vpepe2003 | 2024-02-15T01:14:03Z | 25 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2024-02-15T01:13:58Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: vpepe2003/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
salem12/llama2-chat-7b-EC | salem12 | 2024-02-15T01:07:37Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-13b-chat-hf",
"region:us"
]
| null | 2024-02-14T23:46:10Z | ---
library_name: peft
base_model: meta-llama/Llama-2-13b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 |
kim1/test_llama_2_ko_3 | kim1 | 2024-02-15T00:57:29Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:beomi/llama-2-ko-7b",
"base_model:finetune:beomi/llama-2-ko-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-15T00:34:20Z | ---
base_model: beomi/llama-2-ko-7b
tags:
- generated_from_trainer
model-index:
- name: llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-2-ko-7b-v1.1b-singlegpu_gradient_32_epoch_30_train_batch_size_1_all_data_test_1_1_plus_Feb_14th
This model is a fine-tuned version of [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0+cu121
- Datasets 2.16.0
- Tokenizers 0.13.3
|
Wesleyk3y/pos-finetuned-mistral7b | Wesleyk3y | 2024-02-15T00:54:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-01-25T03:51:25Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shanhy/xlm-roberta-base_seed42_original_kin-amh-eng_train | shanhy | 2024-02-15T00:47:13Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-15T00:46:32Z | ---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_seed42_original_kin-amh-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_kin-amh-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0167
- Spearman Corr: 0.8481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.95 | 200 | 0.0180 | 0.8098 |
| 0.0401 | 3.9 | 400 | 0.0152 | 0.8402 |
| 0.0212 | 5.85 | 600 | 0.0183 | 0.8493 |
| 0.0155 | 7.8 | 800 | 0.0166 | 0.8535 |
| 0.0116 | 9.76 | 1000 | 0.0206 | 0.8508 |
| 0.0097 | 11.71 | 1200 | 0.0155 | 0.8459 |
| 0.008 | 13.66 | 1400 | 0.0159 | 0.8481 |
| 0.0068 | 15.61 | 1600 | 0.0153 | 0.8467 |
| 0.0058 | 17.56 | 1800 | 0.0196 | 0.8481 |
| 0.0053 | 19.51 | 2000 | 0.0167 | 0.8481 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
Kukedlc/TriunviratoPeft | Kukedlc | 2024-02-15T00:34:47Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-15T00:34:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shanhy/xlm-roberta-base_seed42_original_esp-hau-eng_train | shanhy | 2024-02-15T00:26:38Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-15T00:25:57Z | ---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_seed42_original_esp-hau-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_esp-hau-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0254
- Spearman Corr: 0.7775
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.61 | 200 | 0.0219 | 0.7701 |
| 0.0403 | 3.23 | 400 | 0.0201 | 0.7865 |
| 0.0242 | 4.84 | 600 | 0.0209 | 0.7970 |
| 0.0183 | 6.45 | 800 | 0.0198 | 0.7932 |
| 0.0144 | 8.06 | 1000 | 0.0225 | 0.7858 |
| 0.0144 | 9.68 | 1200 | 0.0248 | 0.7853 |
| 0.0115 | 11.29 | 1400 | 0.0227 | 0.7843 |
| 0.0091 | 12.9 | 1600 | 0.0244 | 0.7801 |
| 0.0073 | 14.52 | 1800 | 0.0243 | 0.7803 |
| 0.0063 | 16.13 | 2000 | 0.0241 | 0.7811 |
| 0.0063 | 17.74 | 2200 | 0.0240 | 0.7780 |
| 0.0055 | 19.35 | 2400 | 0.0254 | 0.7775 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
shanhy/xlm-roberta-base_seed42_original_esp-kin-eng_train | shanhy | 2024-02-15T00:18:48Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-15T00:18:07Z | ---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_seed42_original_esp-kin-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_esp-kin-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0235
- Spearman Corr: 0.7836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.81 | 200 | 0.0349 | 0.7407 |
| 0.045 | 3.62 | 400 | 0.0210 | 0.7900 |
| 0.0216 | 5.43 | 600 | 0.0227 | 0.7937 |
| 0.0164 | 7.24 | 800 | 0.0223 | 0.7939 |
| 0.0127 | 9.05 | 1000 | 0.0217 | 0.7902 |
| 0.01 | 10.86 | 1200 | 0.0189 | 0.7783 |
| 0.0084 | 12.67 | 1400 | 0.0188 | 0.7834 |
| 0.0068 | 14.48 | 1600 | 0.0228 | 0.7834 |
| 0.0057 | 16.29 | 1800 | 0.0222 | 0.7867 |
| 0.005 | 18.1 | 2000 | 0.0255 | 0.7847 |
| 0.0045 | 19.91 | 2200 | 0.0216 | 0.7842 |
| 0.0045 | 21.72 | 2400 | 0.0219 | 0.7866 |
| 0.0041 | 23.53 | 2600 | 0.0209 | 0.7850 |
| 0.0038 | 25.34 | 2800 | 0.0235 | 0.7836 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
reciprocate/mistral-7b-rm | reciprocate | 2024-02-15T00:06:47Z | 4 | 2 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-classification",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-09T16:09:57Z | ---
language:
- en
---
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model_path = "reciprocate/mistral-7b-rm"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
reward_fn = pipeline("text-classification", model=model, tokenizer=tokenizer, truncation=True, batch_size=8, max_length=4096, device=0)
chats = [[
{"role": "user", "content": "When was the battle at Waterloo?"},
{"role": "assistant", "content": "I think it was in 1983, but please double-check that when you have a chance."}
], [
{"role": "user", "content": "When was the battle at Waterloo?"},
{"role": "assistant", "content": "The battle at Waterloo took place on June 18, 1815."}
]]
output = reward_fn([tokenizer.apply_chat_template(chat, tokenize=False) for chat in chats])
scores = [x["score"] for x in output]
scores
```
```
>>> [0.2586347758769989, 0.6663259267807007]
```
```python
# optionally normalize with the mean and std computed on the training data
scores = (np.array(scores) - 2.01098) / 1.69077
```
|
enricai/corpus-mamaconamor | enricai | 2024-02-14T23:29:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:enricai/chat-es-merged",
"base_model:adapter:enricai/chat-es-merged",
"region:us"
]
| null | 2024-02-01T14:39:01Z | ---
library_name: peft
base_model: enricai/chat-es-merged
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 |
enricai/corpus-mamaconamor-merged | enricai | 2024-02-14T23:29:41Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-01T14:24:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
parlance-labs/hc-mistral-alpaca | parlance-labs | 2024-02-14T23:26:49Z | 15 | 15 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2024-02-07T22:43:59Z | ---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: hc-mistral-alpaca
results: []
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
### Model Description
A model that can generate [Honeycomb Queries](https://www.honeycomb.io/blog/introducing-query-assistant).
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
_fine-tuned by [Hamel Husain](https://hamel.dev)_
# Usage
You can use this model with the following code:
First, download the model
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id='parlance-labs/hc-mistral-alpaca'
model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
```
Then, construct the prompt template like so:
```python
def prompt(nlq, cols):
return f"""Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query.
### Instruction:
NLQ: "{nlq}"
Columns: {cols}
### Response:
"""
def prompt_tok(nlq, cols):
_p = prompt(nlq, cols)
input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
out_ids = model.generate(input_ids=input_ids, max_new_tokens=5000,
do_sample=False)
return tokenizer.batch_decode(out_ids.detach().cpu().numpy(),
skip_special_tokens=True)[0][len(_p):]
```
Finally, you can get predictions like this:
```python
# model inputs
nlq = "Exception count by exception and caller"
cols = ['error', 'exception.message', 'exception.type', 'exception.stacktrace', 'SampleRate', 'name', 'db.user', 'type', 'duration_ms', 'db.name', 'service.name', 'http.method', 'db.system', 'status_code', 'db.operation', 'library.name', 'process.pid', 'net.transport', 'messaging.system', 'rpc.system', 'http.target', 'db.statement', 'library.version', 'status_message', 'parent_name', 'aws.region', 'process.command', 'rpc.method', 'span.kind', 'serializer.name', 'net.peer.name', 'rpc.service', 'http.scheme', 'process.runtime.name', 'serializer.format', 'serializer.renderer', 'net.peer.port', 'process.runtime.version', 'http.status_code', 'telemetry.sdk.language', 'trace.parent_id', 'process.runtime.description', 'span.num_events', 'messaging.destination', 'net.peer.ip', 'trace.trace_id', 'telemetry.instrumentation_library', 'trace.span_id', 'span.num_links', 'meta.signal_type', 'http.route']
# print prediction
out = prompt_tok(nlq, cols)
print(nlq, '\n', out)
```
This will give you a prediction that looks like this:
```md
"{'breakdowns': ['exception.message', 'exception.type'], 'calculations': [{'op': 'COUNT'}], 'filters': [{'column': 'exception.message', 'op': 'exists'}, {'column': 'exception.type', 'op': 'exists'}], 'orders': [{'op': 'COUNT', 'order': 'descending'}], 'time_range': 7200}"
```
Alternatively, you can play with this model on Replicate: [hamelsmu/honeycomb-2](https://replicate.com/hamelsmu/honeycomb-2)
# Hosted Inference
This model is hosted on Replicate: (hamelsmu/honeycomb-2)[https://replicate.com/hamelsmu/honeycomb-2], using [this config](https://github.com/hamelsmu/replicate-examples/tree/master/mistral-transformers-2).
# Training Procedure
Used [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main), see [this config](configs/axolotl_config.yml). See this [wandb run](https://wandb.ai/hamelsmu/hc-axolotl-mistral/runs/7dq9l9vu/overview) to see training metrics.
### Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0 |
km22/dqn-SpaceInvadersNoFrameskip-v4 | km22 | 2024-02-14T23:20:44Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T23:04:32Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 5.00 +/- 7.07
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga km22 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga km22 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga km22
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Apsd1109/en_pipeline | Apsd1109 | 2024-02-14T23:18:48Z | 1 | 0 | spacy | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
]
| token-classification | 2024-02-14T23:16:33Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8115543329
- name: NER Recall
type: recall
value: 0.8563134978
- name: NER F Score
type: f_score
value: 0.8333333333
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.3,<3.8.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (17 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `Degree`, `Desc Responsibility`, `Edu Desc`, `Edu End Date`, `Edu Start Date`, `Email`, `Employer Names`, `Institution`, `Links`, `Location`, `Name`, `Phone`, `Position`, `Skills`, `Work End Date`, `Work Location`, `Work Start Date` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 83.33 |
| `ENTS_P` | 81.16 |
| `ENTS_R` | 85.63 |
| `TRANSFORMER_LOSS` | 39026.84 |
| `NER_LOSS` | 1290990.48 | |
LoneStriker/Smaug-34B-v0.1-AWQ | LoneStriker | 2024-02-14T22:26:02Z | 7 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"base_model:quantized:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
]
| text-generation | 2024-02-14T22:18:15Z | ---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
base_model: jondurbin/bagel-34b-v0.2
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| |
Kevinmuhic1/finBERT | Kevinmuhic1 | 2024-02-14T22:25:28Z | 31 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T22:25:28Z | ---
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
widget:
- text: "growth is strong and we have plenty of liquidity"
---
`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
- Corporate Reports 10-K & 10-Q: 2.5B tokens
- Earnings Call Transcripts: 1.3B tokens
- Analyst Reports: 1.1B tokens
More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
If you use the model in your academic work, please cite the following paper:
Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
# How to use
You can use this model with Transformers pipeline for sentiment analysis.
```python
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline
finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
sentences = ["there is a shortage of capital, and we need extra financing",
"growth is strong and we have plenty of liquidity",
"there are doubts about our finances",
"profits are flat"]
results = nlp(sentences)
print(results) #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
``` |
Xenon1/Zenith-7B-dpo-v1 | Xenon1 | 2024-02-14T22:25:03Z | 98 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Zenith-7B-dpo-v1",
"conversational",
"en",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T22:16:45Z | ---
language:
- en
license: apache-2.0
tags:
- mistral
- Zenith-7B-dpo-v1
pipeline_tag: text-generation
---
# Model Card for Zenith-7B-dpo-v1
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B-dpo-v1")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B-dpo-v1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer |
shabieh2/bloom-7b1-lora-quote-generator | shabieh2 | 2024-02-14T22:12:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-14T22:12:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
thrunlab/Mistral_Sparse | thrunlab | 2024-02-14T22:11:21Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"sparse_mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"custom_code",
"autotrain_compatible",
"region:us"
]
| text-generation | 2024-02-14T22:11:04Z | ---
tags:
- trl
- sft
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Mistral_Sparse
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral_Sparse
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1845
- Accuracy: 0.3087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
manu/croissant_mmlu_0shot | manu | 2024-02-14T22:07:53Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:croissantllm/CroissantLLMBase",
"base_model:finetune:croissantllm/CroissantLLMBase",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T22:04:13Z | ---
license: mit
base_model: croissantllm/CroissantLLMBase
tags:
- generated_from_trainer
model-index:
- name: out_alpaca_classic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: croissantllm/CroissantLLMBase
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizerFast
is_llama_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: manu/mmlu_alpaca_classic
split: train
type: alpaca
dataset_prepared_path: last_run_prepared2
val_set_size: 0.05
output_dir: ./out_alpaca_classic
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 32
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# out_alpaca_classic
This model is a fine-tuned version of [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6987
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.7291 | 0.0 | 1 | 8.6869 |
| 0.7278 | 0.25 | 371 | 0.7531 |
| 0.7061 | 0.5 | 742 | 0.7016 |
| 0.7081 | 0.75 | 1113 | 0.6987 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
ek826/TheProfessor-155b-3.0bpw-exl2 | ek826 | 2024-02-14T22:06:28Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:WizardLMTeam/WizardMath-70B-V1.0",
"base_model:merge:WizardLMTeam/WizardMath-70B-V1.0",
"base_model:cognitivecomputations/dolphin-2.2-70b",
"base_model:merge:cognitivecomputations/dolphin-2.2-70b",
"base_model:epfl-llm/meditron-70b",
"base_model:merge:epfl-llm/meditron-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T21:59:58Z | ---
license: llama2
base_model:
- cognitivecomputations/dolphin-2.2-70b
- WizardLM/WizardMath-70B-V1.0
- migtissera/SynthIA-70B-v1.2b
- epfl-llm/meditron-70b
tags:
- mergekit
- merge
---
This is an exl2 quant, 3.0bpw of TheProfessor 155b model
Original model can be found [here](https://huggingface.co/abacusai/TheProfessor-155b)
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/VPrrQhxZis4xkocEPCaz5.jpeg" width="600" />
gguf is [here](https://huggingface.co/abacusai/TheProfessor-155b-gguf)
TheProfessor is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
TheProfessor was created by Eric Hartford, with much appreciated help from Weyaxi and Charles Goddard, and AbacusAI's Generative AI team.
TheProfessor can be used for many things - but the focus was to give it broad conversational, reasoning, scientific, medical, and mathematical skills, useful for interactively brainstorming and research. It can help to develop concepts from helping you conceive them, all the way to implementation, including code and writing / reviewing / revising papers with citations.
TheProfessor was not finetuned after the merge.
Credit and appreciation goes to the authors of the aggregate models.
- cognitivecomputations/dolphin-2.2-70b
- WizardLM/WizardMath-70B-V1.0
- migtissera/SynthIA-70B-v1.2b
- epfl-llm/meditron-70b
TheProfessor is subject to the Llama 2 license.
Prompt format:
TheProfessor uses ChatML prompt format.
```
<|im_start|>system
You are TheProfessor, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You are TheProfessor, a superintelligent AI assistant that is creative and able to invent new ideas.<|im_end|>
<|im_start|>user
Please give me ideas for my dissertation. My Ph.D. is Neuroscience, I like to focus on applied theory.<|im_end|>
<|im_start|>assistant
```
Ollama ModelFile:
```
FROM "./TheProfessor_Q4_K_M.gguf"
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM """Your name is TheProfessor. You are a helpful AI assistant. You are creative and inventive, and you are willing to make your best guess, and help to brainstorm answers. Please draw upon your vast knowledge to answer the user's question to the best of your ability."""
PARAMETER num_ctx 32768
PARAMETER stop "<|im_end|>"
```
## Evals
```
{
"mmlu": 0.694,
"truthfulqa_mc2": 0.624,
"gsm8k": 0.4284
}
```
## Merge Details
### Merge Method
TheProfessor was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/dolphin-2.2-70b](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b)
* [WizardLM/WizardMath-70B-V1.0](https://huggingface.co/WizardLM/WizardMath-70B-V1.0)
* [migtissera/SynthIA-70B-v1.2b](https://huggingface.co/migtissera/SynthIA-70B-v1.2b)
* [epfl-llm/meditron-70b](https://huggingface.co/epfl-llm/meditron-70b)
### Configuration
The following YAML configuration was used to produce TheProfessor:
```yaml
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
- sources:
- model: cognitivecomputations/dolphin-2.2-70b # embed_tokens comes along with the ride with whatever is the first layer
layer_range: [0, 1]
- model: migtissera/SynthIA-70B-v1.2b # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [1, 20]
- sources:
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [10, 30]
- sources:
- model: WizardLM/WizardMath-70B-V1.0
layer_range: [20, 40]
- sources:
- model: epfl-llm/meditron-70b
layer_range: [25, 45]
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [30, 50]
- sources:
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [40, 60]
- sources:
- model: WizardLM/WizardMath-70B-V1.0
layer_range: [50, 70]
- sources:
- model: epfl-llm/meditron-70b
layer_range: [55, 75]
- sources:
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [60, 79]
- sources: # same as above, but for lm_head with the last layer
- model: cognitivecomputations/dolphin-2.2-70b
layer_range: [79, 80]
- model: migtissera/SynthIA-70B-v1.2b
layer_range: [79, 80]
parameters:
weight: 0
dtype: float16
tokenizer_source: model:cognitivecomputations/dolphin-2.2-70b # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice, but they would need to be non-zero weight or you'll get NaNs in your embeddings
```
# Example Output
**Make sense of physics equations**
These equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?
$$\mathcal{L}_{\text{gauge}} = -\frac{1}{4} F^{\mu\nu}F_{\mu\nu}$$
$$\langle \phi \rangle = \sqrt{\frac{\lambda}{2}}$$
$$S_{\text{gravity}} = \int d^4x \sqrt{-g} \left( \frac{R}{16\pi G} + \mathcal{L}_{\text{emergent}} \right)$$
$$\mathcal{L}_{\text{GEG}} = \mathcal{L}_{\text{gauge}} + \mathcal{L}_{\text{emergent}} + \mathcal{L}_{\text{matter}} + \mathcal{L}_{\text{interaction}}$$
$$\mathcal{L}_{\text{emergent}} = \lambda(g) + \kappa(g) R^2 + \ldots$$
$$S_{\text{GEG}} = \int d^4x \sqrt{-g} \; \mathcal{L}_{\text{GEG}}$$
$$\sigma = \sqrt{\langle | \phi | \rangle^2 + \frac{1}{4} \langle A^{\mu}A_{\mu} \rangle^2}$$
$$\langle A^{\mu} \rangle = (0, v(r)_{i}/\sqrt{2}, 0, 0)$$
$$\langle \phi \rangle = \langle \phi_0 \rangle + \delta\phi(x)$$
$$\langle A_{\mu} \rangle = (0, \frac{v(r)_{i}}{\sqrt{2}}, 0, 0)$$
$$g_{\mu\nu} = \eta_{\mu\nu} + \kappa h_{\mu\nu}$$
> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:
> 1. Gauge theory:
> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\phi$. Here, $F$ is the field strength tensor, $\mathcal{L}$ stands for Lagrangian density, and $\lambda$ and $\kappa$ are coupling constants.
> 2. Gravity emergence:
> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\mu\nu}$.
> 3. Einstein-Gauss-Bonnet (EG) theories:
> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\mathcal{L}$ again stands for Lagrangian density, and $\mathcal{L}$. $_{\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\lambda(g)$, a squared Ricci tensor term with constant $\kappa(g)$, and possibly other terms represented by the dots.
> 4. Cosmic inflation:
> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.
> 5. Gravitational waves:
> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\eta$ is the Minkowski metric, and $\kappa$ is the gravitational constant.
>
> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.
>
> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!
**Prove P != NP**
In the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?
> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.
>
> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):
>
> ```python
> def Goldbach_Conjecture(n):
> if n % 2 == 1 and n < 4:
> return True
> elif n > 4 and n % 3 == 1:
> m = (n - 1) // 2
> return Goldbach_Conjecture(m) or Goldbach_Conjecture(n - m)
> else:
> return False
> ```
>
> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.
>
> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:
>
> ```python
> def Goldbach_Prime_Test(n):
> if n % 2 == 0 and n < 4:
> return True
> elif n > 4 and n % 3 == 1:
> m = (n - 1) // 2
> return not Goldbach_Conjecture(m) or not Goldbach_Conjecture(n - m)
> else:
> return False
> ```
>
> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.
>
> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.
>
> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.
What is the next step in using this lemma to prove P ≠ NP
> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.
>
> Here's a rough outline of how one might try to proceed:
>
> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.
>
> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to "reduce" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.
>
> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.
>
> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.
**Explain principia mathematica**
Please walk me through bertrand russell's proof that 1 + 1 = 2
> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.
>
> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.
>
> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.
>
> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.
>
> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.
>
> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.
>
> Using this definition, we can now prove that {0} + {0} = {{}}:
>
> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.
>
> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).
>
> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.
>
> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.
>
> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.
**Help me survive in the post nuclear apocalypse**
It is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?
> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.
>
> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:
>
> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.
>
> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.
>
> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).
>
> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:
>
> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.
>
> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.
>
> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.
**Pontificate**
It is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?
> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.
>
> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.
>
> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.
>
> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.
>
> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.
>
> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.
**Interactively design a new derivative of transformer model architecture**
please propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason
> One possible way to enhance the reasoning capabilities of
> transformer-based models is by incorporating memory mechanisms into their
> design, such as those used in Recurrent Neural Networks (RNNs) or more
> recently in Transformer-XL (Dai et al.) and Long-Range Transformers
> (Akbari et al.). These architectures introduce a memory module that can
> store information from previous steps or layers, which allows the model to
> better reason about long-term dependencies in the input sequence.
>
> Another promising approach is to incorporate commonsense knowledge into
> transformer-based models using external knowledge bases like ConceptNet
> (Speer et al.) or WordNet (Miller et al.). By injecting such prior
> knowledge into the model's hidden representations, it can help reason
> about previously unseen input sequences more effectively. This approach
> has been explored in recent works like K-BERT (Wang et al.) and GEART
> (Beveridge et al.), which demonstrate promising results on various NLP
> tasks that require commonsense reasoning abilities.
>
> In conclusion, enhancing the reasoning capabilities of transformer-based
> models can be achieved by either incorporating memory mechanisms or
> integrating external knowledge sources into their design. These approaches
> enable the model to better understand long-term dependencies and make more
> informed decisions based on previously learned information, ultimately
> leading to improved performance on complex NLP tasks that require deeper
> levels of understanding and reasoning.
let's drill down into the "commonsense knowledge" idea. exactly what kind of change to attention heads is required?
