|
--- |
|
language: |
|
- en |
|
license: mit |
|
tags: |
|
- text-classification |
|
- zero-shot-classification |
|
datasets: |
|
- multi_nli |
|
- facebook/anli |
|
- fever |
|
metrics: |
|
- accuracy |
|
pipeline_tag: zero-shot-classification |
|
model-index: |
|
- name: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli |
|
results: |
|
- task: |
|
type: natural-language-inference |
|
name: Natural Language Inference |
|
dataset: |
|
name: anli |
|
type: anli |
|
config: plain_text |
|
split: test_r3 |
|
metrics: |
|
- type: accuracy |
|
value: 0.495 |
|
name: Accuracy |
|
verified: true |
|
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- type: precision |
|
value: 0.4984740618243923 |
|
name: Precision Macro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTllZDU3NmVmYjk4ZmYzNjAwNzExMGZjNDMzOWRkZjRjMTRhNzhlZmI0ZmNlM2E0Mzk4OWE5NTM5MTYyYWU5NCIsInZlcnNpb24iOjF9.WHz_TUJgPVn-rU-9vBCDdmSMOuWzADwr09rJY6ktqRM46zytbyWs7Vcm7jqDrTkfU-rp0_7IyoNv_xEsKhJbBA |
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- type: precision |
|
value: 0.495 |
|
name: Precision Micro |
|
verified: true |
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|
- type: precision |
|
value: 0.4984357572868885 |
|
name: Precision Weighted |
|
verified: true |
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|
- type: recall |
|
value: 0.49461028192371476 |
|
name: Recall Macro |
|
verified: true |
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|
- type: recall |
|
value: 0.495 |
|
name: Recall Micro |
|
verified: true |
|
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|
- type: recall |
|
value: 0.495 |
|
name: Recall Weighted |
|
verified: true |
|
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- type: f1 |
|
value: 0.4942810999491704 |
|
name: F1 Macro |
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verified: true |
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- type: f1 |
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value: 0.495 |
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name: F1 Micro |
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verified: true |
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- type: f1 |
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value: 0.4944671868893595 |
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name: F1 Weighted |
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verified: true |
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- type: loss |
|
value: 1.8788293600082397 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzRlOTYwYjU1Y2Y4ZGM0NDBjYTE2MmEzNWIwN2NiMWVkOWZlNzA2ZmQ3YjZjNzI4MjQwYWZhODIwMzU3ODAyZiIsInZlcnNpb24iOjF9._Xs9bl48MSavvp5eyamrP2iNlFWv35QZCrmWjJXLkUdIBx0ElCjEdxBb3dxPGnUxdpDzGMmOoKCPI44ZPXrtDw |
|
- task: |
|
type: natural-language-inference |
|
name: Natural Language Inference |
|
dataset: |
|
name: anli |
|
type: anli |
|
config: plain_text |
|
split: test_r1 |
|
metrics: |
|
- type: accuracy |
|
value: 0.712 |
|
name: Accuracy |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWYxMGY0ZWU0YTEyY2I3NmQwZmQ3YmFmNzQxNGU5OGNjN2ViN2I0ZjdkYWUzM2RmYzkzMDg3ZjVmNGYwNGZkZCIsInZlcnNpb24iOjF9.snWBusAeo1rrQqWk--vTxb-CBcFqM298YCtwTQGBZiFegKGSTSKzj-SM6HMNsmoQWmMuv7UfYPqYlnzEthOSAg |
|
- type: precision |
|
value: 0.7134839439315348 |
|
name: Precision Macro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjMxMjg1Y2QwNzMwM2ZkNGM3ZTJhOGJmY2FkNGI1ZTFhOGQ3ODViNTJmZTYwMWJkZDYyYWRjMzFmZDI1NTM5YSIsInZlcnNpb24iOjF9.ZJnY6zYOBn-YEtN7uKzQ-VKXPwlIO1zq19Yuo37vBJNSs1dGDd8f1jgfdZuA19e_wA3Nc5nQKe9VXRwPHPgwAQ |
|
- type: precision |
|
value: 0.712 |
|
name: Precision Micro |
|
verified: true |
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|
- type: precision |
|
value: 0.7134676028447461 |
|
name: Precision Weighted |
|
verified: true |
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|
- type: recall |
|
value: 0.7119814425203647 |
|
name: Recall Macro |
|
verified: true |
|
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- type: recall |
|
value: 0.712 |
