Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/xlm-roberta-base-banking77-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/xlm-roberta-base-banking77-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickprock/xlm-roberta-base-banking77-classification") model = AutoModelForSequenceClassification.from_pretrained("nickprock/xlm-roberta-base-banking77-classification") - Notebooks
- Google Colab
- Kaggle
Commit ·
7f9da00
1
Parent(s): b7009e3
Librarian Bot: Add base_model information to model (#3)
Browse files- Librarian Bot: Add base_model information to model (f483765e9d5160f63c29b978a668f35b9675a77e)
Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -6,12 +6,26 @@ datasets:
|
|
| 6 |
- banking77
|
| 7 |
metrics:
|
| 8 |
- accuracy
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
model-index:
|
| 10 |
- name: xlm-roberta-base-banking77-classification
|
| 11 |
results:
|
| 12 |
- task:
|
| 13 |
-
name: Text Classification
|
| 14 |
type: text-classification
|
|
|
|
| 15 |
dataset:
|
| 16 |
name: banking77
|
| 17 |
type: banking77
|
|
@@ -19,9 +33,9 @@ model-index:
|
|
| 19 |
split: train
|
| 20 |
args: default
|
| 21 |
metrics:
|
| 22 |
-
-
|
| 23 |
-
type: accuracy
|
| 24 |
value: 0.9321428571428572
|
|
|
|
| 25 |
- task:
|
| 26 |
type: text-classification
|
| 27 |
name: Text Classification
|
|
@@ -31,63 +45,50 @@ model-index:
|
|
| 31 |
config: default
|
| 32 |
split: test
|
| 33 |
metrics:
|
| 34 |
-
-
|
| 35 |
-
type: accuracy
|
| 36 |
value: 0.9321428571428572
|
|
|
|
| 37 |
verified: true
|
| 38 |
-
-
|
| 39 |
-
type: precision
|
| 40 |
value: 0.9339627666926148
|
|
|
|
| 41 |
verified: true
|
| 42 |
-
-
|
| 43 |
-
type: precision
|
| 44 |
value: 0.9321428571428572
|
|
|
|
| 45 |
verified: true
|
| 46 |
-
-
|
| 47 |
-
type: precision
|
| 48 |
value: 0.9339627666926148
|
|
|
|
| 49 |
verified: true
|
| 50 |
-
-
|
| 51 |
-
type: recall
|
| 52 |
value: 0.9321428571428572
|
|
|
|
| 53 |
verified: true
|
| 54 |
-
-
|
| 55 |
-
type: recall
|
| 56 |
value: 0.9321428571428572
|
|
|
|
| 57 |
verified: true
|
| 58 |
-
-
|
| 59 |
-
type: recall
|
| 60 |
value: 0.9321428571428572
|
|
|
|
| 61 |
verified: true
|
| 62 |
-
-
|
| 63 |
-
type: f1
|
| 64 |
value: 0.9320514513719953
|
|
|
|
| 65 |
verified: true
|
| 66 |
-
-
|
| 67 |
-
type: f1
|
| 68 |
value: 0.9321428571428572
|
|
|
|
| 69 |
verified: true
|
| 70 |
-
-
|
| 71 |
-
type: f1
|
| 72 |
value: 0.9320514513719956
|
|
|
|
| 73 |
verified: true
|
| 74 |
-
-
|
| 75 |
-
type: loss
|
| 76 |
value: 0.30337899923324585
|
|
|
|
| 77 |
verified: true
|
| 78 |
-
widget:
|
| 79 |
-
- text: 'Can I track the card you sent to me? '
|
| 80 |
-
example_title: Card Arrival Example - English
|
| 81 |
-
- text: 'Posso tracciare la carta che mi avete spedito? '
|
| 82 |
-
example_title: Card Arrival Example - Italian
|
| 83 |
-
- text: Can you explain your exchange rate policy to me?
|
| 84 |
-
example_title: Exchange Rate Example - English
|
| 85 |
-
- text: Potete spiegarmi la vostra politica dei tassi di cambio?