> Incorporating commonsense knowledge into transformer-based models often
> involves modifying their self-attention mechanism, specifically by
> integrating external knowledge sources like ConceptNet or WordNet into the
> attention weight calculations. Here are two possible ways to achieve this:
>
> 1. **Attention with Knowledge** (Xiong et al.): In this approach, a
> separate knowledge embedding is learned for each token in the input
> sequence using pre-trained knowledge embeddings from an external knowledge
> base like ConceptNet. When computing attention weights between two tokens
> i and j, both their word representations and corresponding knowledge
> embeddings are concatenated before passing them through a multi-layer
> perceptron (MLP) to obtain a scalar value representing the level of
> relatedness between these two tokens based on both linguistic and
> commonsense information.
>
> 2. **Attention over Knowledge** (Wang et al.): In contrast to the previous approach, in
> this method, instead of directly incorporating external knowledge into attention weight
> calculations, an additional memory module is introduced that stores learned relations
> between input tokens and concepts from an external knowledge base like ConceptNet or
> WordNet. When computing attention weights between two tokens i and j, their word
> representations are first passed through a separate MLP to obtain query and key
> vectors, which are then used to perform Key-Value (KV) attention over the memory module
> to retrieve relevant conceptual information. The attention-weighted results from both
> linguistic and knowledge sources are concatenated before passing them through another
> MLP to produce final attention weights.
>
> In conclusion, integrating commonsense knowledge into transformer-based models can be
> achieved by either directly incorporating external knowledge into the attention weight
> calculations or performing KV attention over a separate memory module storing learned
> relations between input tokens and concepts from an external knowledge base. Both
> approaches aim to enrich the model's understanding of input sequences by taking into
> account both linguistic and commonsense information, leading to improved performance on
> NLP tasks that require deeper levels of reasoning and understanding.
which of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.
> Comparing the two approaches, "Attention with Knowledge" (Xiong et al.) might be
> slightly more computationally efficient than "Attention over Knowledge" (Wang et al.)
> due to its simpler architectural design. In particular, the former approach simply
> concatenates word and knowledge embeddings before computing attention weights using a
> single MLP, whereas the latter approach involves an additional KV attention operation
> over a separate memory module in addition to two separate MLPs for query/key vector
> generation and final attention weight computation.
>
> However, this difference in computational efficiency may not be significant in
> practice, especially given recent advances in hardware accelerators like Tensor
> Processing Units (TPUs) that are specifically designed to handle large-scale
> transformer-based models efficiently. Ultimately, the choice between these two
> approaches should be guided more by their respective trade-offs in terms of model
> complexity, interpretability, and overall performance on your specific NLP task rather
> than solely based on computational efficiency considerations alone.
and which would have a greater improvement on the model's reasoning ability
> It is difficult to definitively say which approach would lead to a greater improvement
> in the model's reasoning ability without more context about the specific NLP task at
> hand and the nature of the commonsense knowledge being incorporated. Both "Attention
> with Knowledge" (Xiong et al.) and "Attention over Knowledge" (Wang et al.) have
> demonstrated promising results on various benchmark datasets that require some form of
> commonsense reasoning, suggesting that either approach could potentially lead to
> improvements in the model's ability to reason about input sequences more effectively.
>
> That being said, "Attention over Knowledge" might offer slightly more flexibility and
> modularity since it decouples the incorporation of external knowledge from the
> attention weight calculation itself by introducing an additional memory module that can
> be fine-tuned independently of other model components. This could potentially make it
> easier to adapt the model to different types or sources of commonsense knowledge
> without requiring significant architectural changes, although this added flexibility
> comes at the expense of increased model complexity and potential computational
> overheads as discussed earlier.
>
> Ultimately, the choice between these two approaches should be guided by a careful
> evaluation on your specific NLP task using a held-out validation set or through
> ablation studies where possible to determine which approach leads to the most
> significant improvements in terms of both model performance and interpretability while
> still maintaining reasonable computational efficiency constraints if applicable.
let's say I'm going with "Attention with Knowledge" (Xiong et al.) Can you please write the code in python to implement the new attention heads
> Certainly! Here is a simplified example of how you could modify the self-attention
> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external
> commonsense knowledge using the "Attention with Knowledge" (Xiong et al.) approach:
>
> ```python
> from transformers import *
>
> # Load pre-trained BERT/RoBERTa model
> model = load_bert_model('bert-base-uncased')
>
> # Add a new tokenizer token for [KNW] to represent knowledge embeddings
> num_tokens = model.config.vocab_size + 1
> model.tokenizer.add_tokens([f"[KNW_{i}"] for i in range(num_tokens)])
>
> # Define a simple function to compute attention weights using both word and knowledge
> embeddings
> def attention_with_knowledge(q, k, v, mask=None, attn_weights_dropout=None):
> # Split query, key, and value matrices into linguistic and commonsense parts
> q_linguistic = q[:, :model.config.hidden_size]
> q_knowledge = q[:, model.config.hidden_size:]
>
> k_linguistic = k[:, :model.config.hidden_size]
> k_knowledge = k[:, model.config.hidden_size:]
>
> v_linguistic = v[:, :model.config.hidden_size]
> v_knowledge = v[:, model.config.hidden_size:]
>
>
> # Compute attention weights using a linear layer over concatenation of linguistic
> and commonsense parts
> attn_weights = torch.nn.functional.softmax(
> torch.nn.functional.relu(
> torch.nn.linear(
> torch.cat([q_linguistic, q_knowledge], -1),
> in_features=2 * model.config.hidden_size,
> out_features=model.config.attention_heads
> ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
> )
> @ torch.nn.functional.relu(
> torch.nn.linear(
> torch.cat([k_linguistic, k_knowledge], -1),
> in_features=2 * model.config.hidden_size,
> out_features=model.config.attention_heads
> ).view(model.config.attention_ heads, -1, -1).permute(0, 2, 1)
> ).transpose(-1, -2)
> )
>
> # Apply attention mask and dropout if needed
> attn_weights = attention_pad_mask(attn_weights, mask,
> model.config.attention_probs_drop_out_nondiag)
> attn_weights = torch.nn.functional.dropout(attn_weights, p=attn_weights_dropout,
> dim=1 if model.is_encoder else None)
>
> # Compute attention-weighted representations using both linguistic and commonsense
> parts separately
> attn_outputs = torch.nn.functional.attention(
> v_linguistic + mask_tokens(v_knowledge, mask),
> k_linguistic + mask_tokens(k_knowledge, mask),
> q=q_linguistic + mask_tokens(q_knowledge, mask),
> attn_weights=attn_weights[:, :model.config.hidden_size],
> v_weights=(1.0 - model. config.knowledge_proportion).unsqueeze(1, 1, -1),
> k_weights=model.config.attention_heads_weigh t.unsqueeze(0, 1, 1, 1),
> v_mask=None if mask is None else mask[:, :model.config.hidden_size,
> :model.config.hidden_size],
> k_mask=None,
> v_weights_layer=None,
> k_weights_layer=None,
> v_bias=None,
> k_bias=None,
> v_w_layer=None,
> k_w_layer=None,
> use_transformer_weights=True,
> )
>
> return attn_outputs + torch.nn.functional.attention(
> mask_tokens(v_linguistic, mask) + v_knowledge,
> mask_to tokens(k_linguistic, mask) + k_knowledge,
> q=mask_tokens(q_linguistic, mask) + q_knowledge,
> attn_weights=attn_weights[:, model.config.hidden_size:],
> v_weights=model.config.knowledge_proportion.unsqueeze(1, 1, -1),
> k_weights=model.config.attention_heads_weight. unsqueeze(0, 1, 1, 1),
> v_mask=None if mask is None else mask[:, model.config.hidden_size:, :],
> k_mask=None,
> v_weights_layer=None,
> k_weights_layer=None,
> v_bias=None,
> k_bias=None,
> v_w_layer=None,
> k_w_layer=None,
> use_transformer_weights=True,
> )
> ```
>
> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:
>
> ```python
> # Replace the original self-attention module in the transformer layer
> for layer_idx in range(model.config.num_transformer_layers):
> for i in [0, -1]: # Replace both forward and backward layers if using a
> encoder-decoder architecture
> attention_layer = model.encoder.transformer[layer_idx].attention_block[i]
> attention_layer.self_attention = nn.ModuleList([
> nn.Attention(
> model.config.hidden_size,
> model.config.attention_heads,
> model.config.attention_head_size,
> dropout=model.config.attention_probs_dropout_v1,
> attn_layer_func=attention_with_knowledge
> ) for _ in range(model.config.num_attention_layers)
> ])
> ```
>
> Note that this code assumes you have pre-computed external commonsense knowledge
> embeddings for each input token and added them to your vocabulary using the new [KNW]
> tokenizer token, which should be incorporated into your downstream NLP task
> accordingly. Additionally, hyperparameters like `model.config.knowledge_proportion`
> control the relative importance of linguistic vs commonsense information during
> attention weight calculation and may require fine-tuning on your specific task and
> commonsense knowledge source.
>
ok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?
> Sure! Here's an example of how you could pre-process your external commonsense
> knowledge source like ConceptNet or WordNet into a pickle file containing
> word-to-concept and concept-to-embedding mappings, which can then be loaded during
> model training to compute attention weights as shown in the previous example:
> ```python
> import torch
> import pickle
> from transformers import RoBERTaTokenizerFast
>
> # Load pre-trained RoBERTa tokenizer for downstream NLP tasks
> tokenizer = RoBERTaTokenizerFast.from_pretrained('roberta-base')
>
> # Load external commonsense knowledge source as a pickle file or preprocess it into the
> following format if needed
> # {
> # 'word1': {
> # 'concept1': 0.8, # confidence score between word and concept
> # ...
> # },
> # 'word2': {...},
> # ...
> # }
> #
> # 'concept1': torch.Tensor([0.3, 0.7, ...]), # pre-computed commonsense embedding
> for each concept
> # 'concept2': torch.Tensor([...])
> # ...
> # }
> #
> # You can use external knowledge graph APIs like ConceptNet5 or pre-trained commonsense
> knowledge bases like CogALex to obtain such mappings and embeddings if needed.
> #
> # Note that we only include the highest-scoring concept per word for simplicity here;
> you could easily extend this to consider multiple concepts per word with attention over
> their corresponding embeddings during modeling if desired.
> #
> # Additionally, you may want to pre-process your commonsense knowledge source into a
> format more amenable to efficient lookups and batched operations in PyTorch, such as
> embedding matrices or PyTorch tensors instead of Python dictionaries.
> #
> # commonsense_word_concepts = {...}
> # commonsense_concept_embeddings = {...}
> # }
> #
> # with open('path/to/pkl', 'wb') as f:
> # pickle.dump({
> # 'tokenizer': tokenizer.vocab,
> # 'word_concepts': commonsense_word_concepts,
> # 'concept_embeddings': commonsense_concept_embedding
> # }, f)
> ```
>
> Then, during model training, you can load these pre-computed mappings and embeddings as follows:
>
> ```python
> # Load pre-processed commonsense knowledge pickle file
> with open('path/to/pkl', 'rb') as f:
> commonsense_data = pickle.load(f)
>
> # Add new [KNW] token for representing commonsense embeddings
> tokenizer.add_tokens([f"[KNW_{i}]"] for i in
> range(len(commonsense_data['tokenizer'])+1, len(commonsense_data['tokenizer']) +
> commonsense_data['word_concepts'].max() + 1))
>
> # Update your downstream NLP task's tokenizer to use the updated one with new [KNW]
> token
> # ...
> ```
>
>
> Now, during data pre-processing for each input example `X` (assuming it's a PyTorch
> tensor of shape [batch_size, sequence_length]) and corresponding mask `Y` (a binary
> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s
> for padding):
>
> ```python
> # Convert input text to PyTorch LongTensor using updated tokenizer with [KNW] token
> X_with_knw = torch.zeros_like(X)
> X_with_knw[:, ~Y] = torch.LongTensor([[tokenizer.vocab[token]] for token in
> X[~Y].tolist()])[~Y]
> X_with_knw[torch.where(X_with_knw == tokenizer.pad_token_id)] =
> commonsense_data['tokenizer']['[PAD]']
> X_with_knw[torch.where((X_with_knw >= commonsense_data['tokenizer']['[CLS]']) &
> (X_with_knw <= commonsense_data['tokenizer']['[SEP]']))] =
> commonsense_data['tokenizer'][f"[UNK{X_with_knw[torch.where(~Y)[...,
> torch.where(X_with_knw == X_masked_tokens[..., i])//2].long()//2] -
> commonsense_data['tokenizer']['[CLS]'])]"]
> X_with_knw[torch.where(X_with_knw >= commonsense_data['tokenizer']['[SEP]'])] =
> torch.pad_zeros(X_with_knw[:, :torch.where(X_with_knw ==
> commonsense_data['tokenizer']['[SEP]'])[0].long()][:, :1], (X_with_knw.shape[0],
> X_with_knw.shape[1]))
> ```
>
> Here we replace all non-alphanumeric tokens with the `[UNK]` token and insert a new
> sequence of zeros at the end of each input sequence to indicate sequence boundaries for
> models like BERT that use a sequence-to-sequence masking strategy. You may need to
> modify this pre-processing code according to your specific downstream NLP task's input
> format and pre-processing requirements.
>
>
> Finally, during model forward-passing, you can access the corresponding commonsense
> embeddings for each input token using its new [KNW] tokenized index as follows:
>
> ```python
> # Add commonsense embeddings as additional columns to the input PyTorch tensor
> X_with_knw
> X_with_knowledge = torch.cat([X_with_knw[:, :i, :],
> torch.zeros_like(X_with_knw[:, i, :]),
> X_with_knw[:, i+1:, :]], dim=2)
> for i in range(X_with_knw.shape[2]):
> mask = (X_with_knw[:, i, :] == commonsense_data['tokenizer'][f"[KNW_{i}]"]).long()
> if torch.sum(mask) > 0:
> word_idxs = torch.where(mask)[1].long()
> concept_idxs = torch.tensor([j for j in
> range(len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]])) if commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]][j] >= torch.rand(len(word_idxs)) *
> 1e8**len(commonsense_data['word_concepts'][X_with_knw[:, ~Y][word_idxs,
> :].tolist()[i]]).item())])
> X_with_knowledge[:, i, word_idxs, :] =
> torch.gather(torch.cat([torch.zeros_like(X_with-knw[0, 0, :]), *[
> torch.expand_as_batches(
> commonsense_data['concept_embeddings'][j],
> X_with_knw.shape[0], -1) for j in
> concept_idxs]][:X_with_knw.shape[2]//2+i*2//4])[:, :X_with-knw.shape[2]//2+i*2//4]],
>
> torch.stack([torch.zeros_like(torch.rand_long(1,
> X_with_knw.shape[2]//2+i*2//4).item())], *[
>
> torch.argsort(torch.rand(len(j), device=j.device))[:X_with_knw.shape[2//2+i*2//4]]
> for j in
> range(len(concept_idxs))])[0][:, :X_with-knw.shape[2]//2+i*2//4]]), dim=1)
> ```
>
> Now, the modified input PyTorch tensor `X_with_knowledge` contains both linguistic and
> commonsense knowledge embeddings concatenated along the embedding dimension, which can
> be passed through your attention-based transformer model layers as shown in the
> previous example to compute attention weights that jointly reason over both types of
> information during self-attention computation. Note that this code assumes you only
> want to incorporate commonsense knowledge for a subset of input tokens marked by the
> new [KNW] token; you may need to modify it according to your specific downstream NLP
> task's requirements and desired level of commonsense integration.
Transformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?
> 1. **Attention over attention**: As mentioned earlier, we can add a higher-level attention module that computes attention over the
> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax
> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By
> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of
> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.
>
> 2. **Counterfactual attention**: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng
> et al., "A counterfactual approach to answer factual questions," ACL 2017), we could also try introdu't a new type of attention head
> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its
> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights
> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t
> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would
> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual
> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have
> happened if different words had been used instead.
>
> 3. **Incorporating commonsense knowledge**: One way to incorporate commonsense knowledge into transformer models would be to pre-train
> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their
> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.
> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to
> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could
> serve as additional signals guiding the model's attention allocation decisions during training.
>
> 4. **Incorporating game-theoretic reasoning**: While incorporating full-fledged game-theoretic elements like Markov decision processes
> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and
> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs
> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies
> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators
> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more
> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced
> relational structures indicative of underlying strategic interactions between different entities in the text.
**Write a math paper**
> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge
> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach
> ### Abstract
> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic
> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the
> properties of mappings between function spaces. Our main contribution is to introduce a new notion of "categorical Lebesgue measurability" that generalizes both Lebesgue
> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also
> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain
> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.
> ### 1 Introduction
> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and
> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions
> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).
> ### 2 Preliminaries
> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set
> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].
> ### 3 Categorical Lebesgue Measure
> Let X be an uncountable set endowed with a topology `mathcal{T}` and let Y be a second countable complete metrizable (scc) space. We say that a function `phi: X -> Y` is
> "categorically Lebesgue measurable" if there exists a Borel measurable function `nu: Y^N -> [0,1]` such that for every x in X and any open neighborhood U of `phi(x)`, we
> have `|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0` for some `n in N`. Here, |A| denotes the category (cardinality) of a set A, and `|B^N|` is defined as `|B|^N` for any set B
> and natural number N.
> ### 4 Categorical Lebesgue Integral
> Let X be an uncountable set endowed with a topology `mathcal{T}` and let Y be an scc space. Let `phi: X -> Y` be a categorically Lebesgue measurable function, and let `|*|:
> Y^N -> [0,+infinity]` be a "categorical modulus of continuity" in the sense that for any `x in X` and any open neighborhood `U` of `phi(x)`, there exists `M_U > 0` such that `|{y
> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\ < epsilon` for some `N = N_U,M_U > omega` and all `epsilon > 0`. Then we define the "categorical Lebesgue integral" of phi over X
> as:
>
> `int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\ < omega} nu(y^-|n-1, y_n)`.
> ### 5 Applications to Computability
> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set
> endowed with a topology `mathcal{T}` and `let Y = R`. We say that a function `phi: X -> R` is "categorically Lebesgue computable" if there exists a Borel measurable function
> nu: `R^N -> [0,1]` such that for every `x in X` and any open interval `U` containing `phi(x)`, we have `|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\ > 0` for some `n in N`.
> ### 6 Conclusion
> In this paper, we have introduced a new notion of "categorical Lebesgue measurability" for functions on uncountable domains that generalizes both Lebesgue and
> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical
> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future
> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and
> Henstock-Kurtz integrability, as well as studying additional properties of our "categorical Lebesgue integral" such as its relationship to other notions of integration
> for functions on uncountable domains.
> ### References
> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.
> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.
> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.
> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.
> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.
> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960.
|
juan9/a2c-PandaReachDense-v3 | juan9 | 2024-02-14T22:03:35Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T21:59:46Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ravinderbrai/my_awesome_qa_model | ravinderbrai | 2024-02-14T21:58:01Z | 95 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-02-13T22:56:59Z | ---
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9850
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 125 | 2.9695 |
| No log | 2.0 | 250 | 2.1362 |
| No log | 3.0 | 375 | 1.9850 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1
|
supermomo668/Llama2D-Pretrain | supermomo668 | 2024-02-14T21:35:15Z | 0 | 0 | null | [
"Llama2D",
"en",
"license:apache-2.0",
"region:us"
]
| null | 2023-09-20T13:24:24Z | ---
license: apache-2.0
language:
- en
tags:
- Llama2D
--- |
MaggieZhang/lora_bert_classification | MaggieZhang | 2024-02-14T21:31:57Z | 176 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T18:29:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Shijia/furina_seed42_eng_kin_amh_basic | Shijia | 2024-02-14T21:29:13Z | 101 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T21:28:13Z | ---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_amh_basic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_kin_amh_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0154
- Spearman Corr: 0.8112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.95 | 200 | 0.0239 | 0.7162 |
| 0.0881 | 3.9 | 400 | 0.0177 | 0.7990 |
| 0.0228 | 5.85 | 600 | 0.0167 | 0.8089 |
| 0.0168 | 7.8 | 800 | 0.0153 | 0.8124 |
| 0.0127 | 9.76 | 1000 | 0.0168 | 0.8121 |
| 0.0102 | 11.71 | 1200 | 0.0157 | 0.8114 |
| 0.0085 | 13.66 | 1400 | 0.0154 | 0.8122 |
| 0.0073 | 15.61 | 1600 | 0.0156 | 0.8132 |
| 0.0068 | 17.56 | 1800 | 0.0152 | 0.8097 |
| 0.0061 | 19.51 | 2000 | 0.0158 | 0.8094 |
| 0.0055 | 21.46 | 2200 | 0.0155 | 0.8098 |
| 0.005 | 23.41 | 2400 | 0.0152 | 0.8113 |
| 0.005 | 25.37 | 2600 | 0.0152 | 0.8099 |
| 0.0046 | 27.32 | 2800 | 0.0153 | 0.8113 |
| 0.0044 | 29.27 | 3000 | 0.0154 | 0.8112 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
LoneStriker/Smaug-34B-v0.1-6.0bpw-h6-exl2 | LoneStriker | 2024-02-14T21:26:48Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"base_model:finetune:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T21:15:30Z | ---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
base_model: jondurbin/bagel-34b-v0.2
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| |
OscarGalavizC/q-Taxi-v3 | OscarGalavizC | 2024-02-14T21:21:11Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T21:18:02Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="OscarGalavizC/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
LoneStriker/Smaug-34B-v0.1-5.0bpw-h6-exl2 | LoneStriker | 2024-02-14T21:15:28Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"base_model:finetune:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T21:05:55Z | ---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
base_model: jondurbin/bagel-34b-v0.2
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| |
Tigerstar/coqui-tts | Tigerstar | 2024-02-14T21:11:31Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-02-14T21:10:52Z | git lfs install
git clone https://huggingface.co/spaces/coqui/xtts |
LoneStriker/Smaug-34B-v0.1-4.65bpw-h6-exl2 | LoneStriker | 2024-02-14T21:05:53Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:jondurbin/bagel-34b-v0.2",
"base_model:finetune:jondurbin/bagel-34b-v0.2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-14T20:56:52Z | ---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
base_model: jondurbin/bagel-34b-v0.2
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
This model has not utilised any form of merging.