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name: Recall Micro |
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verified: true |
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- type: recall |
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value: 0.712 |
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name: Recall Weighted |
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verified: true |
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- type: f1 |
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value: 0.7119226991285647 |
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name: F1 Macro |
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verified: true |
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verified: true |
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value: 0.7119242267218338 |
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name: F1 Weighted |
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verified: true |
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- type: loss |
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value: 1.0105403661727905 |
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name: loss |
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verified: true |
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- task: |
|
type: natural-language-inference |
|
name: Natural Language Inference |
|
dataset: |
|
name: multi_nli |
|
type: multi_nli |
|
config: default |
|
split: validation_mismatched |
|
metrics: |
|
- type: accuracy |
|
value: 0.902766476810415 |
|
name: Accuracy |
|
verified: true |
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- type: precision |
|
value: 0.9023816542652491 |
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name: Precision Macro |
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verified: true |
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- type: precision |
|
value: 0.902766476810415 |
|
name: Precision Micro |
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verified: true |
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- type: precision |
|
value: 0.9034597464719761 |
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name: Precision Weighted |
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verified: true |
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- type: recall |
|
value: 0.9024304801555488 |
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name: Recall Macro |
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verified: true |
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value: 0.902766476810415 |
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name: Recall Micro |
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verified: true |
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value: 0.902766476810415 |
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name: Recall Weighted |
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verified: true |
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value: 0.9023086094638595 |
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name: F1 Macro |
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verified: true |
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value: 0.902766476810415 |
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name: F1 Micro |
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verified: true |
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value: 0.9030161011457231 |
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name: F1 Weighted |
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verified: true |
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- type: loss |
|
value: 0.3283354640007019 |
|
name: loss |
|
verified: true |
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODdmYzYzNTUzZDNmOWIxM2E0ZmUyOWUzM2Y2NGRmZDNiYjg3ZTMzYTUyNzg3OWEzNzYyN2IyNmExOGRlMWUxYSIsInZlcnNpb24iOjF9.Qv0FzFZPkcBs9aHGf4TEREX4jdkc40NazdMlP2M_-w2wHwyjoAjvhk611RLXHcbicozNelZJLnsOMdEMnPLEDg |
