Update README.md

README.md

@@ -1,40 +1,96 @@
 ---
 license: mit
+language:
+- am
+- ar
+- hy
+- eu
+- bn
+- bs
+- bg
+- my
+- hr
+- ca
+- cs
+- da
+- nl
+- en
+- et
+- fi
+- fr
+- ka
+- de
+- el
+- gu
+- ht
+- iw
+- hi
+- hu
+- is
+- in
+- it
+- ja
+- kn
+- km
+- ko
+- lo
+- lv
+- lt
+- ml
+- mr
+- ne
+- no
+- or
+- pa
+- ps
+- fa
+- pl
+- pt
+- ro
+- ru
+- sr
+- zh
+- sd
+- si
+- sk
+- sl
+- es
+- sv
+- tl
+- ta
+- te
+- th
+- tr
+- uk
+- ur
+- ug
+- vi
+- cy
 tags:
 - generated_from_trainer
 model-index:
-- name: verdict-classifier
-  results: []
+- name: verdict-classifier-en
+  results:
+  - task:
+      type: text-classification
+      name: Verdict Classification
+widget:
+- "One might think that this is true, but it's taken out of context."
 ---
 
-<!-- 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. -->
+# English Verdict Classifier
 
-# verdict-classifier
-
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
-It achieves the following results on the evaluation set:
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 1,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into 66 languages with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/).
+It achieves the following results on the evaluation set, being 1,000 such verdicts, but here including duplicates to represent the true distribution:
 - Loss: 0.1856
 - F1 Macro: 0.8148
 - F1 Misinformation: 0.9764
 - F1 Factual: 0.9375
 - F1 Other: 0.5306
-- Prec Macro: 0.8117
-- Prec Misinformation: 0.9775
-- Prec Factual: 0.9375
-- Prec Other: 0.52
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+- Precision Macro: 0.8117
+- Precision Misinformation: 0.9775
+- Precision Factual: 0.9375
+- Precision Other: 0.52
 
 ## Training procedure
 
@@ -54,7 +110,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Prec Macro | Prec Misinformation | Prec Factual | Prec Other |
+| Training Loss | Epoch | Step  | Validation Loss | F1 Macro | F1 Misinformation | F1 Factual | F1 Other | Precision Macro | Precision Misinformation | Precision Factual | Precision Other |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------------:|:----------:|:--------:|:----------:|:-------------------:|:------------:|:----------:|
 | 0.8707        | 1.0   | 3758  | 0.2414          | 0.7832   | 0.9639            | 0.7857     | 0.6      | 0.7950     | 0.9683              | 0.9167       | 0.5        |
 | 0.3918        | 2.0   | 7516  | 0.1856          | 0.8148   | 0.9764            | 0.9375     | 0.5306   | 0.8117     | 0.9775              | 0.9375       | 0.52       |