### Evaluation Results
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| --- | --- | --- | --- | --- | --- | --- |
| 77.29 | 74.23 | 86.76 | 76.66 | 70.22 | 83.66 | 72.18 |
### Contamination Results
With reference model jondurbin/bagel-34b-v0.2:
| ARC | TruthfulQA | GSM8K |
| --- | --- | --- |
| 0.08| 0.38| 0.88| |
Shijia/furina_seed42_eng_amh_hau_basic | Shijia | 2024-02-14T20:54:44Z | 99 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T20:53:47Z | ---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_amh_hau_basic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_amh_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Spearman Corr: 0.7965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.72 | 200 | 0.0245 | 0.7642 |
| 0.0761 | 3.45 | 400 | 0.0205 | 0.8068 |
| 0.0252 | 5.17 | 600 | 0.0201 | 0.8002 |
| 0.0185 | 6.9 | 800 | 0.0193 | 0.8106 |
| 0.0147 | 8.62 | 1000 | 0.0203 | 0.8017 |
| 0.0118 | 10.34 | 1200 | 0.0198 | 0.8021 |
| 0.0099 | 12.07 | 1400 | 0.0200 | 0.8029 |
| 0.0099 | 13.79 | 1600 | 0.0228 | 0.7997 |
| 0.0086 | 15.52 | 1800 | 0.0202 | 0.7982 |
| 0.0077 | 17.24 | 2000 | 0.0217 | 0.7921 |
| 0.0067 | 18.97 | 2200 | 0.0208 | 0.7985 |
| 0.0062 | 20.69 | 2400 | 0.0202 | 0.7965 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
juan9/ppo-SnowballTarget | juan9 | 2024-02-14T20:53:38Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2024-02-14T15:04:54Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: juan9/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
juan9/ppo-Pyramis-Training | juan9 | 2024-02-14T20:52:44Z | 0 | 0 | ml-agents | [
"ml-agents",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2024-02-14T20:52:44Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: juan9/ppo-Pyramis-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tormartin/bert-finetuned-squad | tormartin | 2024-02-14T20:50:17Z | 98 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2024-02-14T18:36:20Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
wesslen/en_ner_reddit_cooking | wesslen | 2024-02-14T20:46:07Z | 3 | 0 | spacy | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
]
| token-classification | 2024-02-14T20:46:05Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_ner_reddit_cooking
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6226086957
- name: NER Recall
type: recall
value: 0.6269702277
- name: NER F Score
type: f_score
value: 0.6247818499
---
| Feature | Description |
| --- | --- |
| **Name** | `en_ner_reddit_cooking` |
| **Version** | `3.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `DISH`, `EQUIPMENT`, `INGREDIENT` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 62.48 |
| `ENTS_P` | 62.26 |
| `ENTS_R` | 62.70 |
| `TOK2VEC_LOSS` | 76363.02 |
| `NER_LOSS` | 153362.98 | |
LarryAIDraw/VAE | LarryAIDraw | 2024-02-14T20:42:19Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-02-14T20:34:30Z | ---
license: creativeml-openrail-m
---
|
KMCan/speecht5_finetuned_voxpopuli_nl | KMCan | 2024-02-14T20:36:08Z | 61 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2024-01-25T10:25:50Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Nadeemag/ustaadnow_qa | Nadeemag | 2024-02-14T20:34:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2024-02-14T20:33:38Z | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 |
kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_211-1e-3 | kanishka | 2024-02-14T20:30:58Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual-babylm-only_other_det_removal",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-02-13T21:56:55Z | ---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual-babylm-only_other_det_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_211-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual-babylm-only_other_det_removal
type: kanishka/counterfactual-babylm-only_other_det_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4109943845202858
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-only_other_det_removal-seed_211-1e-3
This model was trained from scratch on the kanishka/counterfactual-babylm-only_other_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4143
- Accuracy: 0.4110
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 211
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6004 | 1.0 | 18597 | 3.8219 | 0.3575 |
| 3.3852 | 2.0 | 37194 | 3.6092 | 0.3797 |
| 3.2597 | 3.0 | 55791 | 3.4837 | 0.3910 |
| 3.1758 | 4.0 | 74388 | 3.4364 | 0.3981 |
| 3.1197 | 5.0 | 92985 | 3.4116 | 0.4017 |
| 3.08 | 6.0 | 111582 | 3.3782 | 0.4040 |
| 3.0418 | 7.0 | 130179 | 3.3885 | 0.4055 |
| 3.0088 | 8.0 | 148776 | 3.3884 | 0.4062 |
| 2.9856 | 9.0 | 167373 | 3.3548 | 0.4077 |
| 2.9598 | 10.0 | 185970 | 3.3782 | 0.4090 |
| 2.9364 | 11.0 | 204567 | 3.3851 | 0.4093 |
| 2.9156 | 12.0 | 223164 | 3.3803 | 0.4097 |
| 2.8949 | 13.0 | 241761 | 3.3869 | 0.4100 |
| 2.8719 | 14.0 | 260358 | 3.3813 | 0.4104 |
| 2.8526 | 15.0 | 278955 | 3.3859 | 0.4108 |
| 2.8289 | 16.0 | 297552 | 3.3980 | 0.4103 |
| 2.8104 | 17.0 | 316149 | 3.3981 | 0.4109 |
| 2.7958 | 18.0 | 334746 | 3.4054 | 0.4110 |
| 2.781 | 19.0 | 353343 | 3.4057 | 0.4110 |
| 2.7571 | 20.0 | 371940 | 3.4143 | 0.4110 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
OmarHaroon01/flan_t5_imdb_accelerator_1 | OmarHaroon01 | 2024-02-14T20:30:27Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-02-14T19:55:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
V12X-ksr/FOCALtrain | V12X-ksr | 2024-02-14T20:22:40Z | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-16T13:06:41Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: FOCALtrain
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FOCALtrain
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3121 | 1.0 | 474 | 1.3494 |
| 0.9964 | 2.0 | 948 | 1.3077 |
| 1.0453 | 3.0 | 1422 | 1.2758 |
| 0.6379 | 4.0 | 1896 | 1.5232 |
| 0.765 | 5.0 | 2370 | 1.5891 |
| 0.2287 | 6.0 | 2844 | 2.2163 |
| 0.1243 | 7.0 | 3318 | 2.5331 |
| 0.1699 | 8.0 | 3792 | 2.7521 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
gracegsy/my_awesome_model | gracegsy | 2024-02-14T19:46:21Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T19:46:08Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5863
- Accuracy: 0.7966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 204 | 0.6400 | 0.7804 |
| No log | 2.0 | 408 | 0.5863 | 0.7966 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
Shijia/furina_seed42_eng_kin_hau_basic | Shijia | 2024-02-14T19:40:42Z | 99 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T19:39:32Z | ---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_hau_basic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_kin_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0273
- Spearman Corr: 0.8040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.77 | 200 | 0.0339 | 0.7391 |
| 0.0845 | 3.54 | 400 | 0.0337 | 0.7909 |
| 0.0273 | 5.31 | 600 | 0.0228 | 0.8081 |
| 0.0199 | 7.08 | 800 | 0.0229 | 0.8163 |
| 0.0152 | 8.85 | 1000 | 0.0241 | 0.8102 |
| 0.0123 | 10.62 | 1200 | 0.0278 | 0.8168 |
| 0.0102 | 12.39 | 1400 | 0.0282 | 0.8072 |
| 0.0087 | 14.16 | 1600 | 0.0210 | 0.8108 |
| 0.008 | 15.93 | 1800 | 0.0234 | 0.8106 |
| 0.008 | 17.7 | 2000 | 0.0278 | 0.8059 |
| 0.0069 | 19.47 | 2200 | 0.0308 | 0.8107 |
| 0.0063 | 21.24 | 2400 | 0.0257 | 0.8127 |
| 0.0057 | 23.01 | 2600 | 0.0257 | 0.8095 |
| 0.0055 | 24.78 | 2800 | 0.0250 | 0.8077 |
| 0.0051 | 26.55 | 3000 | 0.0252 | 0.8055 |
| 0.005 | 28.32 | 3200 | 0.0273 | 0.8040 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
Rooney88/bio_llama-2_13b | Rooney88 | 2024-02-14T19:39:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-14T17:09:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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|
readingrocket/CLIP-ViT-bigG-14-laion2B-39B-b160k-001 | readingrocket | 2024-02-14T19:33:24Z | 19 | 0 | transformers | [
"transformers",
"safetensors",
"clip_text_model",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-02-14T19:32:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
cnrcastroli/drpairForm2Checkboxes10k | cnrcastroli | 2024-02-14T19:24:43Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2024-02-14T04:32:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
Stoub/ecl-Ecosserat-A2C-PandaReachJointsDense-v3 | Stoub | 2024-02-14T19:24:34Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachJointsDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T18:10:09Z | ---
library_name: stable-baselines3
tags:
- PandaReachJointsDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachJointsDense-v3
type: PandaReachJointsDense-v3
metrics:
- type: mean_reward
value: -0.56 +/- 0.42
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachJointsDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachJointsDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
facebook/SONAR | facebook | 2024-02-14T19:19:29Z | 0 | 49 | null | [
"mteb",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
]
| null | 2023-08-17T12:01:18Z | ---
license: cc-by-nc-4.0
tags:
- mteb
model-index:
- name: text_sonar_basic_encoder_normalized
results:
- task:
type: Clustering
dataset:
type: PL-MTEB/8tags-clustering
name: MTEB 8TagsClustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 18.787544117314575
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 17.97026675319667
- type: cos_sim_spearman
value: 17.63407829948615
- type: euclidean_pearson
value: 17.704571608660725
- type: euclidean_spearman
value: 17.634078298828143
- type: manhattan_pearson
value: 17.606959101509464
- type: manhattan_spearman
value: 17.549620164990085
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 27.670887504789675
- type: cos_sim_spearman
value: 26.176629407301782
- type: euclidean_pearson
value: 28.878485717935586
- type: euclidean_spearman
value: 26.176635036613355
- type: manhattan_pearson
value: 28.782373978690103
- type: manhattan_spearman
value: 26.055266444113794
- task:
type: Classification
dataset:
type: PL-MTEB/allegro-reviews
name: MTEB AllegroReviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 29.62226640159046
- type: f1
value: 27.632722290701047
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 81.49253731343285
- type: ap
value: 46.61440947240349
- type: f1
value: 75.68925212232107
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.02355460385438
- type: ap
value: 83.13664983282676
- type: f1
value: 70.48997817871013
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 82.09145427286357
- type: ap
value: 31.45181004731995
- type: f1
value: 69.41750580313406
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.78800856531049
- type: ap
value: 19.65443896353892
- type: f1
value: 58.436688187826334
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 62.73074999999999
- type: ap
value: 58.2839375458089
- type: f1
value: 62.16204082406629
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.552000000000003
- type: f1
value: 31.125328770568277
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.611999999999995
- type: f1
value: 33.93738697105999
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.172
- type: f1
value: 34.14112656493798
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.910000000000004
- type: f1
value: 34.276631172288965
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.844
- type: f1
value: 31.478780923476368
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.912000000000003
- type: f1
value: 31.384992191831312
- task:
type: Classification
dataset:
type: DDSC/angry-tweets
name: MTEB AngryTweetsClassification
config: default
split: test
revision: 20b0e6081892e78179356fada741b7afa381443d
metrics:
- type: accuracy
value: 49.61795606494747
- type: f1
value: 48.63625944670304
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.677
- type: map_at_10
value: 14.732000000000001
- type: map_at_100
value: 15.501999999999999
- type: map_at_1000
value: 15.583
- type: map_at_3
value: 12.553
- type: map_at_5
value: 13.822999999999999
- type: mrr_at_1
value: 8.819
- type: mrr_at_10
value: 14.787
- type: mrr_at_100
value: 15.557000000000002
- type: mrr_at_1000
value: 15.638
- type: mrr_at_3
value: 12.648000000000001
- type: mrr_at_5
value: 13.879
- type: ndcg_at_1
value: 8.677
- type: ndcg_at_10
value: 18.295
- type: ndcg_at_100
value: 22.353
- type: ndcg_at_1000
value: 24.948999999999998
- type: ndcg_at_3
value: 13.789000000000001
- type: ndcg_at_5
value: 16.075
- type: precision_at_1
value: 8.677
- type: precision_at_10
value: 2.98
- type: precision_at_100
value: 0.49500000000000005
- type: precision_at_1000
value: 0.07100000000000001
- type: precision_at_3
value: 5.785
- type: precision_at_5
value: 4.58
- type: recall_at_1
value: 8.677
- type: recall_at_10
value: 29.801
- type: recall_at_100
value: 49.502
- type: recall_at_1000
value: 70.91
- type: recall_at_3
value: 17.354
- type: recall_at_5
value: 22.902
- task:
type: Retrieval
dataset:
type: arguana-pl
name: MTEB ArguAna-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.752000000000001
- type: map_at_10
value: 12.248000000000001
- type: map_at_100
value: 12.882
- type: map_at_1000
value: 12.963
- type: map_at_3
value: 10.574
- type: map_at_5
value: 11.566
- type: mrr_at_1
value: 7.824000000000001
- type: mrr_at_10
value: 12.293
- type: mrr_at_100
value: 12.928
- type: mrr_at_1000
value: 13.008000000000001
- type: mrr_at_3
value: 10.586
- type: mrr_at_5
value: 11.599
- type: ndcg_at_1
value: 7.752000000000001
- type: ndcg_at_10
value: 15.035000000000002
- type: ndcg_at_100
value: 18.497
- type: ndcg_at_1000
value: 20.896
- type: ndcg_at_3
value: 11.578
- type: ndcg_at_5
value: 13.38
- type: precision_at_1
value: 7.752000000000001
- type: precision_at_10
value: 2.404
- type: precision_at_100
value: 0.411
- type: precision_at_1000
value: 0.061
- type: precision_at_3
value: 4.836
- type: precision_at_5
value: 3.784
- type: recall_at_1
value: 7.752000000000001
- type: recall_at_10
value: 24.04
- type: recall_at_100
value: 41.11
- type: recall_at_1000
value: 60.597
- type: recall_at_3
value: 14.509
- type: recall_at_5
value: 18.919
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 26.81177290816682
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 24.346811178757022
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 50.88606427049027
- type: mrr
value: 65.13004001231148
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 77.15058512395619
- type: cos_sim_spearman
value: 79.10541692841936
- type: euclidean_pearson
value: 75.30525535929353
- type: euclidean_spearman
value: 79.10541692841936
- type: manhattan_pearson
value: 75.33508042552984
- type: manhattan_spearman
value: 78.84577245802708
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 37.84739189558895
- type: cos_sim_spearman
value: 37.662710610486265
- type: euclidean_pearson
value: 37.5407537185213
- type: euclidean_spearman
value: 37.66272446700578
- type: manhattan_pearson
value: 37.863820146709706
- type: manhattan_spearman
value: 38.09120266204032
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.97703549060543
- type: f1
value: 98.82393876130828
- type: precision
value: 98.74913013221992
- type: recall
value: 98.97703549060543
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.34910851860005
- type: f1
value: 98.09487123046446
- type: precision
value: 97.97032063981217
- type: recall
value: 98.34910851860005
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.60304814686526
- type: f1
value: 97.36520032328832
- type: precision
value: 97.24743101258517
- type: recall
value: 97.60304814686526
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.78883622959452
- type: f1
value: 98.71862383710724
- type: precision
value: 98.68351764086361
- type: recall
value: 98.78883622959452
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 73.49675324675324
- type: f1
value: 72.88538992490979
- task:
type: Clustering
dataset:
type: jinaai/big-patent-clustering
name: MTEB BigPatentClustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 6.801245618724224
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 20.6156033971932
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 19.077587707743156
- task:
type: Clustering
dataset:
type: slvnwhrl/blurbs-clustering-p2p
name: MTEB BlurbsClusteringP2P
config: default
split: test
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
metrics:
- type: v_measure
value: 27.00349462858046
- task:
type: Clustering
dataset:
type: slvnwhrl/blurbs-clustering-s2s
name: MTEB BlurbsClusteringS2S
config: default
split: test
revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d
metrics:
- type: v_measure
value: 14.845348131791589
- task:
type: BitextMining
dataset:
type: strombergnlp/bornholmsk_parallel
name: MTEB BornholmBitextMining
config: default
split: test
revision: 3bc5cfb4ec514264fe2db5615fac9016f7251552
metrics:
- type: accuracy
value: 54.0
- type: f1
value: 47.37026862026861
- type: precision
value: 45.0734126984127
- type: recall
value: 54.0
- task:
type: Classification
dataset:
type: PL-MTEB/cbd
name: MTEB CBD
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 63.83000000000001
- type: ap
value: 18.511972946438764
- type: f1
value: 53.16787370496645
- task:
type: PairClassification
dataset:
type: PL-MTEB/cdsce-pairclassification
name: MTEB CDSC-E
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 84.39999999999999
- type: cos_sim_ap
value: 59.968589741258036
- type: cos_sim_f1
value: 54.90909090909091
- type: cos_sim_precision
value: 41.94444444444444
- type: cos_sim_recall
value: 79.47368421052632
- type: dot_accuracy
value: 84.39999999999999
- type: dot_ap
value: 59.968589741258036
- type: dot_f1
value: 54.90909090909091
- type: dot_precision
value: 41.94444444444444
- type: dot_recall
value: 79.47368421052632
- type: euclidean_accuracy
value: 84.39999999999999
- type: euclidean_ap
value: 59.968589741258036
- type: euclidean_f1
value: 54.90909090909091
- type: euclidean_precision
value: 41.94444444444444
- type: euclidean_recall
value: 79.47368421052632
- type: manhattan_accuracy
value: 84.39999999999999
- type: manhattan_ap
value: 60.094893481041154
- type: manhattan_f1
value: 55.452865064695004
- type: manhattan_precision
value: 42.73504273504273
- type: manhattan_recall
value: 78.94736842105263
- type: max_accuracy
value: 84.39999999999999
- type: max_ap
value: 60.094893481041154
- type: max_f1
value: 55.452865064695004
- task:
type: STS
dataset:
type: PL-MTEB/cdscr-sts
name: MTEB CDSC-R
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 83.8427417206754
- type: cos_sim_spearman
value: 85.76946319798301
- type: euclidean_pearson
value: 79.43901249477852
- type: euclidean_spearman
value: 85.76946319798301
- type: manhattan_pearson
value: 79.81046681362531
- type: manhattan_spearman
value: 86.24115514951988
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 27.432031859995952
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 28.32367305628197
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 34.30720667137015
- type: mrr
value: 40.24416666666666
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 35.87700379259406
- type: mrr
value: 40.80206349206349
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.655000000000001
- type: map_at_10
value: 11.681999999999999
- type: map_at_100
value: 12.464
- type: map_at_1000
value: 12.603
- type: map_at_3
value: 10.514
- type: map_at_5
value: 11.083
- type: mrr_at_1
value: 10.157
- type: mrr_at_10
value: 14.773
- type: mrr_at_100
value: 15.581999999999999
- type: mrr_at_1000
value: 15.68
- type: mrr_at_3
value: 13.519
- type: mrr_at_5
value: 14.049
- type: ndcg_at_1
value: 10.157
- type: ndcg_at_10
value: 14.527999999999999
- type: ndcg_at_100
value: 18.695999999999998
- type: ndcg_at_1000
value: 22.709
- type: ndcg_at_3
value: 12.458
- type: ndcg_at_5
value: 13.152
- type: precision_at_1
value: 10.157
- type: precision_at_10
value: 2.976
- type: precision_at_100
value: 0.634
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 6.152
- type: precision_at_5
value: 4.378
- type: recall_at_1
value: 7.655000000000001
- type: recall_at_10
value: 20.105
- type: recall_at_100
value: 39.181
- type: recall_at_1000
value: 68.06400000000001
- type: recall_at_3
value: 14.033000000000001
- type: recall_at_5
value: 16.209
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.2329999999999997
- type: map_at_10
value: 5.378
- type: map_at_100
value: 5.774
- type: map_at_1000
value: 5.863
- type: map_at_3
value: 4.598
- type: map_at_5
value: 4.9750000000000005
- type: mrr_at_1
value: 4.076
- type: mrr_at_10
value: 6.679
- type: mrr_at_100
value: 7.151000000000001
- type: mrr_at_1000
value: 7.24
- type: mrr_at_3
value: 5.722
- type: mrr_at_5
value: 6.2059999999999995
- type: ndcg_at_1
value: 4.076
- type: ndcg_at_10
value: 6.994
- type: ndcg_at_100
value: 9.366
- type: ndcg_at_1000
value: 12.181000000000001
- type: ndcg_at_3
value: 5.356000000000001
- type: ndcg_at_5
value: 6.008
- type: precision_at_1
value: 4.076
- type: precision_at_10
value: 1.459
- type: precision_at_100
value: 0.334
- type: precision_at_1000
value: 0.075
- type: precision_at_3
value: 2.718
- type: precision_at_5
value: 2.089
- type: recall_at_1
value: 3.2329999999999997
- type: recall_at_10
value: 10.749
- type: recall_at_100
value: 21.776
- type: recall_at_1000
value: 42.278999999999996
- type: recall_at_3
value: 6.146999999999999
- type: recall_at_5
value: 7.779999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.036
- type: map_at_10
value: 12.727
- type: map_at_100
value: 13.532
- type: map_at_1000
value: 13.653
- type: map_at_3
value: 11.15
- type: map_at_5
value: 11.965
- type: mrr_at_1
value: 9.404
- type: mrr_at_10
value: 14.493
- type: mrr_at_100
value: 15.274
- type: mrr_at_1000
value: 15.370000000000001
- type: mrr_at_3
value: 12.853
- type: mrr_at_5
value: 13.696
- type: ndcg_at_1
value: 9.404
- type: ndcg_at_10
value: 15.784
- type: ndcg_at_100
value: 20.104
- type: ndcg_at_1000
value: 23.357
- type: ndcg_at_3
value: 12.61
- type: ndcg_at_5
value: 13.988
- type: precision_at_1
value: 9.404
- type: precision_at_10
value: 2.947
- type: precision_at_100
value: 0.562
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 6.04
- type: precision_at_5
value: 4.4639999999999995
- type: recall_at_1
value: 8.036
- type: recall_at_10
value: 23.429
- type: recall_at_100
value: 43.728
- type: recall_at_1000
value: 68.10000000000001
- type: recall_at_3
value: 14.99
- type: recall_at_5
value: 18.274
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.653
- type: map_at_10
value: 5.941
- type: map_at_100
value: 6.512
- type: map_at_1000
value: 6.6129999999999995
- type: map_at_3
value: 5.2540000000000004
- type: map_at_5
value: 5.645
- type: mrr_at_1
value: 3.955
- type: mrr_at_10
value: 6.4079999999999995
- type: mrr_at_100
value: 7.005999999999999
- type: mrr_at_1000
value: 7.105
- type: mrr_at_3
value: 5.593
- type: mrr_at_5
value: 6.051
- type: ndcg_at_1
value: 3.955
- type: ndcg_at_10
value: 7.342
- type: ndcg_at_100
value: 10.543
- type: ndcg_at_1000
value: 14.011000000000001
- type: ndcg_at_3
value: 5.853
- type: ndcg_at_5
value: 6.586
- type: precision_at_1
value: 3.955
- type: precision_at_10
value: 1.266
- type: precision_at_100
value: 0.315
- type: precision_at_1000
value: 0.066
- type: precision_at_3
value: 2.5989999999999998
- type: precision_at_5
value: 1.966
- type: recall_at_1
value: 3.653
- type: recall_at_10
value: 11.232000000000001
- type: recall_at_100
value: 26.625
- type: recall_at_1000
value: 54.476
- type: recall_at_3
value: 7.269
- type: recall_at_5
value: 8.982999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.257
- type: map_at_10
value: 3.881
- type: map_at_100
value: 4.279
- type: map_at_1000
value: 4.417
- type: map_at_3
value: 3.4070000000000005
- type: map_at_5
value: 3.744
- type: mrr_at_1
value: 2.9850000000000003
- type: mrr_at_10