|
- task: |
|
type: natural-language-inference |
|
name: Natural Language Inference |
|
dataset: |
|
name: anli |
|
type: anli |
|
config: plain_text |
|
split: dev_r1 |
|
metrics: |
|
- type: accuracy |
|
value: 0.737 |
|
name: Accuracy |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTQ1ZGVkOTVmNTlhYjhkMjVlNTNhMjNmZWFjZWZjZjcxZmRhMDVlOWI0YTdkOTMwYjVjNWFlOGY4OTc1MmRhNiIsInZlcnNpb24iOjF9.wGLgKA1E46ljbLokdPeip_UCr1gqK8iSSbsJKX2vgKuuhDdUWWiECrUFN-bv_78JWKoKW5T0GF_hb-RVDzA0AQ |
|
- type: precision |
|
value: 0.737681071614645 |
|
name: Precision Macro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmFkMGUwMjNhN2E3NzMxNTc5NDM0MjY1MGU5ODllM2Q2YzA1MDI3OGI1ZmI4YTcxN2E4ZDk5OWY2OGNiN2I0MCIsInZlcnNpb24iOjF9.6G5qhccjheaNfasgRyrkKBTaQPRzuPMZZ0hrLxTNzAydMDgx09FkFP3hni7WLRMWp0IpwzkEeBlxV-mPyQBtBw |
|
- type: precision |
|
value: 0.737 |
|
name: Precision Micro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2QzYjQ4ZDZjOGU5YzI3YmFlMThlYTRkYTUyYWIyNzc4NDkwNzM1OWFiMTgyMzA0NDZmMGI3YTQxODBjM2EwMCIsInZlcnNpb24iOjF9.bvNWyzfct1CLJFx_EuD2GeKieVtyGJy0cwUBP2qJE1ey2i9SVn6n1Dr0AALTGBkxQ6n5-fJ61QFNufpdr2KvCA |
|
- type: precision |
|
value: 0.7376755842752241 |
|
name: Precision Weighted |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2VmYWYzZWQwZmMzMDk0NTdlY2Y3NDkzYWY5ZTdmOGU0ZTUzZWE4YWFhZjVmODhkZmE1Njg4NjA5YjJmYWVhOSIsInZlcnNpb24iOjF9.50FQR2aoBpORLgYa7482ZTrRhT-KfIgv5ltBEHndUBMmqGF9Ru0LHENSGwyD_tO89sGPfiW32TxpbrNWiBdIBA |
|
- type: recall |
|
value: 0.7369675064285843 |
|
name: Recall Macro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTM4OTAyNDYwNjY4Zjc5NDljNjBmNTg2Mzk4YjYxM2MyYTA0MDllYTMyNzEwOGI1ZTEwYWE3ZmU0NDZmZDg2NiIsInZlcnNpb24iOjF9.UvWBxuApNV3vd4hpgwqd6XPHCbkA_bB_Cw24ooquiOf0dstvjP3JvpGoDp5SniOzIOg3i2aYbcvFCLJqEXMZCQ |
|
- type: recall |
|
value: 0.737 |
|
name: Recall Micro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmQ4MjMzNzRmNTI5NjIzNGQ0ZDFmZTA1MDU3OTk0MzYyMGI0NTMzZTZlMTQ1MDc1MzBkMGMzYjcxZjU1NDNjOSIsInZlcnNpb24iOjF9.kpbdXOpDG3CUB-kUEXsgFT3HWWIbu70wwzs2TNf0rhIuRrzdZz3dXXvwqu1BcLJTsOxl8G6NTiYXgnv-ul8lDg |
|
- type: recall |
|
value: 0.737 |
|
name: Recall Weighted |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmU1ZWJkNWE0NjczY2NiZWYyNzYyMzllNzZmZTIxNWRkYTEyZDgxN2E0NTNmM2ExMTc1ZWVjMzBiYjg0ZmM1MiIsInZlcnNpb24iOjF9.S6HHWCWnut_LJqXbEA_Z8ZOTtyq6V51ZeiA0qbwzr0hapDYZOZHrN4prvSLvoNv-GiYDYKatwIsAZxCZc5fmCA |
|
- type: f1 |
|
value: 0.7366853496239583 |
|
name: F1 Macro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzkxYmY2NTcyOTE0ZDdjNGY2ZmE4MzQwMGIxZTA2MDg1NzI5YTQ0MTdkZjdkNzNkMDM2NTk2MTNiNjU4ODMwZCIsInZlcnNpb24iOjF9.ECVaCBqGd0pnQT3xJF7yWrgecIb-5TMiVWpEO0MQGhYy43snkI6Qs-2FOXzvfwIWqG-Q6XIIhGbWZh5TFEGKCA |
|
- type: f1 |
|
value: 0.737 |
|
name: F1 Micro |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDMwMWZiNzQyNWEzNmMzMDJjOTAxYzAxNzc0MTNlYzRkZjllYmNjZmU0OTgzZDFkNWM1ZWI5OTA2NzE5Y2YxOSIsInZlcnNpb24iOjF9.8yZFol_Gcj9n3w9Yk5wx48yql7p3wriDecv-6VSTAB6Q_MWLQAWsCEGRRhgGJ3zvhoRehJZdb35ozk36VOinDQ |
|
- type: f1 |
|
value: 0.7366990292378379 |
|
name: F1 Weighted |
|
verified: true |
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|
- type: loss |
|
value: 0.9349392056465149 |
|
name: loss |
|
verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmI4MTI5MDM1NjBmMzgzMzc2NjM5MzZhOGUyNTgyY2RlZTEyYTIzYzY2ZGJmODcxY2Q5OTVjOWU3OTQ2MzM1NSIsInZlcnNpb24iOjF9.bSOFnYC4Y2y2pW1AR-bgPUHKafR-0OHf8PvexK8eQLsS323Xy9-rYkKUaP09KY6_fk9GqAawv5eqj72B_uyeCA |
|
--- |
|
# DeBERTa-v3-base-mnli-fever-anli |
|
## Model description |
|
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). |
|
The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf). |
|
|
|
For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli. |
|
|
|
|
|
### How to use the model |
|
#### Simple zero-shot classification pipeline |
|
```python |
|
#!pip install transformers[sentencepiece] |
|
from transformers import pipeline |
|
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli") |
|
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU" |
|
candidate_labels = ["politics", "economy", "entertainment", "environment"] |