|
| 86 |
-
example_title: Exchange Rate Example - Italian
|
| 87 |
-
- text: I can't pay by my credit card
|
| 88 |
-
example_title: Card Not Working Example - English
|
| 89 |
-
- text: Non riesco a pagare con la mia carta di credito
|
| 90 |
-
example_title: Card Not Working Example - Italian
|
| 91 |
---
|
| 92 |
|
| 93 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 6 |
- banking77
|
| 7 |
metrics:
|
| 8 |
- accuracy
|
| 9 |
+
widget:
|
| 10 |
+
- text: 'Can I track the card you sent to me? '
|
| 11 |
+
example_title: Card Arrival Example - English
|
| 12 |
+
- text: 'Posso tracciare la carta che mi avete spedito? '
|
| 13 |
+
example_title: Card Arrival Example - Italian
|
| 14 |
+
- text: Can you explain your exchange rate policy to me?
|
| 15 |
+
example_title: Exchange Rate Example - English
|
| 16 |
+
- text: Potete spiegarmi la vostra politica dei tassi di cambio?
|
| 17 |
+
example_title: Exchange Rate Example - Italian
|
| 18 |
+
- text: I can't pay by my credit card
|
| 19 |
+
example_title: Card Not Working Example - English
|
| 20 |
+
- text: Non riesco a pagare con la mia carta di credito
|
| 21 |
+
example_title: Card Not Working Example - Italian
|
| 22 |
+
base_model: xlm-roberta-base
|
| 23 |
model-index:
|
| 24 |
- name: xlm-roberta-base-banking77-classification
|
| 25 |
results:
|
| 26 |
- task:
|
|
|
|
| 27 |
type: text-classification
|
| 28 |
+
name: Text Classification
|
| 29 |
dataset:
|
| 30 |
name: banking77
|
| 31 |
type: banking77
|
|
|
|
| 33 |
split: train
|
| 34 |
args: default
|
| 35 |
metrics:
|
| 36 |
+
- type: accuracy
|
|
|
|
| 37 |
value: 0.9321428571428572
|
| 38 |
+
name: Accuracy
|
| 39 |
- task:
|
| 40 |
type: text-classification
|
| 41 |
name: Text Classification
|
|
|
|
| 45 |
config: default
|
| 46 |
split: test
|
| 47 |
metrics:
|
| 48 |
+
- type: accuracy
|
|
|
|
| 49 |
value: 0.9321428571428572
|
| 50 |
+
name: Accuracy
|
| 51 |
verified: true
|
| 52 |
+
- type: precision
|
|
|
|
| 53 |
value: 0.9339627666926148
|
| 54 |
+
name: Precision Macro
|
| 55 |
verified: true
|
| 56 |
+
- type: precision
|
|
|
|
| 57 |
value: 0.9321428571428572
|
| 58 |
+
name: Precision Micro
|
| 59 |
verified: true
|
| 60 |
+
- type: precision
|
|
|
|
| 61 |
value: 0.9339627666926148
|
| 62 |
+
name: Precision Weighted
|
| 63 |
verified: true
|
| 64 |
+
- type: recall
|
|
|
|
| 65 |
value: 0.9321428571428572
|
| 66 |
+
name: Recall Macro
|
| 67 |
verified: true
|
| 68 |
+
- type: recall
|
|
|
|
| 69 |
value: 0.9321428571428572
|
| 70 |
+
name: Recall Micro
|
| 71 |
verified: true
|
| 72 |
+
- type: recall
|
|
|
|
| 73 |
value: 0.9321428571428572
|
| 74 |
+
name: Recall Weighted
|
| 75 |
verified: true
|
| 76 |
+
- type: f1
|
|
|
|
| 77 |
value: 0.9320514513719953
|
| 78 |
+
name: F1 Macro
|
| 79 |
verified: true
|
| 80 |
+
- type: f1
|
|
|
|
| 81 |
value: 0.9321428571428572
|
| 82 |
+
name: F1 Micro
|
| 83 |
verified: true
|
| 84 |
+
- type: f1
|
|
|
|
| 85 |
value: 0.9320514513719956
|
| 86 |
+
name: F1 Weighted
|
| 87 |
verified: true
|
| 88 |
+
- type: loss
|
|
|
|
| 89 |
value: 0.30337899923324585
|
| 90 |
+
name: loss
|
| 91 |
verified: true
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 92 |
---
|
| 93 |
|
| 94 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|