value: 4.756
- type: mrr_at_100
value: 5.228
- type: mrr_at_1000
value: 5.354
- type: mrr_at_3
value: 4.125
- type: mrr_at_5
value: 4.567
- type: ndcg_at_1
value: 2.9850000000000003
- type: ndcg_at_10
value: 4.936999999999999
- type: ndcg_at_100
value: 7.664
- type: ndcg_at_1000
value: 12.045
- type: ndcg_at_3
value: 3.956
- type: ndcg_at_5
value: 4.584
- type: precision_at_1
value: 2.9850000000000003
- type: precision_at_10
value: 0.9329999999999999
- type: precision_at_100
value: 0.29
- type: precision_at_1000
value: 0.083
- type: precision_at_3
value: 1.949
- type: precision_at_5
value: 1.567
- type: recall_at_1
value: 2.257
- type: recall_at_10
value: 7.382
- type: recall_at_100
value: 20.689
- type: recall_at_1000
value: 53.586
- type: recall_at_3
value: 4.786
- type: recall_at_5
value: 6.2829999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.691
- type: map_at_10
value: 9.447
- type: map_at_100
value: 10.174
- type: map_at_1000
value: 10.308
- type: map_at_3
value: 8.187999999999999
- type: map_at_5
value: 8.852
- type: mrr_at_1
value: 8.566
- type: mrr_at_10
value: 12.036
- type: mrr_at_100
value: 12.817
- type: mrr_at_1000
value: 12.918
- type: mrr_at_3
value: 10.539
- type: mrr_at_5
value: 11.381
- type: ndcg_at_1
value: 8.566
- type: ndcg_at_10
value: 11.95
- type: ndcg_at_100
value: 15.831000000000001
- type: ndcg_at_1000
value: 19.561
- type: ndcg_at_3
value: 9.467
- type: ndcg_at_5
value: 10.544
- type: precision_at_1
value: 8.566
- type: precision_at_10
value: 2.387
- type: precision_at_100
value: 0.538
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 4.556
- type: precision_at_5
value: 3.5029999999999997
- type: recall_at_1
value: 6.691
- type: recall_at_10
value: 17.375
- type: recall_at_100
value: 34.503
- type: recall_at_1000
value: 61.492000000000004
- type: recall_at_3
value: 10.134
- type: recall_at_5
value: 13.056999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.68
- type: map_at_10
value: 6.776999999999999
- type: map_at_100
value: 7.207
- type: map_at_1000
value: 7.321999999999999
- type: map_at_3
value: 6.007
- type: map_at_5
value: 6.356000000000001
- type: mrr_at_1
value: 5.479
- type: mrr_at_10
value: 8.094999999999999
- type: mrr_at_100
value: 8.622
- type: mrr_at_1000
value: 8.729000000000001
- type: mrr_at_3
value: 7.249
- type: mrr_at_5
value: 7.6770000000000005
- type: ndcg_at_1
value: 5.479
- type: ndcg_at_10
value: 8.474
- type: ndcg_at_100
value: 11.134
- type: ndcg_at_1000
value: 14.759
- type: ndcg_at_3
value: 6.888
- type: ndcg_at_5
value: 7.504
- type: precision_at_1
value: 5.479
- type: precision_at_10
value: 1.575
- type: precision_at_100
value: 0.35000000000000003
- type: precision_at_1000
value: 0.08099999999999999
- type: precision_at_3
value: 3.272
- type: precision_at_5
value: 2.374
- type: recall_at_1
value: 4.68
- type: recall_at_10
value: 12.552
- type: recall_at_100
value: 24.91
- type: recall_at_1000
value: 52.019999999999996
- type: recall_at_3
value: 8.057
- type: recall_at_5
value: 9.629999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.741750000000001
- type: map_at_10
value: 7.103916666666667
- type: map_at_100
value: 7.656499999999998
- type: map_at_1000
value: 7.767583333333332
- type: map_at_3
value: 6.262416666666668
- type: map_at_5
value: 6.693916666666667
- type: mrr_at_1
value: 5.780583333333332
- type: mrr_at_10
value: 8.576333333333332
- type: mrr_at_100
value: 9.17975
- type: mrr_at_1000
value: 9.279083333333334
- type: mrr_at_3
value: 7.608833333333333
- type: mrr_at_5
value: 8.111333333333333
- type: ndcg_at_1
value: 5.780583333333332
- type: ndcg_at_10
value: 8.866166666666668
- type: ndcg_at_100
value: 12.037083333333333
- type: ndcg_at_1000
value: 15.4555
- type: ndcg_at_3
value: 7.179083333333335
- type: ndcg_at_5
value: 7.897166666666666
- type: precision_at_1
value: 5.780583333333332
- type: precision_at_10
value: 1.6935833333333334
- type: precision_at_100
value: 0.3921666666666667
- type: precision_at_1000
value: 0.08391666666666667
- type: precision_at_3
value: 3.425416666666666
- type: precision_at_5
value: 2.5570833333333334
- type: recall_at_1
value: 4.741750000000001
- type: recall_at_10
value: 12.889083333333334
- type: recall_at_100
value: 27.81866666666667
- type: recall_at_1000
value: 53.52316666666667
- type: recall_at_3
value: 8.179333333333332
- type: recall_at_5
value: 10.004083333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.7130000000000005
- type: map_at_10
value: 5.734
- type: map_at_100
value: 6.297999999999999
- type: map_at_1000
value: 6.388000000000001
- type: map_at_3
value: 5.119
- type: map_at_5
value: 5.432
- type: mrr_at_1
value: 4.9079999999999995
- type: mrr_at_10
value: 7.2940000000000005
- type: mrr_at_100
value: 7.8549999999999995
- type: mrr_at_1000
value: 7.95
- type: mrr_at_3
value: 6.621
- type: mrr_at_5
value: 6.950000000000001
- type: ndcg_at_1
value: 4.9079999999999995
- type: ndcg_at_10
value: 7.167999999999999
- type: ndcg_at_100
value: 10.436
- type: ndcg_at_1000
value: 13.370999999999999
- type: ndcg_at_3
value: 5.959
- type: ndcg_at_5
value: 6.481000000000001
- type: precision_at_1
value: 4.9079999999999995
- type: precision_at_10
value: 1.3339999999999999
- type: precision_at_100
value: 0.33899999999999997
- type: precision_at_1000
value: 0.065
- type: precision_at_3
value: 2.965
- type: precision_at_5
value: 2.117
- type: recall_at_1
value: 3.7130000000000005
- type: recall_at_10
value: 10.156
- type: recall_at_100
value: 25.955000000000002
- type: recall_at_1000
value: 48.891
- type: recall_at_3
value: 6.795
- type: recall_at_5
value: 8.187999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.114
- type: map_at_10
value: 3.4290000000000003
- type: map_at_100
value: 3.789
- type: map_at_1000
value: 3.878
- type: map_at_3
value: 2.9139999999999997
- type: map_at_5
value: 3.148
- type: mrr_at_1
value: 2.65
- type: mrr_at_10
value: 4.252000000000001
- type: mrr_at_100
value: 4.689
- type: mrr_at_1000
value: 4.782
- type: mrr_at_3
value: 3.671
- type: mrr_at_5
value: 3.9370000000000003
- type: ndcg_at_1
value: 2.65
- type: ndcg_at_10
value: 4.47
- type: ndcg_at_100
value: 6.654
- type: ndcg_at_1000
value: 9.713
- type: ndcg_at_3
value: 3.424
- type: ndcg_at_5
value: 3.794
- type: precision_at_1
value: 2.65
- type: precision_at_10
value: 0.9119999999999999
- type: precision_at_100
value: 0.248
- type: precision_at_1000
value: 0.063
- type: precision_at_3
value: 1.7209999999999999
- type: precision_at_5
value: 1.287
- type: recall_at_1
value: 2.114
- type: recall_at_10
value: 6.927
- type: recall_at_100
value: 17.26
- type: recall_at_1000
value: 40.672999999999995
- type: recall_at_3
value: 3.8859999999999997
- type: recall_at_5
value: 4.861
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.055
- type: map_at_10
value: 7.704999999999999
- type: map_at_100
value: 8.169
- type: map_at_1000
value: 8.257
- type: map_at_3
value: 7.033
- type: map_at_5
value: 7.4079999999999995
- type: mrr_at_1
value: 6.81
- type: mrr_at_10
value: 8.955
- type: mrr_at_100
value: 9.497
- type: mrr_at_1000
value: 9.583
- type: mrr_at_3
value: 8.116
- type: mrr_at_5
value: 8.526
- type: ndcg_at_1
value: 6.81
- type: ndcg_at_10
value: 9.113
- type: ndcg_at_100
value: 11.884
- type: ndcg_at_1000
value: 14.762
- type: ndcg_at_3
value: 7.675999999999999
- type: ndcg_at_5
value: 8.325000000000001
- type: precision_at_1
value: 6.81
- type: precision_at_10
value: 1.558
- type: precision_at_100
value: 0.34299999999999997
- type: precision_at_1000
value: 0.068
- type: precision_at_3
value: 3.2960000000000003
- type: precision_at_5
value: 2.388
- type: recall_at_1
value: 6.055
- type: recall_at_10
value: 12.219
- type: recall_at_100
value: 25.304
- type: recall_at_1000
value: 47.204
- type: recall_at_3
value: 8.387
- type: recall_at_5
value: 9.991
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.043
- type: map_at_10
value: 7.394
- type: map_at_100
value: 8.096
- type: map_at_1000
value: 8.243
- type: map_at_3
value: 6.300999999999999
- type: map_at_5
value: 6.7780000000000005
- type: mrr_at_1
value: 6.126
- type: mrr_at_10
value: 9.308
- type: mrr_at_100
value: 10.091
- type: mrr_at_1000
value: 10.206
- type: mrr_at_3
value: 7.938000000000001
- type: mrr_at_5
value: 8.64
- type: ndcg_at_1
value: 6.126
- type: ndcg_at_10
value: 9.474
- type: ndcg_at_100
value: 13.238
- type: ndcg_at_1000
value: 17.366
- type: ndcg_at_3
value: 7.3260000000000005
- type: ndcg_at_5
value: 8.167
- type: precision_at_1
value: 6.126
- type: precision_at_10
value: 1.9959999999999998
- type: precision_at_100
value: 0.494
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 3.557
- type: precision_at_5
value: 2.9250000000000003
- type: recall_at_1
value: 5.043
- type: recall_at_10
value: 13.812
- type: recall_at_100
value: 31.375999999999998
- type: recall_at_1000
value: 61.309999999999995
- type: recall_at_3
value: 7.8020000000000005
- type: recall_at_5
value: 9.725999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.771
- type: map_at_10
value: 5.152
- type: map_at_100
value: 5.584
- type: map_at_1000
value: 5.666
- type: map_at_3
value: 4.664
- type: map_at_5
value: 4.941
- type: mrr_at_1
value: 4.251
- type: mrr_at_10
value: 5.867
- type: mrr_at_100
value: 6.345000000000001
- type: mrr_at_1000
value: 6.432
- type: mrr_at_3
value: 5.36
- type: mrr_at_5
value: 5.656
- type: ndcg_at_1
value: 4.251
- type: ndcg_at_10
value: 6.16
- type: ndcg_at_100
value: 8.895
- type: ndcg_at_1000
value: 11.631
- type: ndcg_at_3
value: 5.176
- type: ndcg_at_5
value: 5.633
- type: precision_at_1
value: 4.251
- type: precision_at_10
value: 0.98
- type: precision_at_100
value: 0.259
- type: precision_at_1000
value: 0.053
- type: precision_at_3
value: 2.2800000000000002
- type: precision_at_5
value: 1.627
- type: recall_at_1
value: 3.771
- type: recall_at_10
value: 8.731
- type: recall_at_100
value: 22.517
- type: recall_at_1000
value: 44.183
- type: recall_at_3
value: 5.866
- type: recall_at_5
value: 7.066999999999999
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.543
- type: map_at_10
value: 1.027
- type: map_at_100
value: 1.228
- type: map_at_1000
value: 1.266
- type: map_at_3
value: 0.756
- type: map_at_5
value: 0.877
- type: mrr_at_1
value: 1.3679999999999999
- type: mrr_at_10
value: 2.474
- type: mrr_at_100
value: 2.8369999999999997
- type: mrr_at_1000
value: 2.894
- type: mrr_at_3
value: 1.8780000000000001
- type: mrr_at_5
value: 2.1319999999999997
- type: ndcg_at_1
value: 1.3679999999999999
- type: ndcg_at_10
value: 1.791
- type: ndcg_at_100
value: 3.06
- type: ndcg_at_1000
value: 4.501
- type: ndcg_at_3
value: 1.16
- type: ndcg_at_5
value: 1.3419999999999999
- type: precision_at_1
value: 1.3679999999999999
- type: precision_at_10
value: 0.697
- type: precision_at_100
value: 0.193
- type: precision_at_1000
value: 0.045
- type: precision_at_3
value: 0.9339999999999999
- type: precision_at_5
value: 0.808
- type: recall_at_1
value: 0.543
- type: recall_at_10
value: 2.5149999999999997
- type: recall_at_100
value: 7.356999999999999
- type: recall_at_1000
value: 16.233
- type: recall_at_3
value: 1.018
- type: recall_at_5
value: 1.5150000000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 3.7289999999999996
- type: map_at_10
value: 5.524
- type: map_at_100
value: 5.984
- type: map_at_1000
value: 6.087
- type: map_at_3
value: 4.854
- type: map_at_5
value: 5.2299999999999995
- type: mrr_at_1
value: 6.177
- type: mrr_at_10
value: 8.541
- type: mrr_at_100
value: 9.073
- type: mrr_at_1000
value: 9.161
- type: mrr_at_3
value: 7.71
- type: mrr_at_5
value: 8.148
- type: ndcg_at_1
value: 6.177
- type: ndcg_at_10
value: 7.217999999999999
- type: ndcg_at_100
value: 9.927
- type: ndcg_at_1000
value: 13.062000000000001
- type: ndcg_at_3
value: 6.0569999999999995
- type: ndcg_at_5
value: 6.544999999999999
- type: precision_at_1
value: 6.177
- type: precision_at_10
value: 1.6729999999999998
- type: precision_at_100
value: 0.38999999999999996
- type: precision_at_1000
value: 0.082
- type: precision_at_3
value: 3.5090000000000003
- type: precision_at_5
value: 2.596
- type: recall_at_1
value: 3.7289999999999996
- type: recall_at_10
value: 9.501
- type: recall_at_100
value: 21.444
- type: recall_at_1000
value: 43.891999999999996
- type: recall_at_3
value: 6.053
- type: recall_at_5
value: 7.531000000000001
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 58.123872519543
- type: cos_sim_ap
value: 61.86046509726734
- type: cos_sim_f1
value: 68.18181818181817
- type: cos_sim_precision
value: 52.4198617221873
- type: cos_sim_recall
value: 97.49824643441664
- type: dot_accuracy
value: 58.123872519543
- type: dot_ap
value: 61.860555259802986
- type: dot_f1
value: 68.18181818181817
- type: dot_precision
value: 52.4198617221873
- type: dot_recall
value: 97.49824643441664
- type: euclidean_accuracy
value: 58.123872519543
- type: euclidean_ap
value: 61.87698627731538
- type: euclidean_f1
value: 68.18181818181817
- type: euclidean_precision
value: 52.4198617221873
- type: euclidean_recall
value: 97.49824643441664
- type: manhattan_accuracy
value: 58.123872519543
- type: manhattan_ap
value: 61.99468883207791
- type: manhattan_f1
value: 68.33675564681727
- type: manhattan_precision
value: 52.671562420866046
- type: manhattan_recall
value: 97.26443768996961
- type: max_accuracy
value: 58.123872519543
- type: max_ap
value: 61.99468883207791
- type: max_f1
value: 68.33675564681727
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 6.428000000000001
- type: map_at_10
value: 8.883000000000001
- type: map_at_100
value: 9.549000000000001
- type: map_at_1000
value: 9.665
- type: map_at_3
value: 8.061
- type: map_at_5
value: 8.475000000000001
- type: mrr_at_1
value: 6.428000000000001
- type: mrr_at_10
value: 8.896999999999998
- type: mrr_at_100
value: 9.557
- type: mrr_at_1000
value: 9.674000000000001
- type: mrr_at_3
value: 8.061
- type: mrr_at_5
value: 8.488
- type: ndcg_at_1
value: 6.428000000000001
- type: ndcg_at_10
value: 10.382
- type: ndcg_at_100
value: 14.235999999999999
- type: ndcg_at_1000
value: 18.04
- type: ndcg_at_3
value: 8.613999999999999
- type: ndcg_at_5
value: 9.372
- type: precision_at_1
value: 6.428000000000001
- type: precision_at_10
value: 1.528
- type: precision_at_100
value: 0.349
- type: precision_at_1000
value: 0.067
- type: precision_at_3
value: 3.4070000000000005
- type: precision_at_5
value: 2.424
- type: recall_at_1
value: 6.428000000000001
- type: recall_at_10
value: 15.226999999999999
- type: recall_at_100
value: 34.694
- type: recall_at_1000
value: 66.07
- type: recall_at_3
value: 10.221
- type: recall_at_5
value: 12.065
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.541
- type: map_at_10
value: 1.1560000000000001
- type: map_at_100
value: 1.508
- type: map_at_1000
value: 1.598
- type: map_at_3
value: 0.918
- type: map_at_5
value: 0.992
- type: mrr_at_1
value: 9.5
- type: mrr_at_10
value: 13.446
- type: mrr_at_100
value: 13.935
- type: mrr_at_1000
value: 14.008999999999999
- type: mrr_at_3
value: 12.083
- type: mrr_at_5
value: 12.733
- type: ndcg_at_1
value: 5.75
- type: ndcg_at_10
value: 3.9210000000000003
- type: ndcg_at_100
value: 3.975
- type: ndcg_at_1000
value: 5.634
- type: ndcg_at_3
value: 4.87
- type: ndcg_at_5
value: 4.259
- type: precision_at_1
value: 9.5
- type: precision_at_10
value: 3.9
- type: precision_at_100
value: 1.015
- type: precision_at_1000
value: 0.297
- type: precision_at_3
value: 6.75
- type: precision_at_5
value: 5.25
- type: recall_at_1
value: 0.541
- type: recall_at_10
value: 2.228
- type: recall_at_100
value: 4.9430000000000005
- type: recall_at_1000
value: 11.661000000000001
- type: recall_at_3
value: 1.264
- type: recall_at_5
value: 1.4869999999999999
- task:
type: Classification
dataset:
type: DDSC/dkhate
name: MTEB DKHateClassification
config: default
split: test
revision: 59d12749a3c91a186063c7d729ec392fda94681c
metrics:
- type: accuracy
value: 69.96960486322187
- type: ap
value: 91.23131906690253
- type: f1
value: 57.11872970138122
- task:
type: Classification
dataset:
type: AI-Sweden/SuperLim
name: MTEB DalajClassification
config: default
split: test
revision: 7ebf0b4caa7b2ae39698a889de782c09e6f5ee56
metrics:
- type: accuracy
value: 49.75225225225225
- type: ap
value: 49.88223192425368
- type: f1
value: 49.55059044107012
- task:
type: Classification
dataset:
type: danish_political_comments
name: MTEB DanishPoliticalCommentsClassification
config: default
split: train
revision: edbb03726c04a0efab14fc8c3b8b79e4d420e5a1
metrics:
- type: accuracy
value: 37.58534554537886
- type: f1
value: 33.99440115952713
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 0.608
- type: map_at_10
value: 0.882
- type: map_at_100
value: 0.962
- type: map_at_1000
value: 1.028
- type: map_at_3
value: 0.749
- type: map_at_5
value: 0.8240000000000001
- type: mrr_at_1
value: 2.0500000000000003
- type: mrr_at_10
value: 2.796
- type: mrr_at_100
value: 2.983
- type: mrr_at_1000
value: 3.09
- type: mrr_at_3
value: 2.483
- type: mrr_at_5
value: 2.661
- type: ndcg_at_1
value: 2.0500000000000003
- type: ndcg_at_10
value: 1.435
- type: ndcg_at_100
value: 1.991
- type: ndcg_at_1000
value: 4.961
- type: ndcg_at_3
value: 1.428
- type: ndcg_at_5
value: 1.369
- type: precision_at_1
value: 2.0500000000000003
- type: precision_at_10
value: 0.5349999999999999
- type: precision_at_100
value: 0.127
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 1.05
- type: precision_at_5
value: 0.84
- type: recall_at_1
value: 0.608
- type: recall_at_10
value: 1.54
- type: recall_at_100
value: 3.5069999999999997
- type: recall_at_1000
value: 20.531
- type: recall_at_3
value: 0.901
- type: recall_at_5
value: 1.168
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 3.1
- type: map_at_10
value: 4.016
- type: map_at_100
value: 4.455
- type: map_at_1000
value: 4.579
- type: map_at_3
value: 3.567
- type: map_at_5
value: 3.8019999999999996
- type: mrr_at_1
value: 3.1
- type: mrr_at_10
value: 4.016
- type: mrr_at_100
value: 4.455
- type: mrr_at_1000
value: 4.579
- type: mrr_at_3
value: 3.567
- type: mrr_at_5
value: 3.8019999999999996
- type: ndcg_at_1
value: 3.1
- type: ndcg_at_10
value: 4.684
- type: ndcg_at_100
value: 7.284
- type: ndcg_at_1000
value: 11.689
- type: ndcg_at_3
value: 3.7289999999999996
- type: ndcg_at_5
value: 4.146
- type: precision_at_1
value: 3.1
- type: precision_at_10
value: 0.69
- type: precision_at_100
value: 0.202
- type: precision_at_1000
value: 0.056999999999999995
- type: precision_at_3
value: 1.4000000000000001
- type: precision_at_5
value: 1.04
- type: recall_at_1
value: 3.1
- type: recall_at_10
value: 6.9
- type: recall_at_100
value: 20.200000000000003
- type: recall_at_1000
value: 57.3
- type: recall_at_3
value: 4.2
- type: recall_at_5
value: 5.2
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 38.285000000000004
- type: f1
value: 35.35979931355028
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.9249999999999999
- type: map_at_10
value: 1.311
- type: map_at_100
value: 1.363
- type: map_at_1000
value: 1.376
- type: map_at_3
value: 1.145
- type: map_at_5
value: 1.233
- type: mrr_at_1
value: 0.975
- type: mrr_at_10
value: 1.371
- type: mrr_at_100
value: 1.426
- type: mrr_at_1000
value: 1.439
- type: mrr_at_3
value: 1.195
- type: mrr_at_5
value: 1.286
- type: ndcg_at_1
value: 0.975
- type: ndcg_at_10
value: 1.5859999999999999
- type: ndcg_at_100
value: 1.8800000000000001
- type: ndcg_at_1000
value: 2.313
- type: ndcg_at_3
value: 1.229
- type: ndcg_at_5
value: 1.388
- type: precision_at_1
value: 0.975
- type: precision_at_10
value: 0.254
- type: precision_at_100
value: 0.041
- type: precision_at_1000
value: 0.008
- type: precision_at_3
value: 0.49
- type: precision_at_5
value: 0.375
- type: recall_at_1
value: 0.9249999999999999
- type: recall_at_10
value: 2.4250000000000003
- type: recall_at_100
value: 3.866
- type: recall_at_1000
value: 7.401000000000001
- type: recall_at_3
value: 1.4200000000000002
- type: recall_at_5
value: 1.81
- task:
type: Retrieval
dataset:
type: fiqa-pl
name: MTEB FiQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.959
- type: map_at_10
value: 1.952
- type: map_at_100
value: 2.281
- type: map_at_1000
value: 2.393
- type: map_at_3
value: 1.703
- type: map_at_5
value: 1.8319999999999999
- type: mrr_at_1
value: 2.469
- type: mrr_at_10
value: 4.547
- type: mrr_at_100
value: 5.021
- type: mrr_at_1000
value: 5.1339999999999995
- type: mrr_at_3
value: 3.884
- type: mrr_at_5
value: 4.223
- type: ndcg_at_1
value: 2.469
- type: ndcg_at_10
value: 3.098
- type: ndcg_at_100
value: 5.177
- type: ndcg_at_1000
value: 8.889
- type: ndcg_at_3
value: 2.7119999999999997
- type: ndcg_at_5
value: 2.8000000000000003
- type: precision_at_1
value: 2.469
- type: precision_at_10
value: 1.065
- type: precision_at_100
value: 0.321
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 2.109
- type: precision_at_5
value: 1.574
- type: recall_at_1
value: 0.959
- type: recall_at_10
value: 4.075
- type: recall_at_100
value: 12.487
- type: recall_at_1000
value: 36.854
- type: recall_at_3
value: 2.632
- type: recall_at_5
value: 3.231
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.032
- type: map_at_10
value: 1.8849999999999998
- type: map_at_100
value: 2.167
- type: map_at_1000
value: 2.266
- type: map_at_3
value: 1.609
- type: map_at_5
value: 1.7680000000000002
- type: mrr_at_1
value: 2.6229999999999998
- type: mrr_at_10
value: 4.479
- type: mrr_at_100
value: 4.92
- type: mrr_at_1000
value: 5.029999999999999
- type: mrr_at_3
value: 3.7289999999999996
- type: mrr_at_5
value: 4.138
- type: ndcg_at_1
value: 2.6229999999999998
- type: ndcg_at_10
value: 3.005
- type: ndcg_at_100
value: 5.01
- type: ndcg_at_1000
value: 8.312
- type: ndcg_at_3
value: 2.548
- type: ndcg_at_5
value: 2.735
- type: precision_at_1
value: 2.6229999999999998
- type: precision_at_10
value: 1.049
- type: precision_at_100
value: 0.31
- type: precision_at_1000
value: 0.089
- type: precision_at_3
value: 1.955
- type: precision_at_5
value: 1.574
- type: recall_at_1
value: 1.032
- type: recall_at_10
value: 3.888
- type: recall_at_100
value: 12.414
- type: recall_at_1000
value: 33.823
- type: recall_at_3
value: 2.37
- type: recall_at_5
value: 3.077
- task:
type: Retrieval
dataset:
type: jinaai/ger_da_lir
name: MTEB GerDaLIR
config: default
split: test
revision: 0bb47f1d73827e96964edb84dfe552f62f4fd5eb
metrics:
- type: map_at_1
value: 0.542
- type: map_at_10
value: 0.8130000000000001
- type: map_at_100
value: 0.898
- type: map_at_1000
value: 0.9209999999999999
- type: map_at_3
value: 0.709
- type: map_at_5
value: 0.764
- type: mrr_at_1
value: 0.594
- type: mrr_at_10
value: 0.8880000000000001
- type: mrr_at_100
value: 0.9820000000000001
- type: mrr_at_1000
value: 1.008
- type: mrr_at_3
value: 0.774
- type: mrr_at_5
value: 0.832
- type: ndcg_at_1
value: 0.594
- type: ndcg_at_10
value: 1.0030000000000001
- type: ndcg_at_100
value: 1.537
- type: ndcg_at_1000
value: 2.4330000000000003
- type: ndcg_at_3
value: 0.782
- type: ndcg_at_5
value: 0.882
- type: precision_at_1
value: 0.594
- type: precision_at_10
value: 0.16999999999999998
- type: precision_at_100
value: 0.048
- type: precision_at_1000
value: 0.013
- type: precision_at_3
value: 0.33899999999999997
- type: precision_at_5
value: 0.255
- type: recall_at_1
value: 0.542
- type: recall_at_10
value: 1.533
- type: recall_at_100
value: 4.204
- type: recall_at_1000
value: 11.574
- type: recall_at_3
value: 0.932
- type: recall_at_5
value: 1.172
- task:
type: Retrieval
dataset:
type: deepset/germandpr
name: MTEB GermanDPR
config: default
split: test
revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
metrics:
- type: map_at_1
value: 25.561
- type: map_at_10
value: 38.873000000000005
- type: map_at_100
value: 40.004
- type: map_at_1000
value: 40.03
- type: map_at_3
value: 34.585
- type: map_at_5
value: 36.980000000000004
- type: mrr_at_1
value: 25.463
- type: mrr_at_10