|
output = classifier(sequence_to_classify, candidate_labels, multi_label=False) |
|
print(output) |
|
``` |
|
#### NLI use-case |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
import torch |
|
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") |
|
|
|
model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForSequenceClassification.from_pretrained(model_name) |
|
|
|
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." |
|
hypothesis = "The movie was good." |
|
|
|
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") |
|
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" |
|
prediction = torch.softmax(output["logits"][0], -1).tolist() |
|
label_names = ["entailment", "neutral", "contradiction"] |
|
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} |
|
print(prediction) |
|
``` |
|
### Training data |
|
DeBERTa-v3-base-mnli-fever-anli was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. |
|
|
|
### Training procedure |
|
DeBERTa-v3-base-mnli-fever-anli was trained using the Hugging Face trainer with the following hyperparameters. |
|
``` |
|
training_args = TrainingArguments( |
|
num_train_epochs=3, # total number of training epochs |
|
learning_rate=2e-05, |
|
per_device_train_batch_size=32, # batch size per device during training |
|
per_device_eval_batch_size=32, # batch size for evaluation |
|
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler |
|
weight_decay=0.06, # strength of weight decay |
|
fp16=True # mixed precision training |
|
) |
|
``` |
|
### Eval results |
|
The model was evaluated using the test sets for MultiNLI and ANLI and the dev set for Fever-NLI. The metric used is accuracy. |
|
|
|
mnli-m | mnli-mm | fever-nli | anli-all | anli-r3 |
|
---------|----------|---------|----------|---------- |
|
0.903 | 0.903 | 0.777 | 0.579 | 0.495 |
|
|
|
## Limitations and bias |
|
Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. |
|
|
|
## Citation |
|
If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. |
|
|
|
### Ideas for cooperation or questions? |
|
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) |
|
|
|
### Debugging and issues |
|
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. |
|
Also make sure to install sentencepiece to avoid tokenizer errors. Run: `pip install transformers[sentencepiece]` or `pip install sentencepiece` |
|
|
|
|
|
## Model Recycling |
|
|
|
[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.65&mnli_lp=nan&20_newsgroup=-0.61&ag_news=-0.01&amazon_reviews_multi=0.46&anli=0.84&boolq=2.12&cb=16.07&cola=-0.76&copa=8.60&dbpedia=-0.40&esnli=-0.29&financial_phrasebank=-1.98&imdb=-0.47&isear=-0.22&mnli=-0.21&mrpc=0.50&multirc=1.91&poem_sentiment=1.73&qnli=0.07&qqp=-0.37&rotten_tomatoes=-0.74&rte=3.94&sst2=-0.45&sst_5bins=0.07&stsb=1.27&trec_coarse=-0.16&trec_fine=0.18&tweet_ev_emoji=-0.93&tweet_ev_emotion=-1.33&tweet_ev_hate=-1.67&tweet_ev_irony=-5.46&tweet_ev_offensive=-0.17&tweet_ev_sentiment=-0.11&wic=-0.21&wnli=-1.20&wsc=4.18&yahoo_answers=-0.70&model_name=MoritzLaurer%2FDeBERTa-v3-base-mnli-fever-anli&base_name=microsoft%2Fdeberta-v3-base) using MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli as a base model yields average score of 79.69 in comparison to 79.04 by microsoft/deberta-v3-base. |
|
|
|
The model is ranked 2nd among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023. |
|
|
|
Results: |
|
|
|
| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |
|
|---------------:|----------:|-----------------------:|-------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|-------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| |
|
| 85.8072 | 90.4333 | 67.32 | 59.625 | 85.107 | 91.0714 | 85.8102 | 67 | 79.0333 | 91.6327 | 82.5 | 94.02 | 71.6428 | 89.5749 | 89.7059 | 64.1708 | 88.4615 | 93.575 | 91.4148 | 89.6811 | 86.2816 | 94.6101 | 57.0588 | 91.5508 | 97.6 | 91.2 | 45.264 | 82.6179 | 54.5455 | 74.3622 | 84.8837 | 71.6949 | 71.0031 | 69.0141 | 68.2692 | 71.3333 | |
|
|
|
|
|
For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/) |
|
|