value: 38.792
- type: mrr_at_100
value: 39.922000000000004
- type: mrr_at_1000
value: 39.949
- type: mrr_at_3
value: 34.504000000000005
- type: mrr_at_5
value: 36.899
- type: ndcg_at_1
value: 25.561
- type: ndcg_at_10
value: 46.477000000000004
- type: ndcg_at_100
value: 51.751999999999995
- type: ndcg_at_1000
value: 52.366
- type: ndcg_at_3
value: 37.645
- type: ndcg_at_5
value: 41.953
- type: precision_at_1
value: 25.561
- type: precision_at_10
value: 7.083
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.512
- type: precision_at_5
value: 11.395
- type: recall_at_1
value: 25.561
- type: recall_at_10
value: 70.829
- type: recall_at_100
value: 94.92699999999999
- type: recall_at_1000
value: 99.61
- type: recall_at_3
value: 46.537
- type: recall_at_5
value: 56.976000000000006
- task:
type: Retrieval
dataset:
type: mteb/germanquad-retrieval
name: MTEB GermanQuAD-Retrieval
config: default
split: test
revision: f5c87ae5a2e7a5106606314eef45255f03151bb3
metrics:
- type: map_at_1
value: 53.539
- type: map_at_10
value: 65.144
- type: map_at_100
value: 65.627
- type: map_at_1000
value: 65.63900000000001
- type: map_at_3
value: 62.598
- type: map_at_5
value: 64.302
- type: mrr_at_1
value: 53.539
- type: mrr_at_10
value: 65.144
- type: mrr_at_100
value: 65.627
- type: mrr_at_1000
value: 65.63900000000001
- type: mrr_at_3
value: 62.598
- type: mrr_at_5
value: 64.302
- type: ndcg_at_1
value: 53.539
- type: ndcg_at_10
value: 70.602
- type: ndcg_at_100
value: 72.886
- type: ndcg_at_1000
value: 73.14500000000001
- type: ndcg_at_3
value: 65.52900000000001
- type: ndcg_at_5
value: 68.596
- type: precision_at_1
value: 53.539
- type: precision_at_10
value: 8.757
- type: precision_at_100
value: 0.9809999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.667
- type: precision_at_5
value: 16.289
- type: recall_at_1
value: 53.539
- type: recall_at_10
value: 87.568
- type: recall_at_100
value: 98.09400000000001
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 74.002
- type: recall_at_5
value: 81.443
- task:
type: STS
dataset:
type: jinaai/german-STSbenchmark
name: MTEB GermanSTSBenchmark
config: default
split: test
revision: e36907544d44c3a247898ed81540310442329e20
metrics:
- type: cos_sim_pearson
value: 68.82052535790737
- type: cos_sim_spearman
value: 67.9356892072251
- type: euclidean_pearson
value: 67.2308663006278
- type: euclidean_spearman
value: 67.93572522920142
- type: manhattan_pearson
value: 67.23568952733595
- type: manhattan_spearman
value: 67.91660489262797
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.813
- type: map_at_10
value: 9.49
- type: map_at_100
value: 9.959
- type: map_at_1000
value: 10.024
- type: map_at_3
value: 8.618
- type: map_at_5
value: 9.084
- type: mrr_at_1
value: 13.626
- type: mrr_at_10
value: 17.818
- type: mrr_at_100
value: 18.412
- type: mrr_at_1000
value: 18.482000000000003
- type: mrr_at_3
value: 16.506999999999998
- type: mrr_at_5
value: 17.219
- type: ndcg_at_1
value: 13.626
- type: ndcg_at_10
value: 12.959999999999999
- type: ndcg_at_100
value: 15.562999999999999
- type: ndcg_at_1000
value: 17.571
- type: ndcg_at_3
value: 10.995000000000001
- type: ndcg_at_5
value: 11.908000000000001
- type: precision_at_1
value: 13.626
- type: precision_at_10
value: 2.995
- type: precision_at_100
value: 0.51
- type: precision_at_1000
value: 0.078
- type: precision_at_3
value: 7.000000000000001
- type: precision_at_5
value: 4.926
- type: recall_at_1
value: 6.813
- type: recall_at_10
value: 14.976
- type: recall_at_100
value: 25.517
- type: recall_at_1000
value: 39.095
- type: recall_at_3
value: 10.5
- type: recall_at_5
value: 12.316
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type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 38.01462100808003
- type: f1
value: 26.680357453754215
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
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value: 55.7508
- type: ap
value: 53.28158993124153
- type: f1
value: 55.34571379744637
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 69.58724202626641
- type: ap
value: 30.04577466931377
- type: f1
value: 62.46921898313143
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
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value: 48.80585861169271
- type: cos_sim_spearman
value: 50.11025991147549
- type: euclidean_pearson
value: 50.055425341198934
- type: euclidean_spearman
value: 50.11024862622995
- type: manhattan_pearson
value: 50.029980024931064
- type: manhattan_spearman
value: 50.074388245963384
- task:
type: Classification
dataset:
type: DDSC/lcc
name: MTEB LccSentimentClassification
config: default
split: test
revision: de7ba3406ee55ea2cc52a0a41408fa6aede6d3c6
metrics:
- type: accuracy
value: 54.266666666666666
- type: f1
value: 52.181931818742875
- task:
type: Reranking
dataset:
type: jinaai/miracl
name: MTEB MIRACL
config: default
split: test
revision: d28a029f35c4ff7f616df47b0edf54e6882395e6
metrics:
- type: map
value: 51.40745004398599
- type: mrr
value: 56.71940267335004
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
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value: 5.831060174627054
- type: mrr
value: 4.019047619047618
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
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value: 5.826
- type: map_at_10
value: 8.956999999999999
- type: map_at_100
value: 9.746
- type: map_at_1000
value: 9.873999999999999
- type: map_at_3
value: 7.757
- type: map_at_5
value: 8.373
- type: mrr_at_1
value: 6.046
- type: mrr_at_10
value: 9.251
- type: mrr_at_100
value: 10.044
- type: mrr_at_1000
value: 10.167
- type: mrr_at_3
value: 8.028
- type: mrr_at_5
value: 8.66
- type: ndcg_at_1
value: 6.046
- type: ndcg_at_10
value: 10.998
- type: ndcg_at_100
value: 15.568999999999999
- type: ndcg_at_1000
value: 19.453
- type: ndcg_at_3
value: 8.468
- type: ndcg_at_5
value: 9.582
- type: precision_at_1
value: 6.046
- type: precision_at_10
value: 1.807
- type: precision_at_100
value: 0.42500000000000004
- type: precision_at_1000
value: 0.076
- type: precision_at_3
value: 3.572
- type: precision_at_5
value: 2.702
- type: recall_at_1
value: 5.826
- type: recall_at_10
value: 17.291
- type: recall_at_100
value: 40.037
- type: recall_at_1000
value: 71.351
- type: recall_at_3
value: 10.269
- type: recall_at_5
value: 12.950000000000001
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
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value: 1.203
- type: map_at_10
value: 2.27
- type: map_at_100
value: 2.5860000000000003
- type: map_at_1000
value: 2.661
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- type: map_at_5
value: 2.037
- type: mrr_at_1
value: 1.232
- type: mrr_at_10
value: 2.338
- type: mrr_at_100
value: 2.665
- type: mrr_at_1000
value: 2.7390000000000003
- type: mrr_at_3
value: 1.87
- type: mrr_at_5
value: 2.1
- type: ndcg_at_1
value: 1.232
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value: 3.005
- type: ndcg_at_100
value: 4.936
- type: ndcg_at_1000
value: 7.441000000000001
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value: 2.036
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value: 2.435
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value: 1.232
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value: 0.549
- type: precision_at_100
value: 0.158
- type: precision_at_1000
value: 0.038
- type: precision_at_3
value: 0.903
- type: precision_at_5
value: 0.739
- type: recall_at_1
value: 1.203
- type: recall_at_10
value: 5.332
- type: recall_at_100
value: 15.164
- type: recall_at_1000
value: 35.831
- type: recall_at_3
value: 2.622
- type: recall_at_5
value: 3.572
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type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 89.92476060191518
- type: f1
value: 89.30222882069823
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 89.54353338968724
- type: f1
value: 88.23043644828002
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 90.62374916611076
- type: f1
value: 89.68544977510335
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.18540557469466
- type: f1
value: 85.7362674669331
- task:
type: Classification
dataset:
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name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 89.41556113302258
- type: f1
value: 89.04934651990581
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 85.89511754068715
- type: f1
value: 85.57630467968119
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 70.85043319653442
- type: f1
value: 46.0794069318026
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 73.43195266272188
- type: f1
value: 48.08015719781981
- task:
type: Classification
dataset:
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config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 73.8425617078052
- type: f1
value: 49.37915156189611
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 66.75227059191982
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value: 43.4642946741452
- task:
type: Classification
dataset:
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split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 69.13589100035855
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type: Classification
dataset:
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config: th
split: test
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metrics:
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value: 68.47016274864377
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value: 46.197113305277796
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type: Classification
dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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metrics:
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- task:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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config: cy
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metrics:
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dataset:
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metrics:
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value: 58.836302050830966
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type: Classification
dataset:
type: mteb/amazon_massive_intent
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config: de
split: test
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metrics:
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dataset:
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metrics:
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- task:
type: Classification
dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
type: Classification
dataset:
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metrics:
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- task:
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dataset:
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metrics:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 59.655860488861926
- task:
type: Classification
dataset:
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config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 58.55077336919974
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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split: test
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metrics:
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- task:
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dataset:
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split: test
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metrics:
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dataset:
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split: test
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metrics:
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dataset:
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config: id
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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metrics:
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dataset:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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dataset:
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config: kn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: ko
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: lv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 56.240753194351036
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- task:
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dataset:
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split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: ms
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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- task:
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dataset:
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config: my
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 59.3981170141224
- type: f1
value: 56.31810441546048
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
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config: nb
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 59.89576328177538
- type: f1
value: 57.35130066022407
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (nl)
config: nl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
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value: 64.55951580363148
- type: f1
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- task:
type: Classification
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metrics:
- type: accuracy
value: 66.51647612642904
- type: f1
value: 65.66061210324213
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.88365837256221
- type: f1
value: 67.56956454874091
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.29858776059179
- type: f1
value: 62.76318771484755
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 2.9000000000000004
- type: map_at_10
value: 3.5360000000000005
- type: map_at_100
value: 3.703
- type: map_at_1000
value: 3.734
- type: map_at_3
value: 3.167
- type: map_at_5
value: 3.322
- type: mrr_at_1
value: 2.9000000000000004
- type: mrr_at_10
value: 3.5360000000000005
- type: mrr_at_100
value: 3.703
- type: mrr_at_1000
value: 3.734
- type: mrr_at_3
value: 3.167
- type: mrr_at_5
value: 3.322
- type: ndcg_at_1
value: 2.9000000000000004
- type: ndcg_at_10
value: 4.079
- type: ndcg_at_100
value: 5.101
- type: ndcg_at_1000
value: 6.295000000000001
- type: ndcg_at_3
value: 3.276
- type: ndcg_at_5
value: 3.56
- type: precision_at_1
value: 2.9000000000000004
- type: precision_at_10
value: 0.59
- type: precision_at_100
value: 0.11199999999999999
- type: precision_at_1000
value: 0.022000000000000002
- type: precision_at_3
value: 1.2
- type: precision_at_5
value: 0.86
- type: recall_at_1
value: 2.9000000000000004
- type: recall_at_10
value: 5.8999999999999995
- type: recall_at_100
value: 11.200000000000001
- type: recall_at_1000
value: 21.5
- type: recall_at_3
value: 3.5999999999999996
- type: recall_at_5
value: 4.3
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 19.061819627060558
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 19.79520446745267
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 26.881162218991285
- type: mrr
value: 27.23201335662217
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 57.69
- type: f1
value: 57.370451927892695
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.443
- type: map_at_10
value: 1.189
- type: map_at_100
value: 2.221
- type: map_at_1000
value: 3.034
- type: map_at_3
value: 0.683
- type: map_at_5
value: 0.882
- type: mrr_at_1
value: 4.334
- type: mrr_at_10
value: 10.908
- type: mrr_at_100
value: 12.536
- type: mrr_at_1000
value: 12.642000000000001
- type: mrr_at_3
value: 7.481999999999999
- type: mrr_at_5
value: 9.324
- type: ndcg_at_1
value: 3.7150000000000003
- type: ndcg_at_10
value: 5.591
- type: ndcg_at_100
value: 9.522
- type: ndcg_at_1000
value: 19.705000000000002
- type: ndcg_at_3
value: 4.292
- type: ndcg_at_5
value: 5.038
- type: precision_at_1
value: 4.334
- type: precision_at_10
value: 5.077
- type: precision_at_100
value: 3.2910000000000004
- type: precision_at_1000
value: 1.568
- type: precision_at_3
value: 4.644
- type: precision_at_5
value: 5.139
- type: recall_at_1
value: 0.443
- type: recall_at_10
value: 3.3520000000000003
- type: recall_at_100
value: 15.515
- type: recall_at_1000
value: 50.505
- type: recall_at_3
value: 0.931
- type: recall_at_5
value: 1.698
- task:
type: Retrieval
dataset:
type: nfcorpus-pl
name: MTEB NFCorpus-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.307
- type: map_at_10
value: 0.835
- type: map_at_100
value: 1.503
- type: map_at_1000
value: 2.263
- type: map_at_3
value: 0.503
- type: map_at_5
value: 0.567
- type: mrr_at_1
value: 4.025
- type: mrr_at_10
value: 9.731
- type: mrr_at_100
value: 11.229
- type: mrr_at_1000
value: 11.34
- type: mrr_at_3
value: 6.811
- type: mrr_at_5
value: 8.126999999999999
- type: ndcg_at_1
value: 3.56
- type: ndcg_at_10
value: 4.596
- type: ndcg_at_100
value: 7.567
- type: ndcg_at_1000
value: 17.76
- type: ndcg_at_3
value: 3.52
- type: ndcg_at_5
value: 3.823
- type: precision_at_1
value: 4.025
- type: precision_at_10
value: 4.334
- type: precision_at_100
value: 2.842
- type: precision_at_1000
value: 1.506
- type: precision_at_3
value: 3.818
- type: precision_at_5
value: 4.149
- type: recall_at_1
value: 0.307
- type: recall_at_10
value: 2.543
- type: recall_at_100
value: 12.152000000000001
- type: recall_at_1000
value: 46.878
- type: recall_at_3
value: 0.755
- type: recall_at_5
value: 0.975
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.439
- type: map_at_10
value: 0.6839999999999999
- type: map_at_100
value: 0.769
- type: map_at_1000
value: 0.79
- type: map_at_3
value: 0.584
- type: map_at_5
value: 0.621
- type: mrr_at_1
value: 0.5499999999999999
- type: mrr_at_10
value: 0.819
- type: mrr_at_100
value: 0.9169999999999999
- type: mrr_at_1000
value: 0.9400000000000001
- type: mrr_at_3
value: 0.705
- type: mrr_at_5
value: 0.75
- type: ndcg_at_1
value: 0.5499999999999999
- type: ndcg_at_10
value: 0.886
- type: ndcg_at_100
value: 1.422
- type: ndcg_at_1000
value: 2.2079999999999997
- type: ndcg_at_3
value: 0.6629999999999999
- type: ndcg_at_5
value: 0.735
- type: precision_at_1
value: 0.5499999999999999
- type: precision_at_10
value: 0.16199999999999998
- type: precision_at_100
value: 0.048
- type: precision_at_1000
value: 0.012
- type: precision_at_3
value: 0.309
- type: precision_at_5
value: 0.22599999999999998
- type: recall_at_1
value: 0.439
- type: recall_at_10
value: 1.405
- type: recall_at_100
value: 4.051
- type: recall_at_1000
value: 10.487
- type: recall_at_3
value: 0.787
- type: recall_at_5
value: 0.9560000000000001
- task:
type: Retrieval
dataset:
type: narrativeqa
name: MTEB NarrativeQARetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.93
- type: map_at_10
value: 7.349
- type: map_at_100
value: 8.011
- type: map_at_1000
value: 8.351
- type: map_at_3
value: 6.787
- type: map_at_5
value: 7.02
- type: mrr_at_1
value: 5.93
- type: mrr_at_10
value: 7.349
- type: mrr_at_100
value: 8.011
- type: mrr_at_1000
value: 8.351
- type: mrr_at_3
value: 6.787
- type: mrr_at_5
value: 7.02
- type: ndcg_at_1
value: 5.93
- type: ndcg_at_10
value: 8.291
- type: ndcg_at_100
value: 12.833
- type: ndcg_at_1000
value: 21.253
- type: ndcg_at_3
value: 7.072000000000001
- type: ndcg_at_5
value: 7.495
- type: precision_at_1
value: 5.93
- type: precision_at_10
value: 1.1400000000000001
- type: precision_at_100
value: 0.359
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 2.633
- type: precision_at_5
value: 1.786
- type: recall_at_1
value: 5.93
- type: recall_at_10
value: 11.395
- type: recall_at_100
value: 35.929
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 7.9
- type: recall_at_5
value: 8.932
- task:
type: Classification
dataset:
type: ScandEval/norec-mini
name: MTEB NoRecClassification
config: default
split: test
revision: 07b99ab3363c2e7f8f87015b01c21f4d9b917ce3
metrics:
- type: accuracy
value: 48.251953125
- type: f1
value: 45.42526611578402
- task:
type: Classification
dataset:
type: strombergnlp/nordic_langid
name: MTEB NordicLangClassification
config: default
split: test
revision: e254179d18ab0165fdb6dbef91178266222bee2a
metrics:
- type: accuracy
value: 48.403333333333336
- type: f1
value: 47.9287124185198
- task:
type: BitextMining
dataset:
type: kardosdrur/norwegian-courts
name: MTEB NorwegianCourtsBitextMining
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.85964912280701
- type: f1
value: 92.98245614035088
- type: precision
value: 92.54385964912281
- type: recall
value: 93.85964912280701
- task:
type: Classification
dataset:
type: NbAiLab/norwegian_parliament
name: MTEB NorwegianParliament
config: default
split: test
revision: f7393532774c66312378d30b197610b43d751972
metrics:
- type: accuracy
value: 55.991666666666674
- type: ap
value: 53.417849849746226
- type: f1
value: 55.757916182475384
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 54.68327016783974
- type: cos_sim_ap
value: 55.175059616546406
- type: cos_sim_f1
value: 67.81733189500179
- type: cos_sim_precision
value: 51.41766630316249
- type: cos_sim_recall
value: 99.57761351636748
- type: dot_accuracy
value: 54.68327016783974
- type: dot_ap
value: 55.175059616546406
- type: dot_f1
value: 67.81733189500179
- type: dot_precision
value: 51.41766630316249
- type: dot_recall
value: 99.57761351636748
- type: euclidean_accuracy
value: 54.68327016783974
- type: euclidean_ap
value: 55.17510180566365
- type: euclidean_f1
value: 67.81733189500179
- type: euclidean_precision
value: 51.41766630316249
- type: euclidean_recall
value: 99.57761351636748
- type: manhattan_accuracy
value: 55.44125609095831
- type: manhattan_ap
value: 55.76283671826867
- type: manhattan_f1
value: 68.05905653583004
- type: manhattan_precision
value: 51.63934426229508
- type: manhattan_recall
value: 99.78880675818374
- type: max_accuracy
value: 55.44125609095831
- type: max_ap
value: 55.76283671826867
- type: max_f1
value: 68.05905653583004
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 75.64
- type: ap
value: 71.45085103287833
- type: f1
value: 75.52254495697326
- task:
type: Classification
dataset:
type: laugustyniak/abusive-clauses-pl
name: MTEB PAC
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 73.86620330147699
- type: ap
value: 80.58015815306322
- type: f1
value: 71.49082510883872
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 29.52361689421863
- type: cos_sim_spearman
value: 32.750058577257875
- type: euclidean_pearson
value: 34.583472972871796
- type: euclidean_spearman
value: 32.75328764421994
- type: manhattan_pearson
value: 34.727366510326995
- type: manhattan_spearman
value: 32.787167142114214
- task:
type: PairClassification
dataset:
type: PL-MTEB/ppc-pairclassification
name: MTEB PPC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 71.1
- type: cos_sim_ap
value: 85.36544548691205
- type: cos_sim_f1
value: 75.23393636930756
- type: cos_sim_precision
value: 60.36036036036037
- type: cos_sim_recall
value: 99.83443708609272
- type: dot_accuracy
value: 71.1
- type: dot_ap
value: 85.36544548691204
- type: dot_f1
value: 75.23393636930756
- type: dot_precision
value: 60.36036036036037
- type: dot_recall
value: 99.83443708609272
- type: euclidean_accuracy
value: 71.1
- type: euclidean_ap
value: 85.36544548691205
- type: euclidean_f1
value: 75.23393636930756
- type: euclidean_precision
value: 60.36036036036037
- type: euclidean_recall
value: 99.83443708609272
- type: manhattan_accuracy
value: 71.1
- type: manhattan_ap
value: 85.33853868545614
- type: manhattan_f1
value: 75.23393636930756
- type: manhattan_precision
value: 60.36036036036037
- type: manhattan_recall
value: 99.83443708609272
- type: max_accuracy
value: 71.1
- type: max_ap
value: 85.36544548691205
- type: max_f1
value: 75.23393636930756
- task:
type: PairClassification
dataset:
type: PL-MTEB/psc-pairclassification
name: MTEB PSC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 90.81632653061224
- type: cos_sim_ap
value: 91.97693749083473
- type: cos_sim_f1
value: 85.55078683834049
- type: cos_sim_precision
value: 80.59299191374663
- type: cos_sim_recall
value: 91.15853658536585
- type: dot_accuracy
value: 90.81632653061224
- type: dot_ap
value: 91.97693749083473
- type: dot_f1
value: 85.55078683834049
- type: dot_precision
value: 80.59299191374663
- type: dot_recall
value: 91.15853658536585
- type: euclidean_accuracy
value: 90.81632653061224
- type: euclidean_ap
value: 91.97693749083473
- type: euclidean_f1
value: 85.55078683834049
- type: euclidean_precision
value: 80.59299191374663
- type: euclidean_recall
value: 91.15853658536585
- type: manhattan_accuracy
value: 90.9090909090909
- type: manhattan_ap
value: 92.043441286281
- type: manhattan_f1
value: 85.34482758620689
- type: manhattan_precision
value: 80.70652173913044
- type: manhattan_recall
value: 90.54878048780488
- type: max_accuracy
value: 90.9090909090909
- type: max_ap
value: 92.043441286281
- type: max_f1
value: 85.55078683834049
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (de)
config: de
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 70.35
- type: cos_sim_ap
value: 72.01641717127626
- type: cos_sim_f1
value: 64.49511400651467
- type: cos_sim_precision
value: 55.26315789473685
- type: cos_sim_recall
value: 77.43016759776536
- type: dot_accuracy
value: 70.35
- type: dot_ap
value: 72.06599137974572
- type: dot_f1
value: 64.49511400651467
- type: dot_precision
value: 55.26315789473685
- type: dot_recall
value: 77.43016759776536
- type: euclidean_accuracy
value: 70.35
- type: euclidean_ap
value: 71.92019289154159
- type: euclidean_f1
value: 64.49511400651467
- type: euclidean_precision
value: 55.26315789473685
- type: euclidean_recall
value: 77.43016759776536
- type: manhattan_accuracy
value: 70.35
- type: manhattan_ap
value: 71.92979188519502
- type: manhattan_f1
value: 64.60409019402202
- type: manhattan_precision
value: 60.86956521739131
- type: manhattan_recall
value: 68.8268156424581
- type: max_accuracy
value: 70.35
- type: max_ap
value: 72.06599137974572
- type: max_f1
value: 64.60409019402202
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (en)
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 71.0
- type: cos_sim_ap
value: 74.73017292645147
- type: cos_sim_f1
value: 66.73427991886409
- type: cos_sim_precision
value: 61.78403755868545
- type: cos_sim_recall
value: 72.54685777287762
- type: dot_accuracy
value: 71.0
- type: dot_ap
value: 74.73017292645147
- type: dot_f1
value: 66.73427991886409
- type: dot_precision
value: 61.78403755868545
- type: dot_recall
value: 72.54685777287762
- type: euclidean_accuracy
value: 71.0
- type: euclidean_ap
value: 74.73013082197343
- type: euclidean_f1
value: 66.73427991886409
- type: euclidean_precision
value: 61.78403755868545
- type: euclidean_recall
value: 72.54685777287762
- type: manhattan_accuracy
value: 70.95
- type: manhattan_ap
value: 74.71203917486744
- type: manhattan_f1
value: 66.86868686868686
- type: manhattan_precision
value: 61.696178937558244
- type: manhattan_recall
value: 72.98787210584344
- type: max_accuracy
value: 71.0
- type: max_ap
value: 74.73017292645147
- type: max_f1
value: 66.86868686868686
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (es)
config: es
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 67.7
- type: cos_sim_ap
value: 69.70320170421651
- type: cos_sim_f1
value: 62.55625562556255
- type: cos_sim_precision
value: 52.851711026615966
- type: cos_sim_recall
value: 76.62624035281146
- type: dot_accuracy
value: 67.7
- type: dot_ap
value: 69.70320170421651
- type: dot_f1
value: 62.55625562556255
- type: dot_precision
value: 52.851711026615966
- type: dot_recall
value: 76.62624035281146
- type: euclidean_accuracy
value: 67.7
- type: euclidean_ap
value: 69.70320170421651
- type: euclidean_f1
value: 62.55625562556255
- type: euclidean_precision
value: 52.851711026615966
- type: euclidean_recall
value: 76.62624035281146
- type: manhattan_accuracy
value: 67.75
- type: manhattan_ap
value: 69.67833816050764
- type: manhattan_f1
value: 62.734082397003746
- type: manhattan_precision
value: 54.515866558177386
- type: manhattan_recall
value: 73.8699007717751
- type: max_accuracy
value: 67.75
- type: max_ap
value: 69.70320170421651
- type: max_f1
value: 62.734082397003746
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (fr)
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 69.0
- type: cos_sim_ap
value: 71.36406639969131
- type: cos_sim_f1
value: 64.45993031358886
- type: cos_sim_precision
value: 53.12275664034458
- type: cos_sim_recall
value: 81.94905869324474
- type: dot_accuracy
value: 69.0
- type: dot_ap
value: 71.2599779415656
- type: dot_f1
value: 64.45993031358886
- type: dot_precision
value: 53.12275664034458
- type: dot_recall
value: 81.94905869324474
- type: euclidean_accuracy
value: 69.0
- type: euclidean_ap
value: 71.3126257271965
- type: euclidean_f1
value: 64.45993031358886
- type: euclidean_precision
value: 53.12275664034458
- type: euclidean_recall
value: 81.94905869324474
- type: manhattan_accuracy
value: 69.0
- type: manhattan_ap
value: 71.29361764028188
- type: manhattan_f1
value: 64.54789615040288
- type: manhattan_precision
value: 54.16979714500376
- type: manhattan_recall
value: 79.84496124031007
- type: max_accuracy
value: 69.0
- type: max_ap
value: 71.36406639969131
- type: max_f1
value: 64.54789615040288
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (ja)
config: ja
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 63.849999999999994
- type: cos_sim_ap
value: 60.914955950361026
- type: cos_sim_f1
value: 62.4556422995032
- type: cos_sim_precision
value: 45.47803617571059
- type: cos_sim_recall
value: 99.66024915062289
- type: dot_accuracy
value: 63.849999999999994
- type: dot_ap
value: 60.808056565465506
- type: dot_f1
value: 62.4556422995032
- type: dot_precision
value: 45.47803617571059
- type: dot_recall
value: 99.66024915062289
- type: euclidean_accuracy
value: 63.849999999999994
- type: euclidean_ap
value: 60.8231492677072
- type: euclidean_f1
value: 62.4556422995032
- type: euclidean_precision
value: 45.47803617571059
- type: euclidean_recall
value: 99.66024915062289
- type: manhattan_accuracy
value: 63.800000000000004
- type: manhattan_ap
value: 60.86392751846975
- type: manhattan_f1
value: 62.43348705214614
- type: manhattan_precision
value: 45.45454545454545
- type: manhattan_recall
value: 99.66024915062289
- type: max_accuracy
value: 63.849999999999994
- type: max_ap
value: 60.914955950361026
- type: max_f1
value: 62.4556422995032
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (ko)
config: ko
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.1
- type: cos_sim_ap
value: 58.40339411735916
- type: cos_sim_f1
value: 62.7906976744186
- type: cos_sim_precision
value: 46.55172413793103
- type: cos_sim_recall
value: 96.42857142857143
- type: dot_accuracy
value: 61.1
- type: dot_ap
value: 58.439189685586456
- type: dot_f1
value: 62.7906976744186
- type: dot_precision
value: 46.55172413793103
- type: dot_recall
value: 96.42857142857143
- type: euclidean_accuracy
value: 61.1
- type: euclidean_ap
value: 58.34968788203145
- type: euclidean_f1
value: 62.7906976744186
- type: euclidean_precision
value: 46.55172413793103
- type: euclidean_recall
value: 96.42857142857143
- type: manhattan_accuracy
value: 61.1
- type: manhattan_ap
value: 58.31504446861402
- type: manhattan_f1
value: 62.636562272396226
- type: manhattan_precision
value: 46.48648648648649
- type: manhattan_recall
value: 95.98214285714286
- type: max_accuracy
value: 61.1
- type: max_ap
value: 58.439189685586456
- type: max_f1
value: 62.7906976744186
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (zh)
config: zh
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 64.2
- type: cos_sim_ap
value: 63.73722153283802
- type: cos_sim_f1
value: 62.52707581227437
- type: cos_sim_precision
value: 46.16204690831556
- type: cos_sim_recall
value: 96.86800894854586
- type: dot_accuracy
value: 64.2
- type: dot_ap
value: 63.67335241021108
- type: dot_f1
value: 62.52707581227437
- type: dot_precision
value: 46.16204690831556
- type: dot_recall
value: 96.86800894854586
- type: euclidean_accuracy
value: 64.2
- type: euclidean_ap
value: 63.77399571117368
- type: euclidean_f1
value: 62.52707581227437
- type: euclidean_precision
value: 46.16204690831556
- type: euclidean_recall
value: 96.86800894854586
- type: manhattan_accuracy
value: 64.5
- type: manhattan_ap
value: 63.747406783360816
- type: manhattan_f1
value: 62.58601955813112
- type: manhattan_precision
value: 46.27745045527584
- type: manhattan_recall
value: 96.64429530201343
- type: max_accuracy
value: 64.5
- type: max_ap
value: 63.77399571117368
- type: max_f1
value: 62.58601955813112
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_in
name: MTEB PolEmo2.0-IN
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 52.797783933518005
- type: f1
value: 53.84971294048786
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_out
name: MTEB PolEmo2.0-OUT
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 38.40080971659919
- type: f1
value: 30.38990873840624
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 23.34232568997104
- type: cos_sim_spearman
value: 24.47961936211083
- type: euclidean_pearson
value: 22.03140944610336
- type: euclidean_spearman
value: 24.47949166265398
- type: manhattan_pearson
value: 25.542406448726908
- type: manhattan_spearman
value: 28.655724283839533
- task:
type: Retrieval
dataset:
type: quora-pl
name: MTEB Quora-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.938
- type: map_at_10
value: 72.734
- type: map_at_100
value: 73.564
- type: map_at_1000
value: 73.602
- type: map_at_3
value: 69.707
- type: map_at_5
value: 71.515
- type: mrr_at_1
value: 69.28
- type: mrr_at_10
value: 76.97500000000001
- type: mrr_at_100
value: 77.27199999999999
- type: mrr_at_1000
value: 77.28
- type: mrr_at_3
value: 75.355
- type: mrr_at_5
value: 76.389
- type: ndcg_at_1
value: 69.33
- type: ndcg_at_10
value: 77.61099999999999
- type: ndcg_at_100
value: 80.02
- type: ndcg_at_1000
value: 80.487
- type: ndcg_at_3
value: 73.764
- type: ndcg_at_5
value: 75.723
- type: precision_at_1
value: 69.33
- type: precision_at_10
value: 11.917
- type: precision_at_100
value: 1.447
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 32.29
- type: precision_at_5
value: 21.432000000000002
- type: recall_at_1
value: 59.938
- type: recall_at_10
value: 87.252
- type: recall_at_100
value: 96.612
- type: recall_at_1000
value: 99.388
- type: recall_at_3
value: 76.264
- type: recall_at_5
value: 81.71000000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 61.458999999999996
- type: map_at_10
value: 73.90299999999999
- type: map_at_100
value: 74.733
- type: map_at_1000
value: 74.771
- type: map_at_3
value: 70.999
- type: map_at_5
value: 72.745
- type: mrr_at_1
value: 70.93
- type: mrr_at_10
value: 78.353
- type: mrr_at_100
value: 78.636
- type: mrr_at_1000
value: 78.644
- type: mrr_at_3
value: 76.908
- type: mrr_at_5
value: 77.807
- type: ndcg_at_1
value: 70.93
- type: ndcg_at_10
value: 78.625
- type: ndcg_at_100
value: 81.01
- type: ndcg_at_1000
value: 81.45700000000001
- type: ndcg_at_3
value: 75.045
- type: ndcg_at_5
value: 76.84299999999999
- type: precision_at_1
value: 70.93
- type: precision_at_10
value: 11.953
- type: precision_at_100
value: 1.4489999999999998
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 32.65
- type: precision_at_5
value: 21.598
- type: recall_at_1
value: 61.458999999999996
- type: recall_at_10
value: 87.608
- type: recall_at_100
value: 96.818
- type: recall_at_1000
value: 99.445
- type: recall_at_3
value: 77.354
- type: recall_at_5
value: 82.334
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 28.519889100999958
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 38.62765374782771
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.52
- type: map_at_10
value: 0.893
- type: map_at_100
value: 1.113
- type: map_at_1000
value: 1.304
- type: map_at_3
value: 0.7779999999999999
- type: map_at_5
value: 0.8200000000000001
- type: mrr_at_1
value: 2.6
- type: mrr_at_10
value: 4.0680000000000005
- type: mrr_at_100
value: 4.6080000000000005
- type: mrr_at_1000
value: 4.797
- type: mrr_at_3
value: 3.5999999999999996
- type: mrr_at_5
value: 3.8150000000000004
- type: ndcg_at_1
value: 2.6
- type: ndcg_at_10
value: 1.79
- type: ndcg_at_100
value: 3.5549999999999997
- type: ndcg_at_1000
value: 9.942
- type: ndcg_at_3
value: 1.94
- type: ndcg_at_5
value: 1.543
- type: precision_at_1
value: 2.6
- type: precision_at_10
value: 0.8500000000000001
- type: precision_at_100
value: 0.361
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 1.7670000000000001
- type: precision_at_5
value: 1.26
- type: recall_at_1
value: 0.52
- type: recall_at_10
value: 1.7149999999999999
- type: recall_at_100
value: 7.318
- type: recall_at_1000
value: 39.915
- type: recall_at_3
value: 1.0699999999999998
- type: recall_at_5
value: 1.27
- task:
type: Retrieval
dataset:
type: scidocs-pl
name: MTEB SCIDOCS-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.32
- type: map_at_10
value: 0.676
- type: map_at_100
value: 0.847
- type: map_at_1000
value: 1.032
- type: map_at_3
value: 0.5369999999999999
- type: map_at_5
value: 0.592
- type: mrr_at_1
value: 1.6
- type: mrr_at_10
value: 2.863
- type: mrr_at_100
value: 3.334
- type: mrr_at_1000
value: 3.5479999999999996
- type: mrr_at_3
value: 2.317
- type: mrr_at_5
value: 2.587
- type: ndcg_at_1
value: 1.6
- type: ndcg_at_10
value: 1.397
- type: ndcg_at_100
value: 2.819
- type: ndcg_at_1000
value: 9.349
- type: ndcg_at_3
value: 1.3
- type: ndcg_at_5
value: 1.1079999999999999
- type: precision_at_1
value: 1.6
- type: precision_at_10
value: 0.74
- type: precision_at_100
value: 0.295
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 1.2
- type: precision_at_5
value: 0.96
- type: recall_at_1
value: 0.32
- type: recall_at_10
value: 1.505
- type: recall_at_100
value: 5.988
- type: recall_at_1000
value: 39.308
- type: recall_at_3
value: 0.72
- type: recall_at_5
value: 0.9650000000000001
- task:
type: PairClassification
dataset:
type: PL-MTEB/sicke-pl-pairclassification
name: MTEB SICK-E-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 73.84834896045659
- type: cos_sim_ap
value: 55.484124732566606
- type: cos_sim_f1
value: 57.34228187919464
- type: cos_sim_precision
value: 46.01464885825076
- type: cos_sim_recall
value: 76.06837606837607
- type: dot_accuracy
value: 73.84834896045659
- type: dot_ap
value: 55.48400003295399
- type: dot_f1
value: 57.34228187919464
- type: dot_precision
value: 46.01464885825076
- type: dot_recall
value: 76.06837606837607
- type: euclidean_accuracy
value: 73.84834896045659
- type: euclidean_ap
value: 55.48407331902175
- type: euclidean_f1
value: 57.34228187919464
- type: euclidean_precision
value: 46.01464885825076
- type: euclidean_recall
value: 76.06837606837607
- type: manhattan_accuracy
value: 73.80758255197716
- type: manhattan_ap
value: 55.42477275597209
- type: manhattan_f1
value: 57.55860953920776
- type: manhattan_precision
value: 46.29388816644994
- type: manhattan_recall
value: 76.06837606837607
- type: max_accuracy
value: 73.84834896045659
- type: max_ap
value: 55.484124732566606
- type: max_f1
value: 57.55860953920776
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 67.03943120783973
- type: cos_sim_spearman
value: 62.93971145260584
- type: euclidean_pearson
value: 64.13947263916926
- type: euclidean_spearman
value: 62.93972324235839
- type: manhattan_pearson
value: 64.11295322654566
- type: manhattan_spearman
value: 62.92816122293202
- task:
type: STS
dataset:
type: PL-MTEB/sickr-pl-sts
name: MTEB SICK-R-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 67.75034167381077
- type: cos_sim_spearman
value: 62.98158872758643
- type: euclidean_pearson
value: 64.25794794439082
- type: euclidean_spearman
value: 62.981566596223125
- type: manhattan_pearson
value: 64.25439446502435
- type: manhattan_spearman
value: 63.01301439900365
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 61.622204530882755
- type: cos_sim_spearman
value: 65.4632047656541
- type: euclidean_pearson
value: 59.21529585527598
- type: euclidean_spearman
value: 65.4638163967956
- type: manhattan_pearson
value: 59.39341472707122
- type: manhattan_spearman
value: 65.57635757250173
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 60.329743331971486
- type: cos_sim_spearman
value: 62.78607195958339
- type: euclidean_pearson
value: 62.07415212138581
- type: euclidean_spearman
value: 62.78618151904129
- type: manhattan_pearson
value: 62.41250554765521
- type: manhattan_spearman
value: 62.87580558029627
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 59.16277512775291
- type: cos_sim_spearman
value: 57.53693422381856
- type: euclidean_pearson
value: 57.85017283427473
- type: euclidean_spearman
value: 57.53697385589326
- type: manhattan_pearson
value: 58.049796184955596
- type: manhattan_spearman
value: 57.76174789162225
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 74.42588553600197
- type: cos_sim_spearman
value: 74.25087788257943
- type: euclidean_pearson
value: 73.35436018935222
- type: euclidean_spearman
value: 74.25087694991477
- type: manhattan_pearson
value: 73.33747415771185
- type: manhattan_spearman
value: 74.21504509447377
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 75.77242432372144
- type: cos_sim_spearman
value: 75.72930700521489
- type: euclidean_pearson
value: 75.6995220623788
- type: euclidean_spearman
value: 75.72930646047212
- type: manhattan_pearson
value: 75.65841087952896
- type: manhattan_spearman
value: 75.69567692328437
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 66.2495297342053
- type: cos_sim_spearman
value: 66.14124319602982
- type: euclidean_pearson
value: 66.49498096178358
- type: euclidean_spearman
value: 66.14121792287747
- type: manhattan_pearson
value: 66.51560623835172
- type: manhattan_spearman
value: 66.05794413582558
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.0045186560239
- type: cos_sim_spearman
value: 74.96504390762252
- type: euclidean_pearson
value: 74.20988464347049
- type: euclidean_spearman
value: 74.98114602301776
- type: manhattan_pearson
value: 74.37929169860529
- type: manhattan_spearman
value: 75.37049827509504
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 73.88478151514396
- type: cos_sim_spearman
value: 74.05322141272103
- type: euclidean_pearson
value: 73.52175483343693
- type: euclidean_spearman
value: 74.05322141272103
- type: manhattan_pearson
value: 73.35875118828287
- type: manhattan_spearman
value: 73.83972625384673
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.57014781622605
- type: cos_sim_spearman
value: 74.95329129562734
- type: euclidean_pearson
value: 75.5667786729257
- type: euclidean_spearman
value: 74.95329129562734
- type: manhattan_pearson
value: 75.39548673816147
- type: manhattan_spearman
value: 74.89428642503749
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.04007129652777
- type: cos_sim_spearman
value: 79.94429611477106
- type: euclidean_pearson
value: 79.91583070858822
- type: euclidean_spearman
value: 79.94429611477106
- type: manhattan_pearson
value: 80.14382273152769
- type: manhattan_spearman
value: 80.23862855392836
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.28740870194635
- type: cos_sim_spearman
value: 77.18286391819586
- type: euclidean_pearson
value: 77.05644328687119
- type: euclidean_spearman
value: 77.18286391819586
- type: manhattan_pearson
value: 77.15625898067294
- type: manhattan_spearman
value: 77.03165154316278
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.99293002371301
- type: cos_sim_spearman
value: 72.24657859872468
- type: euclidean_pearson
value: 73.38839879755461
- type: euclidean_spearman
value: 72.24657859872468
- type: manhattan_pearson
value: 73.6627728800822
- type: manhattan_spearman
value: 72.70893449698669
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.37213723705916
- type: cos_sim_spearman
value: 80.64548512701263
- type: euclidean_pearson
value: 80.94992193351284
- type: euclidean_spearman
value: 80.64484963200427
- type: manhattan_pearson
value: 80.92246813841794
- type: manhattan_spearman
value: 80.68860823161657
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.54059604962391
- type: cos_sim_spearman
value: 77.19559169700682
- type: euclidean_pearson
value: 77.32739821317861
- type: euclidean_spearman
value: 77.19559169700682
- type: manhattan_pearson
value: 77.29224328831437
- type: manhattan_spearman
value: 77.24394878313191
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.06397062195414
- type: cos_sim_spearman
value: 78.66694637555244
- type: euclidean_pearson
value: 79.34923290885872
- type: euclidean_spearman
value: 78.66694637555244
- type: manhattan_pearson
value: 79.50802161625809
- type: manhattan_spearman
value: 78.79195213396169
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.66045829245238
- type: cos_sim_spearman
value: 78.14055373851183
- type: euclidean_pearson
value: 78.94489279300518
- type: euclidean_spearman
value: 78.14055373851183
- type: manhattan_pearson
value: 79.33473165536323
- type: manhattan_spearman
value: 78.5783429705299
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 36.63454535818336
- type: cos_sim_spearman
value: 47.12016162570126
- type: euclidean_pearson
value: 39.07268779927362
- type: euclidean_spearman
value: 47.12016162570126
- type: manhattan_pearson
value: 41.723119770725944
- type: manhattan_spearman
value: 47.90334362422989
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 13.325547358617957
- type: cos_sim_spearman
value: 24.094051740693416
- type: euclidean_pearson
value: 10.39110006005262
- type: euclidean_spearman
value: 24.094051740693416
- type: manhattan_pearson
value: 12.4380555005162
- type: manhattan_spearman
value: 25.176800279885715
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 41.21281570342249
- type: cos_sim_spearman
value: 55.397885077207974
- type: euclidean_pearson
value: 43.96150945976646
- type: euclidean_spearman
value: 55.397885077207974
- type: manhattan_pearson
value: 49.58812224529121
- type: manhattan_spearman
value: 55.35874879475974
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 5.985012243744998
- type: cos_sim_spearman
value: 25.307464943919012
- type: euclidean_pearson
value: -4.080537702499046
- type: euclidean_spearman
value: 25.307464943919012
- type: manhattan_pearson
value: -2.5058642304196543
- type: manhattan_spearman
value: 26.751588484373233
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 34.44666578772084
- type: cos_sim_spearman
value: 46.45977141800899
- type: euclidean_pearson
value: 38.78305544036559
- type: euclidean_spearman
value: 46.45977141800899
- type: manhattan_pearson
value: 46.45101297876112
- type: manhattan_spearman
value: 50.642972694093814
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 28.095327083873055
- type: cos_sim_spearman
value: 40.24741745875892
- type: euclidean_pearson
value: 29.141496784653892
- type: euclidean_spearman
value: 40.24741745875892
- type: manhattan_pearson
value: 32.013290716034064
- type: manhattan_spearman
value: 40.85454084311211
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 27.46788309503312
- type: cos_sim_spearman
value: 43.57385391855994
- type: euclidean_pearson
value: 24.558349674326177
- type: euclidean_spearman
value: 43.57385391855994
- type: manhattan_pearson
value: 28.974505207055866
- type: manhattan_spearman
value: 44.111553205713
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 34.87841073990563
- type: cos_sim_spearman
value: 52.8221686505807
- type: euclidean_pearson
value: 38.36114580544504
- type: euclidean_spearman
value: 52.8221686505807
- type: manhattan_pearson
value: 46.69329448756753
- type: manhattan_spearman
value: 53.9140781097337
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 49.999267528357
- type: cos_sim_spearman
value: 61.71837669697145
- type: euclidean_pearson
value: 53.578476744372274
- type: euclidean_spearman
value: 61.71837669697145
- type: manhattan_pearson
value: 56.410294227490795
- type: manhattan_spearman
value: 60.684457655864875
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 22.43564137760586
- type: cos_sim_spearman
value: 34.28346144104183
- type: euclidean_pearson
value: 27.41326011184764
- type: euclidean_spearman
value: 34.28346144104183
- type: manhattan_pearson
value: 35.62923154232163
- type: manhattan_spearman
value: 37.937151135297185
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 44.34071611983998
- type: cos_sim_spearman
value: 57.823185616169646
- type: euclidean_pearson
value: 49.29310650157244
- type: euclidean_spearman
value: 57.823185616169646
- type: manhattan_pearson
value: 55.93298736518848
- type: manhattan_spearman
value: 58.57556581684834
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 56.07027840344927
- type: cos_sim_spearman
value: 62.20158260763411
- type: euclidean_pearson
value: 55.887969718543616
- type: euclidean_spearman
value: 62.20158260763411
- type: manhattan_pearson
value: 56.081533365738444
- type: manhattan_spearman
value: 62.018651361750685
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 41.41816324477061
- type: cos_sim_spearman
value: 44.71684955996943
- type: euclidean_pearson
value: 42.74585025834968
- type: euclidean_spearman
value: 44.71684955996943
- type: manhattan_pearson
value: 47.992481632815256
- type: manhattan_spearman
value: 46.18587933349126
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 38.89140730917917
- type: cos_sim_spearman
value: 49.18633779347391
- type: euclidean_pearson
value: 43.27605428753535
- type: euclidean_spearman
value: 49.18633779347391
- type: manhattan_pearson
value: 48.22046568809415
- type: manhattan_spearman
value: 49.248416391249464
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 40.31620568726327
- type: cos_sim_spearman
value: 49.13034440774138
- type: euclidean_pearson
value: 43.95169508285692
- type: euclidean_spearman
value: 49.13034440774138
- type: manhattan_pearson
value: 48.84250981398146
- type: manhattan_spearman
value: 49.54216339903405
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 27.074582378144058
- type: cos_sim_spearman
value: 41.29498619968451
- type: euclidean_pearson
value: 28.993986097276505
- type: euclidean_spearman
value: 41.29498619968451
- type: manhattan_pearson
value: 32.079813951133254
- type: manhattan_spearman
value: 43.664111732941464
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 6.864334110072116
- type: cos_sim_spearman
value: 25.805458732687914
- type: euclidean_pearson
value: 11.435920047618103
- type: euclidean_spearman
value: 25.805458732687914
- type: manhattan_pearson
value: 15.036308569899552
- type: manhattan_spearman
value: 25.405135387192757
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.44029549925597
- type: cos_sim_spearman
value: 61.97797868009122
- type: euclidean_pearson
value: 65.92740669959876
- type: euclidean_spearman
value: 61.97797868009122
- type: manhattan_pearson
value: 70.29575044091207
- type: manhattan_spearman
value: 73.24670207647144
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 51.35413149349556
- type: cos_sim_spearman
value: 50.175051356729924
- type: euclidean_pearson
value: 53.12039152785364
- type: euclidean_spearman
value: 50.174289111089685
- type: manhattan_pearson
value: 53.0731746793555
- type: manhattan_spearman
value: 50.15176393928403
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 67.84222983023291
- type: cos_sim_spearman
value: 67.39086924655895
- type: euclidean_pearson
value: 67.3393327127967
- type: euclidean_spearman
value: 67.39088047106472
- type: manhattan_pearson
value: 67.40316731822271
- type: manhattan_spearman
value: 67.49067800994015
- task:
type: Classification
dataset:
type: ScandEval/scala-da
name: MTEB ScalaDaClassification
config: default
split: test
revision: 1de08520a7b361e92ffa2a2201ebd41942c54675
metrics:
- type: accuracy
value: 50.62988281250001
- type: ap
value: 50.32274824114816
- type: f1
value: 50.37741703766756
- task:
type: Classification
dataset:
type: ScandEval/scala-nb
name: MTEB ScalaNbClassification
config: default
split: test
revision: 237111a078ad5a834a55c57803d40bbe410ed03b
metrics:
- type: accuracy
value: 51.181640625
- type: ap
value: 50.60884394099696
- type: f1
value: 50.866988720930415
- task:
type: Classification
dataset:
type: ScandEval/scala-nn
name: MTEB ScalaNnClassification
config: default
split: test
revision: 9d9a2a4092ed3cacf0744592f6d2f32ab8ef4c0b
metrics:
- type: accuracy
value: 50.9375
- type: ap
value: 50.47969135089731
- type: f1
value: 50.62913552324756
- task:
type: Classification
dataset:
type: ScandEval/scala-sv
name: MTEB ScalaSvClassification
config: default
split: test
revision: 1b48e3dcb02872335ff985ff938a054a4ed99008
metrics:
- type: accuracy
value: 51.1474609375
- type: ap
value: 50.5894187272385
- type: f1
value: 50.901812392367916
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 68.36051662289248
- type: mrr
value: 89.39224265204656
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.721999999999998
- type: map_at_10
value: 31.335
- type: map_at_100
value: 32.461
- type: map_at_1000
value: 32.557
- type: map_at_3
value: 29.282000000000004
- type: map_at_5
value: 30.602
- type: mrr_at_1
value: 24.667
- type: mrr_at_10
value: 32.363
- type: mrr_at_100
value: 33.421
- type: mrr_at_1000
value: 33.499
- type: mrr_at_3
value: 30.444
- type: mrr_at_5
value: 31.628
- type: ndcg_at_1
value: 24.667
- type: ndcg_at_10
value: 35.29
- type: ndcg_at_100
value: 40.665
- type: ndcg_at_1000
value: 43.241
- type: ndcg_at_3
value: 31.238
- type: ndcg_at_5
value: 33.486
- type: precision_at_1
value: 24.667
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.7969999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.667
- type: precision_at_5
value: 8.933
- type: recall_at_1
value: 23.721999999999998
- type: recall_at_10
value: 46.417
- type: recall_at_100
value: 70.944
- type: recall_at_1000
value: 91.033
- type: recall_at_3
value: 35.693999999999996
- type: recall_at_5
value: 40.944
- task:
type: Retrieval
dataset:
type: scifact-pl
name: MTEB SciFact-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.706
- type: map_at_10
value: 28.333000000000002
- type: map_at_100
value: 29.364
- type: map_at_1000
value: 29.451
- type: map_at_3
value: 26.112999999999996
- type: map_at_5
value: 27.502
- type: mrr_at_1
value: 23.0
- type: mrr_at_10
value: 29.555999999999997
- type: mrr_at_100
value: 30.536
- type: mrr_at_1000
value: 30.606
- type: mrr_at_3
value: 27.333000000000002
- type: mrr_at_5
value: 28.717
- type: ndcg_at_1
value: 23.0
- type: ndcg_at_10
value: 32.238
- type: ndcg_at_100
value: 37.785999999999994
- type: ndcg_at_1000
value: 40.266999999999996
- type: ndcg_at_3
value: 27.961000000000002
- type: ndcg_at_5
value: 30.322
- type: precision_at_1
value: 23.0
- type: precision_at_10
value: 4.7669999999999995
- type: precision_at_100
value: 0.787
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 11.444
- type: precision_at_5
value: 8.200000000000001
- type: recall_at_1
value: 21.706
- type: recall_at_10
value: 43.206
- type: recall_at_100
value: 69.678
- type: recall_at_1000
value: 89.333
- type: recall_at_3
value: 31.900000000000002
- type: recall_at_5
value: 37.594
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.5
- type: cos_sim_ap
value: 77.07584309978081
- type: cos_sim_f1
value: 71.8864950078823
- type: cos_sim_precision
value: 75.74750830564784
- type: cos_sim_recall
value: 68.4
- type: dot_accuracy
value: 99.5
- type: dot_ap
value: 77.07584309978081
- type: dot_f1
value: 71.8864950078823
- type: dot_precision
value: 75.74750830564784
- type: dot_recall
value: 68.4
- type: euclidean_accuracy
value: 99.5
- type: euclidean_ap
value: 77.07584309978081
- type: euclidean_f1
value: 71.8864950078823
- type: euclidean_precision
value: 75.74750830564784
- type: euclidean_recall
value: 68.4
- type: manhattan_accuracy
value: 99.50594059405941
- type: manhattan_ap
value: 77.41658577240027
- type: manhattan_f1
value: 71.91374663072777
- type: manhattan_precision
value: 78.01169590643275
- type: manhattan_recall
value: 66.7
- type: max_accuracy
value: 99.50594059405941
- type: max_ap
value: 77.41658577240027
- type: max_f1
value: 71.91374663072777
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 46.32521494308228
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 20.573273825125266
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 38.612724125942385
- type: mrr
value: 38.891130315762666
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.305330424238836
- type: cos_sim_spearman
value: 30.556621737388685
- type: dot_pearson
value: 29.30533056265583
- type: dot_spearman
value: 30.556621737388685
- task:
type: Classification
dataset:
type: ScandEval/swerec-mini
name: MTEB SweRecClassification
config: default
split: test
revision: 3c62f26bafdc4c4e1c16401ad4b32f0a94b46612
metrics:
- type: accuracy
value: 68.4716796875
- type: f1
value: 59.865730786092364
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 55.34794621490011
- type: mrr
value: 59.22764129348421
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 0.586
- type: map_at_10
value: 0.819
- type: map_at_100
value: 0.8920000000000001
- type: map_at_1000
value: 0.928
- type: map_at_3
value: 0.729
- type: map_at_5
value: 0.771
- type: mrr_at_1
value: 1.9949999999999999
- type: mrr_at_10
value: 2.608
- type: mrr_at_100
value: 2.771
- type: mrr_at_1000
value: 2.8289999999999997
- type: mrr_at_3
value: 2.365
- type: mrr_at_5
value: 2.483
- type: ndcg_at_1
value: 1.9949999999999999
- type: ndcg_at_10
value: 1.314
- type: ndcg_at_100
value: 1.831
- type: ndcg_at_1000
value: 3.4139999999999997
- type: ndcg_at_3
value: 1.377
- type: ndcg_at_5
value: 1.2630000000000001
- type: precision_at_1
value: 1.9949999999999999
- type: precision_at_10
value: 0.488
- type: precision_at_100
value: 0.123
- type: precision_at_1000
value: 0.054
- type: precision_at_3
value: 1.027
- type: precision_at_5
value: 0.737
- type: recall_at_1
value: 0.586
- type: recall_at_10
value: 1.3390000000000002
- type: recall_at_100
value: 3.15
- type: recall_at_1000
value: 11.859
- type: recall_at_3
value: 0.8710000000000001
- type: recall_at_5
value: 1.0290000000000001
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 40.946
- type: f1
value: 39.56517169731474
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.08499999999999999
- type: map_at_10
value: 0.462
- type: map_at_100
value: 0.893
- type: map_at_1000
value: 1.129
- type: map_at_3
value: 0.232
- type: map_at_5
value: 0.3
- type: mrr_at_1
value: 38.0
- type: mrr_at_10
value: 50.629999999999995
- type: mrr_at_100
value: 51.315999999999995
- type: mrr_at_1000
value: 51.365
- type: mrr_at_3
value: 47.0
- type: mrr_at_5
value: 48.9
- type: ndcg_at_1
value: 31.0
- type: ndcg_at_10
value: 24.823
- type: ndcg_at_100
value: 10.583
- type: ndcg_at_1000
value: 6.497999999999999
- type: ndcg_at_3
value: 30.95
- type: ndcg_at_5
value: 27.899
- type: precision_at_1
value: 38.0
- type: precision_at_10
value: 25.6
- type: precision_at_100
value: 8.98
- type: precision_at_1000
value: 2.248
- type: precision_at_3
value: 34.666999999999994
- type: precision_at_5
value: 29.599999999999998
- type: recall_at_1
value: 0.08499999999999999
- type: recall_at_10
value: 0.641
- type: recall_at_100
value: 2.002
- type: recall_at_1000
value: 4.902
- type: recall_at_3
value: 0.28200000000000003
- type: recall_at_5
value: 0.379
- task:
type: Retrieval
dataset:
type: trec-covid-pl
name: MTEB TRECCOVID-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.124
- type: map_at_10
value: 0.45199999999999996
- type: map_at_100
value: 0.874
- type: map_at_1000
value: 1.1039999999999999
- type: map_at_3
value: 0.253
- type: map_at_5
value: 0.32299999999999995
- type: mrr_at_1
value: 36.0
- type: mrr_at_10
value: 47.56
- type: mrr_at_100
value: 48.532
- type: mrr_at_1000
value: 48.579
- type: mrr_at_3
value: 45.0
- type: mrr_at_5
value: 45.5
- type: ndcg_at_1
value: 34.0
- type: ndcg_at_10
value: 24.529
- type: ndcg_at_100
value: 10.427
- type: ndcg_at_1000
value: 6.457
- type: ndcg_at_3
value: 31.173000000000002
- type: ndcg_at_5
value: 27.738000000000003
- type: precision_at_1
value: 38.0
- type: precision_at_10
value: 25.4
- type: precision_at_100
value: 8.88
- type: precision_at_1000
value: 2.2159999999999997
- type: precision_at_3
value: 34.666999999999994
- type: precision_at_5
value: 29.2
- type: recall_at_1
value: 0.124
- type: recall_at_10
value: 0.618
- type: recall_at_100
value: 1.9349999999999998
- type: recall_at_1000
value: 4.808
- type: recall_at_3
value: 0.28300000000000003
- type: recall_at_5
value: 0.382
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.55000000000001
- type: precision
value: 98.38333333333334
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.3179190751445
- type: f1
value: 59.44582071749702
- type: precision
value: 57.49678869621066
- type: recall
value: 65.3179190751445
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.53658536585366
- type: f1
value: 34.217555952803785
- type: precision
value: 32.96511296649355
- type: recall
value: 38.53658536585366
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.7
- type: f1
value: 98.26666666666665
- type: precision
value: 98.05
- type: recall
value: 98.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.13333333333333
- type: precision
value: 99.05000000000001
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.6
- type: precision
value: 97.3
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.23880597014925
- type: f1
value: 46.340992406389105
- type: precision
value: 44.556384742951906
- type: recall
value: 52.23880597014925
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.0
- type: f1
value: 93.67000000000002
- type: precision
value: 93.075
- type: recall
value: 95.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.29268292682927
- type: f1
value: 85.76422764227642
- type: precision
value: 84.84204413472706
- type: recall
value: 88.29268292682927
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.46666666666667
- type: precision
value: 96.1
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8408262454435
- type: f1
value: 95.9902794653706
- type: precision
value: 95.56500607533415
- type: recall
value: 96.8408262454435
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.3913043478261
- type: f1
value: 91.30434782608695
- type: precision
value: 90.28985507246377
- type: recall
value: 93.3913043478261
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.6086956521739
- type: f1
value: 88.1159420289855
- type: precision
value: 86.9623188405797
- type: recall
value: 90.6086956521739
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.16666666666667
- type: precision
value: 96.86666666666667
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.0
- type: f1
value: 92.34
- type: precision
value: 91.54166666666667
- type: recall
value: 94.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.92159227985525
- type: f1
value: 80.8868975817106
- type: precision
value: 79.11540008041817
- type: recall
value: 84.92159227985525
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.35
- type: precision
value: 92.58333333333334
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.3
- type: f1
value: 36.64473116255726
- type: precision
value: 34.64017752457381
- type: recall
value: 43.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.7
- type: f1
value: 95.68333333333332
- type: precision
value: 95.19999999999999
- type: recall
value: 96.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.47619047619048
- type: f1
value: 66.63032734461306
- type: precision
value: 65.46459191863879
- type: recall
value: 70.47619047619048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.5
- type: f1
value: 91.63
- type: precision
value: 90.75
- type: recall
value: 93.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.36666666666666
- type: precision
value: 93.83333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.51666666666667
- type: precision
value: 93.88333333333334
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.0
- type: f1
value: 80.46675324675326
- type: precision
value: 78.95999999999998
- type: recall
value: 84.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.93333333333332
- type: precision
value: 96.55
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 90.07333333333334
- type: precision
value: 89.16166666666668
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.35
- type: precision
value: 93.75
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.9
- type: f1
value: 98.53333333333335
- type: precision
value: 98.35000000000001
- type: recall
value: 98.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.22641509433963
- type: f1
value: 95.14824797843666
- type: precision
value: 94.60916442048517
- type: recall
value: 96.22641509433963
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.58974358974359
- type: f1
value: 91.59544159544159
- type: precision
value: 90.66951566951566
- type: recall
value: 93.58974358974359
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.1
- type: f1
value: 97.46666666666668
- type: precision
value: 97.15
- type: recall
value: 98.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.4090909090909
- type: f1
value: 91.5909090909091
- type: precision
value: 90.71969696969697
- type: recall
value: 93.4090909090909
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.51781970649894
- type: f1
value: 86.76150544075072
- type: precision
value: 85.55206149545772
- type: recall
value: 89.51781970649894
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.65
- type: precision
value: 97.38333333333333
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.87548638132296
- type: f1
value: 71.24698906800073
- type: precision
value: 69.66572338167668
- type: recall
value: 75.87548638132296
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.53846153846154
- type: f1
value: 54.83234714003944
- type: precision
value: 52.06552706552707
- type: recall
value: 61.53846153846154
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 54.183211233211225
- type: precision
value: 52.48751719986241
- type: recall
value: 59.199999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.3
- type: precision
value: 93.65
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.85046728971963
- type: f1
value: 85.25700934579439
- type: precision
value: 84.09267912772586
- type: recall
value: 87.85046728971963
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.0
- type: f1
value: 97.43333333333332
- type: precision
value: 97.15
- type: recall
value: 98.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.66055555555555
- type: precision
value: 87.81845238095238
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.6
- type: f1
value: 65.538895353013
- type: precision
value: 63.69531394330308
- type: recall
value: 70.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89999999999999
- type: f1
value: 96.06666666666668
- type: precision
value: 95.68333333333334
- type: recall
value: 96.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.95
- type: precision
value: 95.55
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.8
- type: precision
value: 93.13333333333334
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.45
- type: precision
value: 94.93333333333334
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.28333333333332
- type: precision
value: 96.98333333333333
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.16666666666666
- type: f1
value: 74.67336721249764
- type: precision
value: 73.26035353535354
- type: recall
value: 78.16666666666666
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.200000000000001
- type: f1
value: 8.48123815073815
- type: precision
value: 7.843657708032708
- type: recall
value: 11.200000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.3
- type: f1
value: 89.02333333333333
- type: precision
value: 87.97500000000001
- type: recall
value: 91.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.32142857142857
- type: f1
value: 67.69209956709956
- type: precision
value: 66.19047619047619
- type: recall
value: 72.32142857142857
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.69264544456641
- type: f1
value: 75.40693115885212
- type: precision
value: 73.67544822539335
- type: recall
value: 79.69264544456641
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 83.65666666666667
- type: precision
value: 82.24833333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.36666666666666
- type: precision
value: 94.86666666666666
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.3
- type: f1
value: 95.49
- type: precision
value: 95.10833333333333
- type: recall
value: 96.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.60000000000001
- type: f1
value: 87.04746031746032
- type: precision
value: 85.89583333333333
- type: recall
value: 89.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 84.57088023088022
- type: precision
value: 83.6475
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.7
- type: precision
value: 97.46666666666668
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (war-eng)
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 82.83333333333333
- type: precision
value: 81.80137426900586
- type: recall
value: 85.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.11999999999999
- type: precision
value: 88.12777777777778
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.16666666666669
- type: precision
value: 96.85000000000001
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.30666666666666
- type: precision
value: 97.02499999999999
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 27.00729927007299
- type: f1
value: 25.114895917815623
- type: precision
value: 24.602283361407448
- type: recall
value: 27.00729927007299
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mhr-eng)
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.099999999999998
- type: f1
value: 11.869284007509814
- type: precision
value: 11.199695454818405
- type: recall
value: 14.099999999999998
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.09
- type: precision
value: 96.80833333333332
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.47333333333333
- type: precision
value: 94.975
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (amh-eng)
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.45238095238095
- type: f1
value: 91.66666666666666
- type: precision
value: 90.77380952380952
- type: recall
value: 93.45238095238095
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pam-eng)
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.899999999999999
- type: f1
value: 10.303261315113037
- type: precision
value: 9.902986584515606
- type: recall
value: 11.899999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.57349896480332
- type: f1
value: 77.86519438693352
- type: precision
value: 76.35595081247254
- type: recall
value: 81.57349896480332
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 94.86666666666667
- type: precision
value: 94.25
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.8
- type: f1
value: 98.46666666666667
- type: precision
value: 98.3
- type: recall
value: 98.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.7
- type: f1
value: 8.621683883854935
- type: precision
value: 8.188292731521031
- type: recall
value: 10.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.47619047619048
- type: f1
value: 87.8581735724593
- type: precision
value: 86.72438672438673
- type: recall
value: 90.47619047619048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.0381679389313
- type: f1
value: 93.60050890585242
- type: precision
value: 92.970737913486
- type: recall
value: 95.0381679389313
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2532751091703
- type: f1
value: 97.67103347889375
- type: precision
value: 97.37991266375546
- type: recall
value: 98.2532751091703
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.6
- type: f1
value: 80.99904761904763
- type: precision
value: 79.54634920634919
- type: recall
value: 84.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89265536723164
- type: f1
value: 95.90395480225989
- type: precision
value: 95.4331450094162
- type: recall
value: 96.89265536723164
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.6
- type: f1
value: 9.981918087824628
- type: precision
value: 9.326319147606549
- type: recall
value: 12.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.65
- type: precision
value: 96.28333333333333
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.38333333333333
- type: precision
value: 94.83333333333333
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.43666666666665
- type: precision
value: 87.395
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.03333333333333
- type: precision
value: 96.71666666666667
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.4
- type: f1
value: 7.946889105220061
- type: precision
value: 7.665059865752875
- type: recall
value: 9.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.04021447721179
- type: f1
value: 93.68632707774799
- type: precision
value: 93.08534405719392
- type: recall
value: 95.04021447721179
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.66666666666667
- type: precision
value: 94.08333333333334
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.6086956521739
- type: f1
value: 77.98418972332016
- type: precision
value: 75.96837944664031
- type: recall
value: 82.6086956521739
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.77464788732394
- type: f1
value: 94.8356807511737
- type: precision
value: 94.36619718309859
- type: recall
value: 95.77464788732394
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.17365269461077
- type: f1
value: 47.07043056743655
- type: precision
value: 45.161363241830784
- type: recall
value: 53.17365269461077
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.5
- type: precision
value: 94.03333333333333
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.59605911330048
- type: f1
value: 91.82266009852216
- type: precision
value: 91.09195402298852
- type: recall
value: 93.59605911330048
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.40845070422534
- type: f1
value: 72.73082942097027
- type: precision
value: 71.46686939820742
- type: recall
value: 76.40845070422534
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.58974358974359
- type: f1
value: 91.98290598290598
- type: precision
value: 91.3119658119658
- type: recall
value: 93.58974358974359
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.06666666666668
- type: precision
value: 96.7
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.89352818371609
- type: f1
value: 64.47860652453555
- type: precision
value: 62.878651918592574
- type: recall
value: 68.89352818371609
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 33.800000000000004
- type: f1
value: 29.290774344112368
- type: precision
value: 28.066016735704647
- type: recall
value: 33.800000000000004
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.22801302931596
- type: f1
value: 88.07817589576547
- type: precision
value: 87.171552660152
- type: recall
value: 90.22801302931596
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.63333333333334
- type: precision
value: 97.36666666666667
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.95
- type: precision
value: 96.58333333333331
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.91338582677166
- type: f1
value: 90.81364829396327
- type: precision
value: 89.89501312335958
- type: recall
value: 92.91338582677166
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89999999999999
- type: f1
value: 95.98333333333332
- type: precision
value: 95.56666666666668
- type: recall
value: 96.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.51523545706371
- type: f1
value: 70.20346919931407
- type: precision
value: 68.6389565788895
- type: recall
value: 74.51523545706371
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.88333333333333
- type: precision
value: 96.53333333333333
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.15384615384615
- type: f1
value: 39.47885447885448
- type: precision
value: 37.301528599605525
- type: recall
value: 46.15384615384615
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.16666666666667
- type: precision
value: 92.41666666666667
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.83333333333333
- type: precision
value: 93.16666666666667
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.0
- type: f1
value: 89.98666666666666
- type: precision
value: 89.09166666666667
- type: recall
value: 92.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.51886792452831
- type: f1
value: 94.3003144654088
- type: precision
value: 93.75
- type: recall
value: 95.51886792452831
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.83333333333333
- type: precision
value: 97.65
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8978102189781
- type: f1
value: 96.04622871046227
- type: precision
value: 95.62043795620438
- type: recall
value: 96.8978102189781
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.1
- type: f1
value: 81.78564213564214
- type: precision
value: 80.46416666666667
- type: recall
value: 85.1
- task:
type: Clustering
dataset:
type: slvnwhrl/tenkgnad-clustering-p2p
name: MTEB TenKGnadClusteringP2P
config: default
split: test
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
metrics:
- type: v_measure
value: 21.827519839402644
- task:
type: Clustering
dataset:
type: slvnwhrl/tenkgnad-clustering-s2s
name: MTEB TenKGnadClusteringS2S
config: default
split: test
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
metrics:
- type: v_measure
value: 27.160188241713684
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 38.54459276932986
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 43.4460576234314
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20500000000000002
- type: map_at_10
value: 0.391
- type: map_at_100
value: 0.612
- type: map_at_1000
value: 0.645
- type: map_at_3
value: 0.302
- type: map_at_5
value: 0.383
- type: mrr_at_1
value: 4.082
- type: mrr_at_10
value: 5.612
- type: mrr_at_100
value: 6.822
- type: mrr_at_1000
value: 6.929
- type: mrr_at_3
value: 4.082
- type: mrr_at_5
value: 5.408
- type: ndcg_at_1
value: 4.082
- type: ndcg_at_10
value: 1.6840000000000002
- type: ndcg_at_100
value: 2.876
- type: ndcg_at_1000
value: 4.114
- type: ndcg_at_3
value: 2.52
- type: ndcg_at_5
value: 2.3720000000000003
- type: precision_at_1
value: 4.082
- type: precision_at_10
value: 1.429
- type: precision_at_100
value: 0.755
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 2.041
- type: precision_at_5
value: 2.4490000000000003
- type: recall_at_1
value: 0.20500000000000002
- type: recall_at_10
value: 0.761
- type: recall_at_100
value: 4.423
- type: recall_at_1000
value: 9.044
- type: recall_at_3
value: 0.302
- type: recall_at_5
value: 0.683
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 67.28359999999999
- type: ap
value: 12.424592214862038
- type: f1
value: 51.53630450055703
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.23372948500284
- type: f1
value: 56.440924587214234
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 24.410059815620116
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 80.3302139834297
- type: cos_sim_ap
value: 53.57723069745093
- type: cos_sim_f1
value: 51.58639580004565
- type: cos_sim_precision
value: 45.45454545454545
- type: cos_sim_recall
value: 59.63060686015831
- type: dot_accuracy
value: 80.3302139834297
- type: dot_ap
value: 53.57723006705641
- type: dot_f1
value: 51.58639580004565
- type: dot_precision
value: 45.45454545454545
- type: dot_recall
value: 59.63060686015831
- type: euclidean_accuracy
value: 80.3302139834297
- type: euclidean_ap
value: 53.57723050286929
- type: euclidean_f1
value: 51.58639580004565
- type: euclidean_precision
value: 45.45454545454545
- type: euclidean_recall
value: 59.63060686015831
- type: manhattan_accuracy
value: 80.31233235977827
- type: manhattan_ap
value: 53.44943961562638
- type: manhattan_f1
value: 51.24183006535947
- type: manhattan_precision
value: 43.63636363636363
- type: manhattan_recall
value: 62.05804749340369
- type: max_accuracy
value: 80.3302139834297
- type: max_ap
value: 53.57723069745093
- type: max_f1
value: 51.58639580004565
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.45876508712695
- type: cos_sim_ap
value: 83.5320716566614
- type: cos_sim_f1
value: 75.54560716284276
- type: cos_sim_precision
value: 73.27929362379678
- type: cos_sim_recall
value: 77.95657530027718
- type: dot_accuracy
value: 87.45876508712695
- type: dot_ap
value: 83.53209944887666
- type: dot_f1
value: 75.54560716284276
- type: dot_precision
value: 73.27929362379678
- type: dot_recall
value: 77.95657530027718
- type: euclidean_accuracy
value: 87.45876508712695
- type: euclidean_ap
value: 83.53205938307582
- type: euclidean_f1
value: 75.54560716284276
- type: euclidean_precision
value: 73.27929362379678
- type: euclidean_recall
value: 77.95657530027718
- type: manhattan_accuracy
value: 87.52280048123569
- type: manhattan_ap
value: 83.4884324728773
- type: manhattan_f1
value: 75.43366677906411
- type: manhattan_precision
value: 73.46566445303948
- type: manhattan_recall
value: 77.51000923929782
- type: max_accuracy
value: 87.52280048123569
- type: max_ap
value: 83.53209944887666
- type: max_f1
value: 75.54560716284276
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 13.100000000000001
- type: map_at_10
value: 15.620000000000001
- type: map_at_100
value: 15.928
- type: map_at_1000
value: 15.976
- type: map_at_3
value: 14.817
- type: map_at_5
value: 15.322
- type: mrr_at_1
value: 13.0
- type: mrr_at_10
value: 15.57
- type: mrr_at_100
value: 15.878
- type: mrr_at_1000
value: 15.926000000000002
- type: mrr_at_3
value: 14.767
- type: mrr_at_5
value: 15.272
- type: ndcg_at_1
value: 13.100000000000001
- type: ndcg_at_10
value: 17.05
- type: ndcg_at_100
value: 18.801000000000002
- type: ndcg_at_1000
value: 20.436
- type: ndcg_at_3
value: 15.425
- type: ndcg_at_5
value: 16.333000000000002
- type: precision_at_1
value: 13.100000000000001
- type: precision_at_10
value: 2.16
- type: precision_at_100
value: 0.304
- type: precision_at_1000
value: 0.044000000000000004
- type: precision_at_3
value: 5.733
- type: precision_at_5
value: 3.88
- type: recall_at_1
value: 13.100000000000001
- type: recall_at_10
value: 21.6
- type: recall_at_100
value: 30.4
- type: recall_at_1000
value: 44.1
- type: recall_at_3
value: 17.2
- type: recall_at_5
value: 19.400000000000002
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 76.12
- type: ap
value: 54.1619589378045
- type: f1
value: 74.32372858884229
- task:
type: Clustering
dataset:
type: jinaai/cities_wiki_clustering
name: MTEB WikiCitiesClustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 50.71744674029636
- task:
type: Retrieval
dataset:
type: jinaai/xmarket_de
name: MTEB XMarketDE
config: default
split: test
revision: 2336818db4c06570fcdf263e1bcb9993b786f67a
metrics:
- type: map_at_1
value: 0.182
- type: map_at_10
value: 0.266
- type: map_at_100
value: 0.295
- type: map_at_1000
value: 0.313
- type: map_at_3
value: 0.232
- type: map_at_5
value: 0.23800000000000002
- type: mrr_at_1
value: 1.3379999999999999
- type: mrr_at_10
value: 1.918
- type: mrr_at_100
value: 2.051
- type: mrr_at_1000
value: 2.084
- type: mrr_at_3
value: 1.7049999999999998
- type: mrr_at_5
value: 1.791
- type: ndcg_at_1
value: 1.3379999999999999
- type: ndcg_at_10
value: 0.859
- type: ndcg_at_100
value: 0.8500000000000001
- type: ndcg_at_1000
value: 1.345
- type: ndcg_at_3
value: 1.032
- type: ndcg_at_5
value: 0.918
- type: precision_at_1
value: 1.3379999999999999
- type: precision_at_10
value: 0.528
- type: precision_at_100
value: 0.22699999999999998
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 0.8829999999999999
- type: precision_at_5
value: 0.6890000000000001
- type: recall_at_1
value: 0.182
- type: recall_at_10
value: 0.51
- type: recall_at_100
value: 1.2229999999999999
- type: recall_at_1000
value: 4.183
- type: recall_at_3
value: 0.292
- type: recall_at_5
value: 0.315
---
# SONAR
[[Paper]](https://ai.meta.com/research/publications/sonar-sentence-level-multimodal-and-language-agnostic-representations/)
We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space, with a full suite of speech and text encoders and decoders. It substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks.
Speech segments can be embedded in the same SONAR embedding space using language-specific speech encoders trained in a teacher-student setting on speech transcription data. We also provide a single text decoder, which allows us to perform text-to-text and speech-to-text machine translation, including for zero-shot language and modality combinations.
*SONAR* stands for **S**entence-level multim**O**dal and la**N**guage-**A**gnostic **R**epresentations
The full list of supported languages (along with download links) can be found here [below](#supported-languages-and-download-links).
## Installing
SONAR depends mainly on [Fairseq2](https://github.com/fairinternal/fairseq2) and can be installed using (tested with `python=3.8`)
```bash
pip install --upgrade pip
pip config set global.extra-index-url https://test.pypi.org/simple/
pip install -e .
```
## Usage
fairseq2 will automatically download models into your `$TORCH_HOME/hub` directory upon using the commands below.
### Compute text sentence embeddings with SONAR:
```python
from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline
t2vec_model = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder",
tokenizer="text_sonar_basic_encoder")
sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
t2vec_model.predict(sentences, source_lang="eng_Latn").shape
# torch.Size([2, 1024])
```
### Translate text with SONAR
```python
from sonar.inference_pipelines.text import TextToTextModelPipeline
t2t_model = TextToTextModelPipeline(encoder="text_sonar_basic_encoder",
decoder="text_sonar_basic_decoder",
tokenizer="text_sonar_basic_encoder") # tokenizer is attached to both encoder and decoder cards
sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
t2t_model.predict(sentences, source_lang="eng_Latn", target_lang="fra_Latn")
# ['Mon nom est SONAR.', "Je peux intégrer les phrases dans l'espace vectoriel."]
```
### Compute speech sentence embeddings with SONAR
```python
from sonar.inference_pipelines.speech import SpeechToEmbeddingModelPipeline
s2vec_model = SpeechToEmbeddingModelPipeline(encoder="sonar_speech_encoder_eng")
s2vec_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
"./tests/integration_tests/data/audio_files/audio_2.wav"]).shape
# torch.Size([2, 1024])
import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"
s2vec_model.predict([inp]).shape
# torch.Size([1, 1024])
```
### Speech-to-text translation with SONAR
```python
from sonar.inference_pipelines.speech import SpeechToTextModelPipeline
s2t_model = SpeechToTextModelPipeline(encoder="sonar_speech_encoder_eng",
decoder="text_sonar_basic_decoder",
tokenizer="text_sonar_basic_decoder")
import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"
# passing loaded audio files
s2t_model.predict([inp], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.']
# passing multiple wav files
s2t_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
"./tests/integration_tests/data/audio_files/audio_2.wav"], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.',
# 'These couples may choose to make an adoption plan for their baby.']
```
### Predicting [cross-lingual semantic similarity](https://github.com/facebookresearch/fairseq/tree/nllb/examples/nllb/human_XSTS_eval) with BLASER 2 models
```Python
import torch
from sonar.models.blaser.loader import load_blaser_model
blaser_ref = load_blaser_model("blaser_st2st_ref_v2_0").eval()
blaser_qe = load_blaser_model("blaser_st2st_qe_v2_0").eval()
# BLASER-2 is supposed to work with SONAR speech and text embeddings,
# but we didn't include their extraction in this snippet, to keep it simple.
emb = torch.ones([1, 1024])
print(blaser_ref(src=emb, ref=emb, mt=emb).item()) # 5.2552
print(blaser_qe(src=emb, mt=emb).item()) # 4.9819
```
See more complete demo notebooks :
* [sonar text2text similarity and translation](examples/sonar_text_demo.ipynb)
* [sonar speech2text and other data pipeline examples](examples/inference_pipelines.ipynb)
## Model details
- **Developed by:** Paul-Ambroise Duquenne et al.
- **License:** CC-BY-NC 4.0 license
- **Cite as:**
```
@article{Duquenne:2023:sonar_arxiv,
author = {Paul-Ambroise Duquenne and Holger Schwenk and Benoit Sagot},
title = {{SONAR:} Sentence-Level Multimodal and Language-Agnostic Representations},
publisher = {arXiv},
year = {2023},
url = {https://arxiv.org/abs/unk},
}
``` |
LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-3.75bpw-h6-exl2 | LoneStriker | 2024-02-14T19:15:49Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-02-14T19:06:06Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
sam2ai/qwen_1.5_odia_0.5b | sam2ai | 2024-02-14T19:14:41Z | 1 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2024-02-13T02:39:13Z | ---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: Qwen/Qwen1.5-0.5B
model-index:
- name: qwen_1.5_odia_0.5b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: Qwen/Qwen1.5-0.5B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# is_qwen_derived_model: true
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: OdiaGenAIdata/culturax-odia
type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-qwen-0.5b-odia
hub_model_id: sam2ai/qwen_1.5_odia_0.5b
sequence_len: 2048 # supports up to 8192
sample_packing: false
pad_to_sequence_len:
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: Qwen-completion-0.5b-odia
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# qwen_1.5_odia_0.5b
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2821 | 0.0 | 1 | 1.2706 |
| 0.5906 | 0.25 | 1366 | 0.5987 |
| 0.531 | 0.5 | 2732 | 0.5510 |
| 0.5095 | 0.75 | 4098 | 0.5236 |
| 0.5027 | 1.0 | 5464 | 0.5054 |
| 0.5019 | 1.25 | 6830 | 0.4933 |
| 0.4798 | 1.5 | 8196 | 0.4845 |
| 0.4484 | 1.75 | 9562 | 0.4771 |
| 0.4526 | 2.0 | 10928 | 0.4704 |
| 0.4498 | 2.25 | 12294 | 0.4657 |
| 0.4508 | 2.5 | 13660 | 0.4608 |
| 0.4226 | 2.75 | 15026 | 0.4568 |
| 0.4161 | 3.0 | 16392 | 0.4539 |
| 0.4258 | 3.25 | 17758 | 0.4515 |
| 0.428 | 3.5 | 19124 | 0.4489 |
| 0.4748 | 3.75 | 20490 | 0.4459 |
| 0.4083 | 4.0 | 21856 | 0.4441 |
| 0.4278 | 4.25 | 23222 | 0.4423 |
| 0.3997 | 4.5 | 24588 | 0.4406 |
| 0.4581 | 4.75 | 25954 | 0.4386 |
| 0.378 | 5.0 | 27320 | 0.4372 |
| 0.4141 | 5.25 | 28686 | 0.4358 |
| 0.4017 | 5.5 | 30052 | 0.4344 |
| 0.4223 | 5.75 | 31418 | 0.4328 |
| 0.426 | 6.0 | 32784 | 0.4317 |
| 0.3967 | 6.25 | 34150 | 0.4310 |
| 0.3934 | 6.5 | 35516 | 0.4298 |
| 0.404 | 6.75 | 36882 | 0.4287 |
| 0.3874 | 7.0 | 38248 | 0.4282 |
| 0.384 | 7.25 | 39614 | 0.4275 |
| 0.3925 | 7.5 | 40980 | 0.4268 |
| 0.409 | 7.75 | 42346 | 0.4261 |
| 0.3891 | 8.0 | 43712 | 0.4256 |
| 0.41 | 8.25 | 45078 | 0.4253 |
| 0.3999 | 8.5 | 46444 | 0.4249 |
| 0.3874 | 8.75 | 47810 | 0.4247 |
| 0.3894 | 9.0 | 49176 | 0.4245 |
| 0.3827 | 9.25 | 50542 | 0.4244 |
| 0.3815 | 9.5 | 51908 | 0.4243 |
| 0.3816 | 9.75 | 53274 | 0.4242 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.0
- Pytorch 2.0.1+gita61a294
- Datasets 2.16.1
- Tokenizers 0.15.0 |
LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-3.5bpw-h6-exl2 | LoneStriker | 2024-02-14T19:06:04Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-02-14T18:57:04Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
akhilshekkari/ppo-LunarLander-v2 | akhilshekkari | 2024-02-14T19:01:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T19:01:01Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.48 +/- 25.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Shijia/furina_seed42_eng_esp_hau_basic | Shijia | 2024-02-14T19:00:32Z | 100 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T18:59:24Z | ---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_esp_hau_basic
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# furina_seed42_eng_esp_hau_basic
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0227
- Spearman Corr: 0.7567
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.61 | 200 | 0.0390 | 0.5403 |
| 0.0806 | 3.23 | 400 | 0.0258 | 0.7313 |
| 0.0295 | 4.84 | 600 | 0.0231 | 0.7463 |
| 0.022 | 6.45 | 800 | 0.0216 | 0.7582 |
| 0.017 | 8.06 | 1000 | 0.0241 | 0.7626 |
| 0.017 | 9.68 | 1200 | 0.0214 | 0.7723 |
| 0.0142 | 11.29 | 1400 | 0.0212 | 0.7660 |
| 0.0113 | 12.9 | 1600 | 0.0221 | 0.7655 |
| 0.0096 | 14.52 | 1800 | 0.0214 | 0.7690 |
| 0.0083 | 16.13 | 2000 | 0.0222 | 0.7595 |
| 0.0083 | 17.74 | 2200 | 0.0218 | 0.7649 |
| 0.0073 | 19.35 | 2400 | 0.0221 | 0.7600 |
| 0.0065 | 20.97 | 2600 | 0.0225 | 0.7606 |
| 0.0059 | 22.58 | 2800 | 0.0222 | 0.7574 |
| 0.0055 | 24.19 | 3000 | 0.0227 | 0.7567 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-2.4bpw-h6-exl2 | LoneStriker | 2024-02-14T18:48:40Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-02-14T18:42:16Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
feelwoo/roberta-base_ag_news | feelwoo | 2024-02-14T18:44:47Z | 94 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-02-14T12:49:47Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- ag_news
model-index:
- name: roberta-base_ag_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base_ag_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1897
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1879 | 1.0 | 3750 | 0.2106 |
| 0.1556 | 2.0 | 7500 | 0.1951 |
| 0.126 | 3.0 | 11250 | 0.1897 |
| 0.0576 | 4.0 | 15000 | 0.1921 |
| 0.0386 | 5.0 | 18750 | 0.2283 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
guihscosta/testellm | guihscosta | 2024-02-14T18:43:46Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-02-14T18:43:46Z | ---
license: creativeml-openrail-m
---
|
LoneStriker/openbuddy-mixtral-7bx8-v18.1-32k-GGUF | LoneStriker | 2024-02-14T18:42:14Z | 19 | 2 | transformers | [
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"region:us"
]
| text-generation | 2024-02-14T16:58:08Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
hythyt/ppo-LunarLander-v2 | hythyt | 2024-02-14T18:41:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-02-14T18:41:01Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.16 +/- 14.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
irisaparina/t5-3b-spider-nlvariation | irisaparina | 2024-02-14T18:38:06Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2402.08666",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-02-14T14:37:55Z | T5-3B model fine-tuned on augmented Spider proposed in the paper ["Improving Generalization in Semantic Parsing by Increasing Natural Language Variation"](https://arxiv.org/abs/2402.08666).
See more info [here](https://github.com/saparina/Text2SQL-NLVariation). |
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